2020 FIP Virtual Symposium Booklet

2020 FIP Symposium

Photonics in the Era of Data Science: From Smart Sensing to AI

March 9-10, 2020
Fitzpatrick Institute for Photonics (FIP)
Pratt School of Engineering, Duke University

FIP 2020 Annual Symposium

Welcome

2020 Fitzpatrick Institute for Photonics (FIP) Annual Meeting
Symposium of Science and Photonics Technology
“Photonics in the Era of Data Science: from Smart Sensing to
Artificial Intelligence (AI)”

Symposium Chair - Tuan Vo-Dinh, Director, Fitzpatrick Institute for Photonics

Symposium Program Committee - Steven Cummer, Charles Gersbach, Nan Jokerst,
Jungsang Kim, Warren Warren, Weitao Yang, Fan Yuan

Symposium Program Coordinator - August Burns, Department Business Manager,
Fitzpatrick Institute for Photonics

Symposium Special Session Committee - Officers of Duke Optical Student Chapter:
Kristen Hagan, President; Evan Jelly, Vice President; and Latifah Maasarani, Outreach
Chair

Monday, March 9, 2020
Fitzpatrick Building, Duke University
8:30am - 9:00am Registration
9:00am - 12:00pm Meeting
12:00pm - 1:30pm Lunch Break
1:30pm - 4:50pm Meeting
4:50pm - 6:00pm FIP Poster Session & Reception

Tuesday, March 10, 2020
Fitzpatrick Building, Duke University
10:00am - 12:00pm Special Session - Photonics in Research & Education
Co-sponsored by: Duke Optical Student Chapter
12:00pm - 1:30pm Lunch Break
1:30pm - 4:30pm Meeting

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Table of Contents

2020 Fitzpatrick Institute for Photonics (FIP) Annual Meeting
Symposium of Science and Photonics Technology
“Photonics in the Era of Data Science: from Smart Sensing to
Artificial Intelligence (AI)”

Welcome 2

Program Agenda 4

Fitzpatrick Institute for Photonics Celebrating 20 years - New Logo 10

Speaker Abstracts & Biographical Sketches 11

Welcome & Opening Remarks Speakers 11

Keynote Speaker 13

Plenary Speakers 14

Invited Guest Speakers 18

Duke Speakers 21

Session Chairs & Panelists 29

Fitzpatrick Institute for Photonics (FIP) Poster Session Abstracts 37

Fitzpatrick Institute for Photonics (FIP) Departments 50

Fitzpatrick Institute for Photonics (FIP) Faculty List 51

2019 FIP Awardees - Fitzpatrick & Chambers Fellowships 55

Celebrating the History of the Laser - 60 Years of Laser Innovation 56

Thanks to our Corporate Partners!



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FIP 2020 Annual Symposium

Symposium on Photonics Science and Technology

2020 Fitzpatrick Institute for Photonics (FIP) Annual Meeting

March 9-10, 2020, Duke University

In Celebration of the 60th Anniversary
of the Invention of the Laser

ADVANCE PROGRAM AGENDA

Monday, March 9, 2020 (Fitzpatrick Center) – Morning Session

8:30-9:00 am Registration

9:00-9:05 Introduction
Tuan Vo-Dinh, Director of the Fitzpatrick Institute for Photonics, R.
Eugene and Susie E. Goodson Professor of Biomedical Engineering
and Professor of Chemistry, Duke University

9:05-9:15 Opening Welcome Address
Lawrence Carin, Vice President for Research, James L. Meriam
Distinguished Professor of Electrical and Computer Engineering,
Professor of Computer Science and Statistical Science,
Duke University

9:15-9:25 Ravi Bellamkonda, Vinik Dean, Pratt School of Engineering,
Professor of Biomedical Engineering, Duke University

9:25-10:05 Symposium Keynote Lecture
Donna Strickland, 2018 Nobel Laureate in Physics
Professor in the Department of Physics and Astronomy
University of Waterloo, Canada

10:05-10:15 FIP Award Presentation – 2019 Pioneer in Photonics Award

10:15-10:35 COFFEE BREAK

10:35-11:10 Plenary Lecture

William T. Freeman, Thomas and Gerd Perkins Professor of

Electrical Engineering, Computer Science and Artificial Intelligence

Laboratory, Department of Electrical Engineering and Computer

Science, Massachusetts Institute of Technology and

Senior Research Scientist at Google

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11:10-12:00 Session 1: Special Topic – Photonics in the Era of Data Science:

from Smart Sensing to Artificial Intelligence (AI)

Chair: Weitao Yang, Philip Handler Distinguished Professor of

Chemistry, Duke University

11:10- 11:40 Guillermo Sapiro, James B. Duke Distinguished
Professor of Electrical and Computer Engineering,

Professor of Computer Science and Mathematics
Duke University

11:40- 12:00 Lawrence Carin, Vice President for Research,
James L. Meriam Distinguished Professor of
Electrical and Computer Engineering, Professor of
Computer Science and Statistical Science,

Duke University

12:00-1:30pm LUNCH BREAK (Lunch provided)
Poster Display (No presenters at this time)
Posters are on display in the Atrium area of the Fitzpatrick Center

_________________________________________________________

Monday, March 9, 2020 (Fitzpatrick Center) - Afternoon Session

1:30-2:05 pm Plenary Lecture
Todd Zickler,William and Ami Kuan Danoff Professor of Electrical

Engineering and Computer Science, School of Engineering & Applied
Sciences, Harvard University

2:05-2:40pm Plenary Lecture
Frederic Zenhausern, Endowed Chair Professor of Basic Medical

Sciences, Professor of Radiation Oncology and Biomedical Engineering,
Director, Center for Applied Nanobioscience and Medicine, College of
Medicine Phoenix, University of Arizona,
Professor, School of Pharmacy, University of Geneva (Switzerland)

2:40-3:10pm Session 2: PANEL SESSION - Digital Health and Immersive Medicine:
Challenges and Prospects of the Medicine of The Future

Co-chair: Tuan Vo-Dinh, Director of the Fitzpatrick Institute for
Photonics, R. Eugene and Susie E. Goodson Professor of Biomedical
Engineering, Duke University


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FIP 2020 Annual Symposium

Co-Chair & Panelist: Frederic Zenhausern, Endowed Chair Professor of
Basic Medical Sciences, Professor of Radiation Oncology and Biomedical
Engineering, Director, Center for Applied Nanobioscience and Medicine,
College of Medicine Phoenix, University of Arizona, Professor, School of
Pharmacy, University of Geneva (Switzerland)

Panelist: William T. Freeman, Thomas and Gerd Perkins Professor of
3:10-3:30 Electrical Engineering, Computer Science and Artificial Intelligence
3:30-4:50 Laboratory, Department of Electrical Engineering and Computer
Science, Massachusetts Institute of Technology and
Senior Research Scientist at Google

6 Panelist: Geoffrey S. Ginbsurg, Director, Center for Applied Genomics
& Precision Medicine, Duke University School of Medicine; Director,
MEDx, School of Medicine-Pratt School of Engineering, Professor
Medicine and Pathology, Duke University Medical Center; Professor of
Biomedical Engineering and Professor in the School of Nursing, Duke
University

Panelist: Sanjay Padhi, Head of Amazon Web Services (AWS) Research,
US Education

COFFEE BREAK

Session 3: Special Topic – Photonics in the Era of Data Science: from
Smart Sensing to AI
Chair: Adam Wax, Professor of Biomedical Engineering and Physics,
Duke University

3:30-4:00 Invited Lecture
Sanjay Padhi, Head of Amazon Web Services (AWS)
Research, US Education

4:00-4:30 Invited Lecture
Ron Alterovitz, Professor, Department of Computer
Science, University of North Carolina at Chapel Hill

4:30-4:50 Sina Farsiu, Paul Ruffin Scarborough Associate Professor
of Biomedical Engineering, Associate Professor of

Ophthalmology, Computer Science, Electrical and
Computer Engineering
Duke University
_________________________________________________________

4:50-6:00 POSTER SESSION & RECEPTION
Presenters will be at posters from 4:50-5:30pm
Posters are exhibited in the Atrium area of the Fitzpatrick Center

FIP SYMPOSIUM COCKTAIL RECEPTION

in the Atrium area of the Fitzpatrick Center
(Heavy hors d’oeuvres will be served)

_____________________________________________________________________

Tuesday, March 10 (Fitzpatrick Center) – Special Morning Session & Panel

10:00- 12:00pm Session 4: Photonics in Research and Education

(Organized by the Duke Optical Student Chapter-DOSC)

Program Committee:

Kristen Hagan, DOSC President, Department of Biomedical

Engineering, Duke University

Evan Jelly, DOSC Vice President, Department of Biomedical

Engineering, Duke University

Latifah Maasarini, DOSC Outreach Chair, Department of

Biomedical Engineering, Duke University







10:00-10:45am DOSC presents “Lightning Talks” of student research

Moderator: Kristen Hagan, Department of

Biomedical Engineering, Duke University

10:45-11:00am Transition break to setup Panel

11:00-12:00pm Panel Session - “Keys to a Successful Career in Science &

Engineering

Moderator: Kristen Hagan, Department of

Biomedical Engineering, Duke University


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FIP 2020 Annual Symposium

Panel Members:
Audrey Bowden, Dorothy J. Wingfield Phillips Chancellor
Faculty Fellow, Associate Professor of Biomedical Engineering
and Electrical Engineering, Vanderbilt University

Brian Cullum, Professor, Departments of Chemistry
and Biochemistry, University of Maryland Baltimore County

Jessica DeGroote Nelson, Director of Technology and Strategy,
Optimax Systems, Inc.

Donna Strickland, 2018 Nobel Laureate in Physics, Professor

in the Department of Physics and Astronomy, University of

Waterloo, Canada

_________________________________________________________________

12:00-1:30pm LUNCH BREAK (Lunch provided)

Poster Display (No presenters at this time)

Posters are on display in the Atrium area of Fitzpatrick Center



________________________________________________________________

Tuesday, March 10 (Fitzpatrick Center) - Afternoon Session

1:30-2:40pm Session 5: Special Topic – Photonics in the Era of Data Science: from

Smart Sensing to AI

Chair: Xiaobai Sun, Professor of Computer Science, Duke University

1:30-2:00 Invited Lecture

Benjamin Watson, Associate Professor

Department of Computer Science

North Carolina State University

2:00-2:20 Roarke Horstmeyer, Assistant Professor
Department of Biomedical Engineering and
Department of Electrical and Computer Engineering
Duke University

2:20-2:40 Alberto Bartesaghi, Associate Professor
Departments of Computer Science, Biochemistry and
Electrical and Computer Engineering, Duke University

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2:40-3:00pm COFFEE BREAK

3:00-4:20pm Session 6: Advanced Photonic Technologies and Systems II

Chair: Shyni Varhese, Professor of Biomedical Engineering,

Mechanical Engineering & Materials Science, Orthopaedic Surgery,

Duke University

3:00-3:10 Poster Award Winners Announced

3:10-3:40 Invited Lecture

Brian Cullum, Professor, Departments of Chemistry

and Biochemistry

University of Maryland Baltimore County



3:40-4:00 Pei Zhong, Professor, Departments of Mechanical

Engineering and Biomedical Engineering

Duke University



4:00-4:20 Olivier Delaire, Associate Professor, Department of

Mechanical Engineering and Materials Science

Duke University

4:20-4:30 Closing Remarks - FIP Director

_____________________________________________________________________

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FIP 2020 Annual Symposium

The Fitzpatrick Institute for Photonics (FIP)
Celebrating 20 Years and Unveiling Our New Logo

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Welcome and Opening Remarks

Tuan Vo-Dinh, PhD

Director of the Fitzpatrick Institute for Photonics,
R. Eugene and Susie E. Goodson Professor of Biomedical Engi-
neering and Professor of Chemistry, Duke University

Dr. Vo-Dinh is R. Eugene and Susie E. Goodson Distinguished Professor of
Biomedical Engineering, Professor of Chemistry, and Director of the Fitzpatrick
Institute for Photonics at Duke University. His research interests involve the
development of advanced technologies for the protection of the environment and the
improvement of human health. His research activities are focused on nanophotonics,
biophotonics, nano-biosensors, biochips, molecular spectroscopy, medical diagnostics
and therapy, immunotherapeutics, bioenergy research, personalized medicine,
and global health. Dr. Vo-Dinh has received seven R&D 100 Awards for Most
Technologically Significant Advance in Research and Development for his pioneering
research and inventions of innovative technologies. He has received the Gold
Medal Award, Society for Applied Spectroscopy (1988); the Languedoc-Roussillon
Award (France) (1989); the Scientist of the Year Award, ORNL (1992); the Thomas
Jefferson Award, Martin Marietta Corporation (1992); two Awards for Excellence in
Technology Transfer, Federal Laboratory Consortium (1995, 1986); the Inventor of
the Year Award, Tennessee Inventors Association (1996); and the Lockheed Martin
Technology Commercialization Award (1998), The Distinguished Inventors Award,
UT-Battelle (2003), and the Distinguished Scientist of the Year Award, ORNL (2003).
In 1997, Dr. Vo-Dinh was presented the prestigious Exceptional Services Award
for distinguished contribution to a Healthy Citizenry from the U.S. Department
of Energy. In 2011 Dr. Vo-Dinh received the Award for Spectrochemical Analysis
from the American Chemical Society (ACS) Division of Analytical Chemistry.

Lawrence Carin, PhD

Vice President for Research, Duke University
James L. Meriam Distinguished Professor of Electrical and
Computer Engineering, Professor of Computer Science and
Statistical Science, Duke University

Vice President for Research Lawrence Carin oversees Duke’s compliance with
regulations on government-funded research. The Office of Research includes
the offices of campus Research Development, Export Controls, Human Subjects
Protection, Licensing and Ventures, Postdoctoral Services, Research Initiatives and
Research Support. Carin joined Duke’s faculty in 1995 as an associate professor of
electrical engineering; he subsequently became the William H. Younger Distinguished
Professor and chair of the Department of Electrical Engineering. Since July 1, 2018,
Carin has held the James L. Meriam Distinguished Professorship. His early research
was in the area of electromagnetics and sensing, but over the last fifteen years, he
has moved to applied statistics and machine learning. Carin has become one of
the leaders in Machine Learning and one of the most widely published machine
learning researchers in the world, co-authoring nearly 400 academic papers,
affecting fields as diverse as bomb detection, neuroscience, and voting behavior.

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FIP 2020 Annual Symposium

Ravi Bellamkonda, PhD

Vinik Dean, Pratt School of Engineering
Professor of Biomedical Engineering, Duke University

Ravi V. Bellamkonda is the Vinik Dean of the Pratt School of Engineering at Duke
University. Prior to becoming dean, Bellamkonda served as the Wallace H. Coulter
Professor and chair of the Department of Biomedical Engineering at Georgia Institute of
Technology and Emory University. He is committed to fostering transformative research
and pedagogical innovation as well as programs that create an entrepreneurial mindset
amongst faculty and students. A trained bioengineer and neuroscientist, Bellamkonda
holds an undergraduate degree in biomedical engineering. His graduate training at
Brown University was in biomaterials and medical science (with Patrick Aebischer),
and his post-doctoral training at Massachusetts Institute of Technology focused on the
molecular mechanisms of axon guidance and neural development (with Jerry Schneider
and Sonal Jhaveri). His current research explores the interplay of biomaterials and the
nervous system for neural interfaces, nerve repair and brain tumor therapy. From 2014
to 2016, Bellamkonda served as president of the American Institute for Biological
and Medical Engineering (AIMBE), the leading policy and advocacy organization for
biomedical engineers with representation from industry, academia and government.
Bellamkonda’s numerous awards include the Clemson Award for Applied Research
from the Society for Biomaterials, EUREKA award from National Cancer Institute
(National Institutes of Health), CAREER award from the National Science Foundation
and Best Professor Award from the Georgia Tech Biomedical Engineering student body.

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Speaker Bios

Keynote Speaker

Donna Strickland, PhD

Nobel Laureate in Physics (2018)
Professor in the Department of Physics and Astronomy
University of Waterloo, Canada

“Generating High-Intensity, Ultrashort Optical Pulses”

With the invention of lasers, the the way toward the most intense laser
intensity of a light wave was pulses ever created. The research has
increased by orders of magnitude several applications today in industry
over what had been achieved and medicine — including the cutting
with a light bulb or sunlight. This of a patient’s cornea in laser eye
much higher intensity led to new surgery, and the machining of small
phenomena being observed, such glass parts for use in cell phones.
as violet light coming out when red Strickland was a research associate at
light went into the material. After the National Research Council Canada,
Gérard Mourou and I developed a physicist at Lawrence Livermore
chirped pulse amplification, also National Laboratory and a member of
known as CPA, the intensity technical staff at Princeton University.
again increased by more than a
factor of 1,000 and it once again In 1997, she joined the University of
made new types of interactions
possible between light and matter. Waterloo, where her ultrafast laser group
We developed a laser that could develops high-intensity laser systems
deliver short pulses of light that for nonlinear optics investigations.
knocked the electrons off their Strickland was named a Companion of
atoms. This new understanding of the Order of Canada. She is a recipient of
laser-matter interactions, led to the a Sloan Research Fellowship, a Premier’s
development of new machining Research Excellence Award and a
techniques that are used in laser Cottrell Scholar Award. She received
eye surgery or micromachining the Rochester Distinguished Scholar
of glass used in cell phones. Award and the Eastman Medal from
the University of Rochester. Strickland
Donna Strickland is a professor in the served as the president of the Optical
Department of Physics and Astronomy Society (OSA) in 2013 and is a fellow of
at the University of Waterloo and is OSA, the Royal Society of Canada, and
one of the recipients of the Nobel Prize SPIE (International Society for Optics
in Physics 2018 for developing chirped and Photonics). She is an honorary
pulse amplification with Gérard Mourou, fellow of the Canadian Academy of
her PhD supervisor at the time. They Engineering as well as the Institute of
published this Nobel-winning research Physics. She received the Golden Plate
in 1985 when Strickland was a PhD Award from the Academy of Achievement
student at the University of Rochester and holds numerous honorary
in New York state. Together they paved doctorates. Strickland earned a PhD in
optics from the University of Rochester

and a B.Eng. from McMaster University.

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FIP 2020 Annual Symposium

Plenary Speakers

William T. Freeman, PhD

Thomas and Gerd Perkins Professor of Electrical & Computer
Engineering, Computer Science and Artificial Intelligence Lab
Massachussets Institute of Technology and
Senior Research Scientist, Google

“Feathers, Wings, and the Future of Computer Vision”

Convolutional neural networks generic viewpoint assumption, color
(CNN’s) imitate just the very constancy, computer vision for computer
first steps of the human visual games, motion magnification, and
system, yet their adoption has led to belief propagation in networks with
a revolution in computer vision. loops. He received outstanding paper
It is reasonable to expect that awards at computer vision or machine
emulating other aspects of learning conferences in 1997, 2006,
human vision will lead to further 2009, 2012 and 2019, and test-of-time
progress. However, some aspects of awards for papers from 1990, 1995 and
human vision may be necessary for 2005. He is a Fellow of IEEE, ACM,
general vision systems, while and AAAI. In 2019, he received the
others may not.  In the metaphor of PAMI Distinguished Researcher Award.
flight, deciding which components
are essential is the problem of He is active in the program or
distinguishing feathers (present organizing committees of computer
in birds but not needed for vision, graphics, and machine learning
artificial flight) from wings conferences. He was the program co-
(needed). With the audience, we will chair for ICCV 2005, and for CVPR
discuss which aspects of human
vision are feathers, and which are 2013. He holds over 30 patents.
wings.

William T. Freeman is the Thomas and
Gerd Perkins Professor of Electrical
Engineering and Computer Science
(EECS) at MIT, and a member of
the Computer Science and Artificial
Intelligence Laboratory (CSAIL) there.
He was the Associate Department
Head of EECS from 2011 - 2014.

His current research interests include

mid-level vision and computational

photography. Previous research

topics include steerable filters and

pyramids, orientation histograms, the

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Speaker Bios

Henry Fuchs, PhD

Federico Gil Distinguished Professor of Computer Science

University of North Carolina at Chapel Hill

“Next Generation Augmented Reality Prescription Eyeglasses:
autofocus, telepresence, and situationally aware personal

assistants”

Just as today’s mobile phones are much Siri and Alexa. These assistants will be

more than simply telephones, tomorrow’s much more useful than they are today

Augmented Reality glasses will be much because they will be “situationally

more than just augmented reality displays. aware”, aware of the user’s pose,

These glasses will look very much like activity, and their 3D surroundings,

today’s prescription eyeglasses, but will and they will be visible to the user,

contain a great deal of technology. They properly integrated into user’s physical

with have focus-adjusting lenses that environment. We will show several early

will provide automatic focus capability experimental steps toward the realization

CANCELLEDfor the user’s everyday needs even whenof some of these capabilities.

the Augmented Reality features are Henry Fuchs is the Federico Gil
turned off. The internal display screens Distinguished Professor of Computer
will support proper eye accommodation Science and Adjunct Professor of
in that they will present computer- Biomedical Engineering at UNC Chapel
generated imagery for augmentation Hill. He has been active in computer
that automatically adjust in perceived graphics since the 1970s -- with
distance so that virtual objects will be rendering algorithms such as BSP Trees,
shown optically at the proper distance which have been widely adopted in
from the user, to match the distance popular video games; parallel graphics
of nearby real objects. These glasses
will also contain more cameras than hardware (Pixel-Planes) that introduced
today’s mobile phones. Some of the techniques now in today’s ubiquitous
cameras will be pointed outward for Graphics Processing Units (GPUs);
tracking the user in their surroundings, virtual reality; and medical applications.
probably using 6DOF SLAM. These He is a member of the National Academy
same cameras will also continually of Engineering, a fellow of the American
construct, and update, a detailed 3D Academy of Arts and Sciences, recipient
reconstruction of the surroundings for of the ACM SIGGRAPH Steven Anson
real-time telepresence and future re- Coons Award (considered the highest
experiencing of the surroundings – as, award in computer graphics) and an
for example, immersive vacation photos. honorary doctorate from TU Wien
Other cameras on these eyeglasses will (Vienna University of Technology).
be pointed downward and inward to

reconstruct the user’s body for mobile

telepresence and for health and activity

monitoring. Because these glasses will

also contain the capabilities of mobile

phones, they will also include personal

assistants, much like enhancements of

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FIP 2020 Annual Symposium

Frederic Zenhausern, PhD

Co-Chair and Endowed Chair Professor, Basic Medical Sciences
Professor of Radiation Oncology and Biomedical Engineering,
Director, Center for Applied Nanobioscience and Medicine, College
of Medicine Phoenix, University of Arizona; Professor, School of
Pharmacy, University of Geneva (Switzerland)

“Light on Chips:An “Optical” Perspective on Organ-On-Chip and
Microsystems in Health Sciences”

As alternate to animal testing, 3D the Therapeutic Development Program
microfluidic biomimetic devices aim to at the University of Arizona’s Cancer
emulate the biology of human tissues, Center, member of BIO5 and Professor
organs and circulation in vitro with the at the Department of Biomedical
great promise to enable a paradigm shift Engineering, College of Engineering.
in health sciences and clinical testing. Prior to joining the University of Arizona,
In order to measure complex biological Dr. Zenhausern was founder director of
processes in these micro-physiological the Center for Applied Nanobioscience at
systems, numerous light-based detection the Arizona State University’s Biodesign
have been miniaturized and integrated to Institute, and co- founder and R&D
study specific functions of human tissues. director of the first phase of the Center
A variety of different approaches in the for Flexible Display and then CTO of
broad field of science and technology have MacroTechnology Works, a university
been designed as bioinspired photonic think tank. Zenhausern was also tenured
devices that will advance biomedical Professor with both the Electrical
engineering, bioanalytical metrology Department and the School of Materials
and disease studies. In this lecture, we at the Ira A. Fulton School of Engineering.
will discuss some of these novel methods Dr. Zenhausern is also Professor at
which have been used to manufacture the Translational Genomics Research
microsystems, examine the behavior of Institute (TGen) and lead innovative
live cells, tissue dynamics or monitoring clinical research initiatives as director
environmental exposure or molecular of the Personalized Medicine Research
interactions with microbiome ecosystems Laboratory at Honor Health Research
to gain knowledge in health sciences. Institute. Over a decade, Zenhausern
held corporate research positions at IBM
Dr. Frederic Zenhausern is Endowed Research Division and Motorola Labs.
Chair Professor of Basic Medical He was also a senior research scientist
Sciences, Professor of Radiation at Firmenich Inc., Princeton (NJ). He is
Oncology at the College of Medicine, also an “Professeur Titulaire (Adjunct)”
Phoenix, and founder director of the with active research collaboration
Center for Applied Nanobioscience and at the University of Geneva’s
Medicine (ANBM) at the University of School of Pharmaceutical Sciences.
Arizona (UofA). Since 2019, he is the
co-chair of the Department of Basic
Medical Sciences. He is also Member of

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Speaker Bios

Todd Zickler, PhD

William and Ami Kuan Danoff Professor of Electrical Engineering
and Computer Science, School of Engineering and Applied
Sciences, Harvard University

“Toward Computer Vision on Microwatt Platforms”

Small platforms such as insect- Todd Zickler received B.Eng.
scale robots and microwatt sensor and Ph.D. degrees in electrical
nodes may not have enough engineering from McGill
power to accomplish visual tasks University in 1996 and Yale
like detection and navigation University in 2004. He joined
using traditional cameras and Harvard University in 2004
conventional computer vision and was appointed professor
techniques. For these sorts of of electrical engineering and
platforms, it might be useful to computer science in 2011.
design different algorithms and His research group models
optical configurations that are interactions between light,
more specialized and efficient materials and optics, and they
for the task and environment at develop optical and computational
hand. This idea is loosely inspired systems to extract useful
by biology, where evolution has information from visual data. He
produced substantial optical is motivated by applications in
and algorithmic diversity for robotics and augmented reality,
visual sensing, especially among and he works at the intersection
invertebrates. Inventing a of computer vision, computer
specialized visual sensor requires graphics, signal processing,
jointly designing optics, vision applied optics, biological
algorithms, and computational vision, and human perception.
architectures that can accomplish
the prescribed tasks while
being as efficient and small as
possible. This talk presents a
few examples, including a class
of lightweight, wide-view object
detectors using refractive optics,
and a class of lightweight range
imagers using metalens optics.



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FIP 2020 Annual Symposium

Invited Speakers

Ron Alterovitz, PhD

Professor, Department of Computer Science

University of North Carolina at Chapel Hill

“Towards Autonomous Robots for Healthcare”

Advances in robotics have the Ron Alterovitz is a Professor in the
Department of Computer Science at
potential to improve healthcare, the University of North Carolina at
Chapel Hill. He leads the Computational
from enabling surgical procedures Robotics Research Group which
develops novel algorithms for robots
that are beyond current clinical to learn and plan their motions, with
a focus on enabling robots to perform
capabilities to autonomously new, less invasive medical procedures
and to assist people in their homes.
assisting people with daily tasks Prior to joining UNC-Chapel Hill in
2009, Dr. Alterovitz earned his B.S. with
in their homes. In this talk, we will Honors from Caltech, completed his
Ph.D. at the University of California,
discuss new algorithms to enable Berkeley, and conducted postdoctoral
research at the UCSF Comprehensive
medical and assistive robots to Cancer Center and the Robotics & AI
group at LAAS-CNRS (National Center
operate semi-autonomously by for Scientific Research) in Toulouse,
France. Dr. Alterovitz has co-authored
learning and planning their motions. a book on Motion Planning in Medicine,
was co-awarded a patent for a medical
These robots rely on data from device, and has received multiple best
paper awards at robotics and computer-
a variety of sensing modalities, assisted medicine conferences. He is the
recipient of an NIH Ruth L. Kirschstein
including optical images, CT scans, National Research Service Award, two
UNC Computer Science Department
and electromagnetic tracking. Excellence in Teaching Awards, an
NSF Early Career Development
First, I will discuss two emerging (CAREER) Award, and the Presidential
Early Career Award for Scientists
medical devices, steerable needles and Engineers (PECASE), the highest
honor bestowed by the United States
and tentacle-like robots, designed Government on science and engineering
professionals in the early stages of
for image-guided neurosurgery their independent research careers.

and pulmonary procedures.

These devices can automatically

maneuver around anatomical

obstacles to perform procedures

at sites inaccessible to traditional

straight instruments. Next, I

will present new algorithms for

robotic assistance in the home

using demonstration-guided

motion planning, an approach

in which the robot first learns

an assistive task from human-

conducted demonstrations and then

autonomously plans motions to

accomplish the learned task in new,

dynamic environments.

18

Speaker Bios

Brian Cullum, PhD

Professor, Department of Chemistry and Biochemistry

University of Maryland Baltimore County

“THORS (THermally-induced Optical Reflection of Sound):

Demonstration, Characterization and Potential Application”

This talk will describe recent waves. The potential of THORS
proof-of principle demonstration to applications such as acoustic
and characterization of the ability silencing, directed acoustics
to optically reflect and suppress and enhanced photoacoustic
acoustic waves, thereby allowing imaging will be also be discussed.
for transient, “non-invasive”
manipulation of acoustics for many
different potential applications. Dr. Brian M. Cullum is Professor
Using THORS (Thermally- of Chemistry and Biochemistry
induced Optical Reflection of at the University of Maryland
Sound), optical radiation is Baltimore County (UMBC),
absorbed by the acoustic transport where he runs a multi-disciplinary
medium (air, etc.) generating an research group focused on optical
abrupt, transient, localized density sensing and chemical imaging for
barrier, resulting in efficient biomedical and defense related
acoustic reflection at this interface applications. He is a Fellow of
that is largely independent of the SPIE (the International Society
acoustic frequency incident on for Photonics and Instrumentation
it. In fact, generating multiple Engineering), has served as a
THORS barriers it is possible to consultant for NATO’s Science
completely suppression incident for Peace Program, and is Head
acoustic waves from being of UMBC’s Nano-bioscience
transmitted (i.e., opto-acoustic Center. Having published over
silencing). This talk will describe 100 scientific papers/book
the THORS phenomenon and chapters and multiple patents, his
the associated proof-of-principle research efforts are focused on
studies into the capabilities the development, demonstration
and limitations of optically and application of novel optical
manipulating sound waves as phenomenon for chemical sensing
well as provide preliminary and imaging, with extensive
demonstrations of this newly experience in optical nanosensors,
discovered technique for acoustic plasmonics and opto-acoustics.
suppression and enhanced
measurement distances of acoustic

19

FIP 2020 Annual Symposium

Sanjay Padhi, PhD

Head of Amazon Web Services (AWS) Research, US Education

“Predictive Analytics using AWS”

One of the most explored features as AWS Research Credit program
of Big Data is predictive analytics. will also be highlighted.
Predictive analytics is a set of
techniques that are fundamental to Dr. Sanjay Padhi, leads the AWS
large organizations like Amazon.
Methods such as Machine Learning Research Initiatives including AWS’s
are used in many aspects of life,
including health care, education, federal initiatives with the National
financial modeling, and marketing.
Analytics on Big Data has given rise Science Foundation. He is a physicist and
to various “smart” projects, such
as Connected Intersections, Smart Adjunct Professor at Brown University.
Cities, and Smart Health. This talk
will provide a range of such studies Dr. Padhi has more than 15 years of
using predictive analytics including
detailed overview of methods such experience in large-scale distributed
as Machine Learning (ML) and
Deep Learning using AWS. Fully computing, Data Analytics and Machine
managed Artificial Intelligence
(AI) services to help researchers Learning. He is the co-creator of the
build, train and deploy ML models
in various domains including Workload Management System currently
Computer Vision and Natural
Language Processing (NLP) used for all the data processing and
will also be outlined. Supervised
and unsupervised based learning simulations by CMS, one of the largest
frameworks and its implications in
the fields of Scientific Computing, experiments in the world at CERN,
Medical Imaging, Cancer detection,
Diabetic Retinopathy, and Voice- consisting of more than 180 institutions
enabled solutions to improve
management of chronic disease will across 40 countries. He also co-founded
be discussed. Collaborations on
research with funding agencies such the ZEUS Computing Grid project at
as the National Science Foundation
and National Institutes of Health Deutsches Elektronen-Synchrotron
(including NIH STRIDES), as well
(DESY), Germany before joining CERN.

Sanjay obtained his Ph.D from McGill

University in High Energy Physics, co-

author of more than 900 publications

and is also currently appointed by the

Dean of Faculty as an Adjunct Professor

of Physics at Brown University.

20

Speaker Bios

Benjamin Watson, PhD

Associate Professor of Computer Science

North Caorlina State University

“Truly smart displays for emerging immersive experiences”

Displays are stuck in the past, and Benjamin Watson is Associate Professor
are strangling innovation. To realize of Computer Science at North Carolina
emerging high-bandwidth immersive State University. His Visual Experience
experiences in conferencing, Lab focuses on the engineering of
sensemaking, training, teleoperation visual meaning, and works in the fields
and entertainment, displays will have of graphics, visualization, interaction
to abandon decades-old assumptions. and user experience. His work has
Rather than requiring visuals organized been applied in entertainment, security,
into frames, displays will accept new finance, education, and medicine. Watson
information only where and when it is co-chaired the Graphics Interface 2001,
needed. Instead of expecting only pixels, IEEE Virtual Reality 2004 and ACM
displays will also support edges, textures, Interactive 3D Graphics and Games (I3D)
and other higher-level primitives. In 2006 conferences, and was co-program
contrast to knowing nothing about the chair of I3D 2007. Watson is an ACM
viewer or the previous image, displays and senior IEEE member. He earned his
will track viewers and remember recent doctorate at Georgia Tech’s Graphics,
visuals. Unlike today’s “smart” displays, Visualization and Usability Center.
these truly smart displays will fluidly
support group conversation, team

training, telemedicine and more.

Duke Speakers

Alberto Bartesaghi, PhD

Associate Professor of Computer Science, Biochemistry and
Electrical and Computer Engineering,Duke University

“The Cryo-EM Revolution: Leveraging the Potential of a Break-

through in Molecular Imaging”

Cryogenic electron microscopes allow devastating diseases, including HIV,
researchers to peer at the microscopic cancer and Alzheimer’s disease. High
shape of proteins like never before. energy electrons, however, are extremely
These machines blast proteins with a damaging to proteins and to help
300,000-volt beam of electrons so that protect the samples in the microscope,
highly sensitive detectors underneath researchers cryogenically freeze them to
can tease out their shapes based on the help maintain their integrity and use very
interaction with the sample. Recognizing low electron doses to prevent structural
protein structure and function is essential damage. This allows the recording of
for scientists trying to design better images of intact proteins and biomolecular
drugs to tackle some the world’s most structures that were previously

21

FIP 2020 Annual Symposium

Duke Speakers

inaccessible to other technologies. and later became an Associate Scientist
Modern electron detectors can record with the Center for Cancer Research.
rapid bursts of frames (up to 1,500 Dr. Bartesaghi received the “Norman P.
frames per second), allowing the capture Salzman Memorial Award in Virology”
of individual electron events during the from the Foundation for the National
exposure and resulting in extremely low Institutes of Health, Bethesda, MD for
signal-to-noise ratio images. Recent his work on the molecular architecture
advances in computational imaging have of native HIV-1 gp120 trimers. In 2018,
allowed the correction of the naturally he joined the Departments of Computer
occurring drift of the biological sample Science and Biochemistry at Duke
during the exposure -caused by beam- University as an Associate Professor. Dr.
induced motion- dramatically improving Bartesaghi is a pioneer in the development
image resolution and allowing the of computational methods for solving
visualization of the 3D shape of complex structures of large macromolecular
molecular machines at unprecedented complexes by single particle cryo-EM,
levels of detail. These technological cryo-electron tomography and sub-
advances have turned cryo-EM into a volume averaging. He solved many
powerful tool for biological discovery influential high-resolution structures
and structure-based drug design. including those of DNA-targeting
CRISPR/Cas9 surveillance complexes,
training, telemedicine and more. G-protein coupled receptors (GPCRs),
the human cancer target p97, membrane
Alberto Bartesaghi received his B.Sc. and transporters and channels involved in
M.Sc. from the Department of Electrical signaling and metabolism, and envelope
Engineering at the Universidad de viral glycoproteins including SIV,
la Republica, Montevideo, Uruguay, HIV1, Influenza and Ebola. He is also
and his Ph.D. from the Department of interested more broadly in data science,
Electrical and Computer Engineering machine learning, computer vision,
at the University of Minnesota, and molecular computational imaging.
Minneapolis, MN. In 2005, he joined the
Biophysics Section of the Laboratory of
Cell Biology at the NCI/NIH, Bethesda,
MD to conduct his post-doctoral studies,

Lawrence Carin, PhD

Vice President for Research, Duke University
James L. Meriam Distinguished Professor of Electrical and
Computer Engineering, Professor of Computer Science and
Statistical Science, Duke University

“Deep Learning Diagnosis of Thyroid Cancer from Cytopathology

Images”

We consider preoperative prediction images. Inspired by how human experts
of thyroid cancer based on ultra-high- perform diagnosis, our machine learning
resolution whole-slide cytopathology approach first identifies and classifies

22

Speaker Bios

diagnostic image regions containing Initiatives and Research Support. Carin
informative thyroid cells, which only joined Duke’s faculty in 1995 as an associate
comprise a tiny fraction of the entire image. professor of electrical engineering; he
These local estimates are then aggregated subsequently became the William H.
into a single prediction of thyroid Younger Distinguished Professor and
malignancy. Several unique characteristics chair of the Department of Electrical
of thyroid cytopathology guide our deep- Engineering. Since July 1, 2018, Carin has
learning-based approach. While our held the James L. Meriam Distinguished
method is closely related to multiple- Professorship. His early research was in
instance learning, it deviates from these the area of electromagnetics and sensing,
methods by using a supervised procedure but over the last fifteen years, he has moved
to extract diagnostically relevant regions. to applied statistics and machine learning.
Moreover, we propose to simultaneously Carin has become one of the leaders in
predict thyroid malignancy, as well as Machine Learning and one of the most
a diagnostic score assigned by a human widely published machine learning
expert, which further allows us to devise an researchers in the world, co-authoring
improved training strategy. Experimental nearly 400 academic papers, affecting
results show that the proposed algorithm fields as diverse as bomb detection,
achieves performance comparable to neuroscience, and voting behavior.
human experts, and demonstrate the
potential of using the algorithm for Carin is passionate about making a
screening and as an assistive tool for the difference through his work, and serves
improved diagnosis of indeterminate cases. as co-founder and Chief Scientist of
Infinia ML, solving some of the toughest
Vice President for Research Lawrence machine learning challenges in the world.
Carin oversees Duke’s compliance
with regulations on government- Carin earned BS, MS, and Ph.D.
funded research. The Office of Research degrees in electrical engineering
includes the offices of campus Research at the University of Maryland,
Development, Export Controls, Human College Park. He is an IEEE Fellow.
Subjects Protection, Licensing and
Ventures, Postdoctoral Services, Research

23

FIP 2020 Annual Symposium

Olivier Delaire, PhD

Associate Professor of Mechanical Engineering and Materials

Science, Physics and Chemistry, Duke University

“Terahertz dynamics of atoms in materials: x-ray scattering and

ultrafast experiments”

A detailed view of atomic vibrations in combination of inelastic x-ray scattering,
crystals is needed to refine microscopic ultrafast pump-probe measurements,
theories of energy transport and and first-principles simulations.
thermodynamics, and to design next- Further, we will describe recent results
generation materials. Understanding the on the 2D material tin sulfide (SnS),
behavior of terahertz vibrations (phonons) with applications in photovoltaics,
is key to rationalize numerous functional photonics, and thermoelectrics. The
properties, ranging from multiferroics for presentation will conclude with some
spintronics or metal-insulator transitions
for ultrafast transistors, to superionics for possible scientific opportunities.
all-solid batteries, and thermoelectrics
for waste-heat harvesting. In some Olivier Delaire obtained a PhD in
materials, the phonon-like vibrations Materials Science from Caltech (2006).
can also become significantly coupled to After a postdoc at Caltech, he became
electronic and spin degrees-of-freedom, a Clifford Shull fellow at Oak Ridge
or the phonons can strongly scatter from National Lab (2008), and subsequently a
each other. Such deviations from the Staff Researcher there. Since 2016, he is
textbook phonon gas model could open Associate Professor at Duke University.
the door to further tuning of materials His group leads projects investigating
properties for improved functionality, atomic dynamics in a wide range of
including in photonics and phononics. materials for energy applications,
In order to study these THz dynamics, sensing, and information storage.
we perform experiments with inelastic (ARVO) Foundation/Pfizer Ophthalmics
x-ray scattering, Raman spectroscopy, Carl Camras Translational Research
ultrafast free-electron lasers, or neutron Award. He is a Fellow of IEEE and
scattering. In addition, we carry out SPIE. sensing, and information storage.
first-principles simulations to rationalize
our measurements. This presentation
will highlight our investigations of
THz atomic dynamics and their impact
on the thermodynamics and ultrafast
mechanism of the fascinating metal-
insulator phase transition in vanadium
dioxide (VO2), which were studied with a

24

Speaker Bios

Sina Farsiu, PhD

Paul Ruffin Scarborough Distinguished Associate Professor of
Engineering, Associate Professor in Departments of Biomedical
Engineering, Ophthalmology, Electrical and Computer Engineer-
ing, and Computer Science, Duke University

“Novel Artificial Intelligence Tools for Bench to Bedside Ophthal-

mology and Neuroscience Applications”

In the past decade, our laboratory has a patient is likely to respond to costly
pioneered the development and utilization
of artificial intelligence (AI)-based and burdensome treatments.
methods for automatic quantification
of anatomic and pathologic biomarkers Sina Farsiu received his Ph.D. in
of the brain and retinal neurons on electrical engineering from the
photonic images. These include the University of California, Santa Cruz
first fully-automatic machine learning (UCSC), in 2005. He is currently the Paul
algorithm for the detection of multiple Ruffin Scarborough Associate Professor
retinal diseases from optical coherence of Engineering and the director of the
tomography (OCT) and the first deep Vision and Image Processing Laboratory
learning algorithms for quantification in the Departments of Biomedical
of retinal neurons from adaptive optics Engineering and Ophthalmology,
(AO) scanning laser ophthalmology with secondary appointments in
and OCT. From two-photon microscopy the Departments of Electrical and
imaging of neuronal activity in mouse Computer Engineering, and Computer
brains to AO-OCT imaging of ganglion Science at Duke University. Dr. Farsiu
cells in human eyes, our algorithms have is a Senior Area Editor for the IEEE
demonstrated on par, if not superior, Transactions on Image Processing, an
performance to human graders. Yet, as Associate Editor of Biomedical Optics
there are no established thresholds to Express, an Associate Editor of SIAM
define acceptable performance based on Journal on Imaging Sciences, and a
traditional performance metrics, it is not program committee member of SPIE
clear whether AI-based algorithms can Ophthalmic Technologies and ARVO
be reliably used in clinical applications. Imaging in the Eye conferences. He
In this presentation, we show the is a recipient of the Association for
clinically-relevant performance of a new Research in Vision and Ophthalmology
fully-automatic AI-based OCT analysis (ARVO) Foundation/Pfizer Ophthalmics
method, by demonstrating its ability to Carl Camras Translational Research
reliably reproduce the primary outcome Award. He is a Fellow of IEEE and
measure of a clinical trial. Furthermore, SPIE. sensing, and information storage.
we introduce a new algorithm that
can be used to automatically analyze
retinal OCT images to predict whether

25

FIP 2020 Annual Symposium

Roarke Horstmeyer, PhD

Assistant Professor of Biomedical Engineering and Electrical &

Computer Engineering, Duke University

“Using machine learning to design intelligent computational micro-

scopes”

Deep learning algorithms offer a powerful rapid sampling methods to aid with the
means to automatically analyze the search for salient features within large,
content of biomedical images. However,
many biological samples of interest are thick cytopathology specimens.
difficult to resolve with a standard optical
microscope. Either they are too large to fit Roarke Horstmeyer is an assistant
within the microscope’s field-of-view, or professor of Biomedical Engineering,
too thick, or are quickly moving around. Electrical and Computer Engineering,
In this talk, I will discuss our recent and Physics at Duke University.
work in addressing these challenges He develops microscopes, cameras
by using deep learning algorithms to and computer algorithms for a wide
design new experimental strategies range of applications, from forming
for microscopic imaging. Specifically, 3D reconstructions of organisms to
we use deep neural networks to jointly detecting blood flow and neuronal
optimize the physical parameters of actvity deep within tissue. Most recently,
our computational microscopes - their Dr. Horstmeyer was a visiting professor
illumination settings, lens layouts and at the University of Erlangen in
data transfer pipelines, for example - for Germany and an Einstein International
specific tasks. Examples include learning Postdoctoral Fellow at Charitè Medical
specific illumination patterns that can School in Berlin. Prior to his time in
improve classification of the malaria Germany, Dr. Horstmeyer earned a PhD
parasite by up to 15%, establishing new from Caltech’s EE department (2016),
image capture strategies for accurate in- an MS from the MIT Media Lab (2011),
silico fluorescent labeling, and creating and bachelor’s degrees in Physics
and Japanese from Duke in 2006.

26

Speaker Bios

Guillermo Sapiro, PhD

James B. Duke Distinguished Professor of Electrical & Computer
Engineering, Professor of Computer Science and Mathematics,
Duke University

“Machine Learning and Computer Vision for Neurodevelopmental

Disorders: Helping One Child at a Time”

Despite significant recent advances in Guillermo Sapiro was born in Montevideo,
molecular genetics and neuroscience, Uruguay, on April 3, 1966. He received his
behavioral ratings based on clinical B.Sc. (summa cum laude), M.Sc., and Ph.D.
observations are still the gold standard from the Department of Electrical Engineering
for screening, diagnosing, and assessing at the Technion, Israel Institute of Technology,
outcomes in neurodevelopmental in 1989, 1991, and 1993 respectively. After
disorders, including autism spectrum post-doctoral research at MIT, Dr. Sapiro
disorder, the core of this talk. Such became Member of Technical Staff at the
behavioral ratings are subjective, research facilities of HP Labs in Palo Alto,
require significant clinician expertise California. He was with the Department of
and training, typically do not capture Electrical and Computer Engineering at
data from the children in their natural the University of Minnesota, where he held
environments such as homes or schools, the position of Distinguished McKnight
and are not scalable for large population University Professor and Vincentine Hermes-
screening, low-income communities, or Luh Chair in Electrical and Computer
longitudinal monitoring, all of which Engineering. Currently he is a James B.
are critical for outcome evaluation in Duke School Professor with Duke University.
multisite studies and for understanding G. Sapiro works on theory and applications
and evaluating symptoms in the in computer vision, computer graphics,
general population. The development medical imaging, image analysis, and
of computational approaches to machine learning. He has authored and
standardized objective behavioral co-authored over 450 papers in these
assessment is, thus, a significant unmet areas and has written a book published by
need in autism spectrum disorder in Cambridge University Press, January 2001.
particular and developmental and G. Sapiro was awarded the Gutwirth
neurodegenerative disorders in general. Scholarship for Special Excellence in
Here, we discuss how computer vision Graduate Studies in 1991, the Ollendorff
and machine learning can develop Fellowship for Excellence in Vision and
scalable low-cost mobile health methods Image Understanding Work in 1992, the
for automatically and consistently Rothschild Fellowship for Post-Doctoral
assessing existing biomarkers, from Studies in 1993, the Office of Naval
eye tracking to movement patterns and Research Young Investigator Award in 1998,
affect, while also providing tools and big the Presidential Early Career Awards
data for novel discovery. We will present for Scientist and Engineers (PECASE)
results from our multiple clinical studies, in 1998, the National Science
where we have already collected the Foundation Career Award in 1999, and
largest available data in the field, as well the National Security Science and
Engineering Faculty Fellowship in 2010.
as the challenges of the discipline. He received the test of time award
at ICCV 2011 and at ICML 2019.
He was elected to the American
academy of Arts and Sciences in 2018.

27

FIP 2020 Annual Symposium

Pei Zhong, PhD

Professor of Mechanical Engineering and Materials Science and

Biomedical Engineering, Duke University

“Advancing Laser Technologies for Minimally Invasive Treatment
of Kidney Stones”

Urolithiasis (or commonly known as the P20 program also includes a unique
kidney stone disease) is a benign but educational enrichment program
severely painful genitourinary disease (EEP) with emphasis on engineering
that is on the rise and is the second most design and entrepreneurship.
costly urologic condition in the US with
a healthcare cost over $2 billion/year. Pei Zhong, PhD is a Professor
The treatment of urolithiasis is shifting of Mechanical Engineering and
from shock wave lithotripsy (SWL) to Materials Science, Biomedical
intracorporeal laser lithotripsy (LL) via Engineering, and an Associate
ureteroscopy. Despite this, the knowledge Professor of Urologic Surgery at
of laser-stone-tissue interaction has Duke University. Professor Zhong’s
not advanced commensurably in the research focus on therapeutic
past decade, with many unresolved ultrasound, medical laser therapy,
issues that hinder the clinical treatment and biomechanics with applications
of stone patients. With the support of in lithotripsy technology development
a NIH P20 grant, we are building a (e.g., shock waves and laser) for
Center for Urological Research and kidney stone treatment, ultrasound-
Engineering (CURE) at Duke to promote mediated drug and gene delivery,
multidisciplinary research collaborations high-intensity focused ultrasound
in partnership with medical device for cancer immunotherapy, and
industry (e.g., Dornier MedTech) and bioeffects of cavitation at the single
with the KURe program and NIDDK cell level. He is a recipient ofAmerican
U01 Urinary Stone Disease Research Foundation for Urological Disease/
Network already exist in the School Searle New Investigator Research
of Medicine. In this talk, I will present Award (1994) and NIH MERIT Award
an overview of our current research (2010), and a Fellow of ASME (2011).
activities in this P20 program, focusing
on characterizing the optical, thermal,
acoustic and mechanical properties of
kidney stones of various compositions,
and investigating the thermal and
cavitation-related mechanisms of
stone damage produced by various
modes of LL (e.g., fragmentation vs.
dusting). We are combining high-speed
photoelastic/shadowgraphic imaging,
OCT and multiphysics modeling to
gain insight into the critical processes
responsible for stone damage and
collateral tissue injury, and to propel LL
technology innovations. Furthermore,

28

Session Chairs & Panelists

Session 1: Special Topic – “Photonics in the Era of Data Science: from
Smart Sensing and Imaging to Artificial Intelligence (AI)”
11:10am-12:00pm Monday

Chair: Weitao Yang, Philip J. Handler Distinguished Professor of Chemistry,
Duke University

Prof. Yang, the Philip Handler Professor of Chemistry, is developing methods
for quantum mechanical calculations of large systems and carrying out quantum
mechanical simulations of biological systems and nanostructures. His group has
developed the linear scaling methods for electronic structure calculations and more
recently the QM/MM methods for simulations of chemical reactions in enzymes.

Session 2: Panel Session – “Digital Health and Immersive Medicine:
Challenges and Prospects of the Medicine of the Future”
2:40pm-3:10pm Monday

Co -Chair & Panelist: Frederic Zenhausern, Endowed Chair Professor, Basic
Medical Sciences, Professor of Radiation Oncology and Biomedical Engineering,
Director, Center for Applied Nanobioscience and Medicine, College of Medicine
Phoenix, University of Arizona; Professor, School of Pharmacy, University of
Geneva (Switzerland)

Dr. Zenhausern was founder director of the Center for Applied Nanobioscience at the Arizona
State University’s Biodesign Institute, and co- founder and R&D director of the first phase
of the Center for Flexible Display and then CTO of MacroTechnology Works, a university
think tank. Zenhausern was also tenured Professor with both the Electrical Department and
the School of Materials at the Ira A. Fulton School of Engineering. Dr. Zenhausern is also
Professor at the Translational Genomics Research Institute (TGen) and lead innovative
clinical research initiatives as director of the Personalized Medicine Research Laboratory at
Honor Health Research Institute. Over a decade, Zenhausern held corporate research positions
at IBM Research Division and Motorola Labs. He was also a senior research scientist at
Firmenich Inc., Princeton (NJ). He is also an “Professeur Titulaire (Adjunct)” with active
research collaboration at the University of Geneva’s School of Pharmaceutical Sciences. Dr.
Zenhausern received his B.S. in biochemistry from the University of Geneva, his M.B.A. in
finance from Rutgers University and his Doctorate Es Science in Applied Physics from the
Department of Condensed Physics Matter at the University of Geneva. He has co-authored
about 80 scientific publications and over three dozen U.S. patents. Zenhausern founded several
startups and currently sit on several corporate scientific boards. He is also the recipients of
several awards and an elected fellow of the U.S. National Academy of Inventors (NAI) and a
Fellow of the American Institute of Medical and Biological Engineering (AIMBE).

29

FIP 2020 Annual Symposium

“Digital Health and Immersive Medicine:
Challenges and Prospects of the Medicine of the Future”

30

Co -Chair: Tuan Vo-Dinh, Director of the Fitzpatrick Institute for Photonics, R.
Eugene and Susie E. Goodson Professor of Biomedical Engineering, Professor of
Chemistry, Duke University

Dr. Vo-Dinh is R. Eugene and Susie E. Goodson Distinguished Professor of Biomedical
Engineering, Professor of Chemistry, and Director of the Fitzpatrick Institute for
Photonics at Duke University. His research interests involve the development of advanced
technologies for the protection of the environment and the improvement of human health.
His research activities are focused on nanophotonics, biophotonics, nano-biosensors,
biochips, molecular spectroscopy, medical diagnostics and therapy, immunotherapeutics,
bioenergy research, personalized medicine, and global health. Dr. Vo-Dinh has received
seven R&D 100 Awards for Most Technologically Significant Advance in Research and
Development for his pioneering research and inventions of innovative technologies. He has
received the Gold Medal Award, Society for Applied Spectroscopy (1988); the Languedoc-
Roussillon Award (France) (1989); the Scientist of the Year Award, ORNL (1992); the
Thomas Jefferson Award, Martin Marietta Corporation (1992); two Awards for Excellence
in Technology Transfer, Federal Laboratory Consortium (1995, 1986); the Inventor of the
Year Award, Tennessee Inventors Association (1996); and the Lockheed Martin Technology
Commercialization Award (1998), The Distinguished Inventors Award, UT-Battelle (2003),
and the Distinguished Scientist of the Year Award, ORNL (2003). In 1997, Dr. Vo-Dinh was
presented the prestigious Exceptional Services Award for distinguished contribution to a
Healthy Citizenry from the U.S. Department of Energy. In 2011 Dr. Vo-Dinh received the
Award for Spectrochemical Analysis from the American Chemical Society (ACS) Division
of Analytical Chemistry.

Panelist: Geoffrey S. Ginsburg, Director, Center for Applied Genomics &
Precision Medicine, Duke University School of Medicine; Director, MEDx, School
of Medicine-Pratt School of Engineering, Professor Medicine and Pathology, Duke
University Medical Center; Professor of Biomedical Engineering and Professor in
the School of Nursing, Duke University

Dr. Ginsburg is the founding director for the Center for Applied Genomics & Precision
Medicine at the Duke University Medical Center and for MEDx, a partnership between
the Schools of Medicine and Engineering to spark and translate innovation. His research
addresses the challenges for translating genomic and digital health information into medical
practice and the integration of precision medicine into healthcare. In 2017 he received
Duke’s Translational Research Mentorship Award and is a finalist in the NIH/BARDA
Antimicrobial Resistance Prize. He is a member of the Advisory Council to the Director
of NIH and is co-chair of the National Academies Roundtable on Genomic and Precision
Health and is founder and president of the Global Genomic Medicine Collaborative, a not for
profit organization aimed creating international partnerships to advance the implementation
of precision medicine. He has recently served as a member of the Board of External Experts
for the NHLBI, the advisory council for the National Center for Accelerating Translational
Science, chair of the review for Genome Canada’s Large Scale Applied Research
Competition in Genomics and Precision Medicine, and the World Economic Forum’s Global
Agenda Council on the Future of the Health Sector. He is a founder of Predigen, Inc and
MeTree&You, Inc. He was previously Vice President of Molecular Medicine at Millennium
Pharmaceuticals, Inc and a faculty member at Harvard Medical School.

31

FIP 2020 Annual Symposium

Panelist: Sanjay Padhi, Head of Amazon Web Services (AWS) Research, US
Education

Dr. Sanjay Padhi, leads the AWS Research Initiatives including AWS’s federal initiatives
with the National Science Foundation. He is a physicist and Adjunct Professor at Brown
University. Dr. Padhi has more than 15 years of experience in large-scale distributed
computing, Data Analytics and Machine Learning. He is the co-creator of the Workload
Management System currently used for all the data processing and simulations by CMS, one
of the largest experiments in the world at CERN, consisting of more than 180 institutions
across 40 countries. He also co-founded the ZEUS Computing Grid project at Deutsches
Elektronen-Synchrotron (DESY), Germany before joining CERN. Sanjay obtained his Ph.D
from McGill University in High Energy Physics, co-author of more than 900 publications
and is also currently appointed by the Dean of Faculty as an Adjunct Professor of Physics
at Brown University.

Session 3: Special Topic – “Photonics in the Era of Data Science: from
Smart Sensing and Imaging to Artificial Intelligence (AI)”
3:30pm-4:50pm Monday

Chair: Adam Wax, Professor of Biomedical Engineering and Physics,
Duke University

Adam Wax received the B.S. degree in electrical engineering from Rensselaer
Polytecnic Institute, Troy, NY, and the second B.S. degree in physics from the State
University of New York at Albany, Albany, NY, in 1993, and the M.A. and Ph.D.
degrees in physics from Duke University, Durham, NC, in 1996 and 1999, respectively.
He was a Postdoctoral Fellow at George R. Harrison Spectroscopy Laboratory,
Massachusetts Institute of Technology, Cambridge. He is currently Professor of
Biomedical Engineering and Director of Graduate Studies at Duke University. His
research interests include optical spectroscopy for early cancer detection and novel
microscopy and interferometry techniques. Dr. Wax is a fellow of the Optical Society
of America, SPIE and AIMBE. He was named Outstanding Postdoc Mentor in 2012.

32

Session 4: Photonics in Research and Education
(Organized by the Duke Optical Student Chapter)
10:00am-12:00pm Tuesday

Lightning Talks 10:00am-10:45am Tuesday

Co -Chair: Evan Jelly, Duke Optical Student Chapter Vice President,
Department of Biomedical Engineering, Duke University

Evan Jelly joined Duke University’s Biomedical Engineering Department as a PhD
student in 2018. He holds a BS in Physics from The College of New Jersey, and a MSc
in Biophotonics with distinction from Cardiff University. Evan conducted research
developing fluorescent imaging systems with GE Healthcare Cell Technologies,
as well as Florida State University before joining Dr. Adam Wax’s BIOS Lab as a
research associate in March of 2017. Evan’s current research is focused towards
developing novel delivery schemes for optical coherence tomography and is active
in both scientific outreach and policy initiatives. At Duke, Evan is the recipient of
the John T. Chambers Fellowship from the Fitzpatrick Institute for Photonics and
currently serves as the 2019-2020 Duke Optical Student Chapter Vice President.

Co -Chair: Latifah Maasarini, Duke Optical Student Chapter Outreach
Chair, Department of Biomedical Engineering, Duke University

Latifah Maasarani is a first year PhD student in Biomedical Engineering at Duke
University. She works in Dr. Adam Wax’s BIOS Lab developing quantitative phase
imaging technologies. Latifah graduated with honors from CREOL, the College of Optics
and Photonics, at the University of Central Florida with a Bachelor’s Degree in Photonic
Science and Engineering in 2019. At Duke, Latifah remains active in community service,
leadership, and equity & diversity initiatives. Latifah is an Astronaut Scholar and a
recipient of the 2019 Jack Kent Cooke Graduate Scholarship. She currently serves as
the 2019-2020 Duke Optical Student Chapter Outreach Chair and the BME PhD Student
Association First Year Representative.


Panel Session “Keys to a Successful Career in Engineering &
Science” 11:00am-12:00pm Tuesday

Co-Chair & Moderator: Kristen Hagan, Duke Optical Student Chapter
President, Department of Biomedical Engineering, Duke University

Kristen Hagan is a third year Ph.D. candidate in Duke University’s Biomedical
Engineering Department. She graduate summa cum laude from The University of Texas
at Austin with a BS in Biomedical Engineering. At Duke University, she works under
the advisorship of Dr. Joseph Izatt. During her time at Duke, she has worked to develop
handheld adaptive optics ophthalmic systems to bring reliable, high-resolution cellular
imaging of the retina to the clinic. She is a recipient of the James B. Duke Fellowship,
the James T. Chambers Fellowship, the University Scholars Fellowship, and the National
Science Foundation Graduate Research Fellowship. She currently serves as the 2019-
2020 Duke Optical Student Chapter President.

33

FIP 2020 Annual Symposium


Panelist: Audrey Bowden, Dorothy J. Wingfield Phillips Chancellor
Faculty Fellow, Associate Professor of Biomedical Engineering and Electrical
Engineering, Vanderbilt University

Audrey K (Ellerbee) Bowden is an Associate Professor of Biomedical Engineering. She
received her BSE in EE from Princeton University, her PhD in BME from Duke University
and completed her postdoctoral training in Chemistry and Chemical Biology at Harvard
University. During her career, Dr. Bowden served as an International Fellow at Ngee
Ann Polytechnic in Singapore and as a Legislative Assistant in the United States Senate
through the AAAS Science and Technology Policy Fellows Program sponsored by the
OSA and SPIE. She is a member of the OSA, a Senior Member of SPIE and is the recipient
of numerous awards, including the Air Force Young Investigator Award, the NSF Career
Award, the Hellman Faculty Scholars Award, and the Phi Beta Kappa Teaching Award.
She is a former Associate Editor of IEEE Photonics Journal and a member of numerous
professional committees. Her research interests include biomedical optics, microfluidics,
and point of care diagnostics.

Panelist: Brian Cullum, Professor of Chemistry and Biochemistry,
University of Maryland Baltimore County

Dr. Brian M. Cullum is Professor of Chemistry and Biochemistry at the University of
Maryland Baltimore County (UMBC), where he runs a multi-disciplinary research
group focused on optical sensing and chemical imaging for biomedical and defense
related applications. He is a Fellow of SPIE (the International Society for Photonics and
Instrumentation Engineering), has served as a consultant for NATO’s Science for Peace
Program, and is Head of UMBC’s Nano-bioscience Center. Having published over 100
scientific papers/book chapters and multiple patents, his research efforts are focused on the
development, demonstration and application of novel optical phenomenon for chemical
sensing and imaging, with extensive experience in optical nanosensors, plasmonics and
opto-acoustics.

Panelist: Jessica DeGroote Nelson, Director of Technology and Strategy,
Optimax Systems, Inc.

Jessica DeGroote Nelson is the Director of Technology and Strategy at Optimax Systems,
Inc. Optimax is America’s largest optics manufacturer specializing in custom high
precision optics. Jessica is also an adjunct faculty member at The Institute of Optics at the
University of Rochester teaching both an undergraduate and graduate course on Optical
Materials, Fabrication and Testing. She earned her MBA at the Simon School and her
B.S., M.S. and Ph.D. in Optics at The Institute of Optics all at the University of Rochester.
Dr. Nelson is a member of The Optical Society (OSA) and a senior member of SPIE.

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Panelist: Donna Strickland, 2018 Nobel Laureate in Physics; Professor in
the Department of Physics and Astronomy, University of Waterloo, Canada

Donna Strickland is a professor in the Department of Physics and Astronomy at the
University of Waterloo and is one of the recipients of the Nobel Prize in Physics 2018
for developing chirped pulse amplification with Gérard Mourou, her PhD supervisor
at the time. They published this Nobel-winning research in 1985 when Strickland was
a PhD student at the University of Rochester in New York state. Together they paved
the way toward the most intense laser pulses ever created. The research has several
applications today in industry and medicine — including the cutting of a patient’s
cornea in laser eye surgery, and the machining of small glass parts for use in cell phones.
Strickland was a research associate at the National Research Council Canada, a physicist
at Lawrence Livermore National Laboratory and a member of technical staff at Princeton
University. In 1997, she joined the University of Waterloo, where her ultrafast laser group
develops high-intensity laser systems for nonlinear optics investigations. Strickland
was named a Companion of the Order of Canada. She is a recipient of a Sloan Research
Fellowship, a Premier’s Research Excellence Award and a Cottrell Scholar Award. She
received the Rochester Distinguished Scholar Award and the Eastman Medal from the
University of Rochester. Strickland served as the president of the Optical Society (OSA)
in 2013 and is a fellow of OSA, the Royal Society of Canada, and SPIE (International
Society for Optics and Photonics). She is an honorary fellow of the Canadian Academy of
Engineering as well as the Institute of Physics. She received the Golden Plate Award from
the Academy of Achievement and holds numerous honorary doctorates. Strickland earned
a PhD in optics from the University of Rochester and a B.Eng. from McMaster University.

“Keys to a Successful Career in Science and Engineering”

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FIP 2020 Annual Symposium

Session 5: Special Topic – “Photonics in the Era of Data Science: from
Smart Sensing and Imaging to Artificial Intelligence (AI)”
1:30pm-2:40pm Tuesday
Chair: Xiaobai Sun, Professor of Computer Science, Duke University

Dr.  Xiaobai Sun is a professor of Computer Science.  Her research has been on
numerical data analysis, approximation and compression theories, and numerical solution
methods with applications to signal reconstruction, image registration, shape matching,
communication detection and information propagation on networks, and exploratory
classification of text documents.

Session 6: Special Topic - “Advanced Photonic Technologies and
Systems”
3:00pm-4:20pm Tuesday
Chair: Shyni Varghese, Professor of Biomedical Engineering, Mechanical
Engineering & Materials Science, and Orthopaedic Surgery, Duke University

Shyni Varghese, Ph.D., is a Professor of Biomedical Engineering, Mechanical Engineering
& Materials Science, and Orthopaedic Surgery at Duke University. She is also the inaugural
MEDx investigator at Duke University. Prior to moving to Duke, she was a Professor
of Bioengineering at University of California, San Diego. Dr. Varghese’s research is
at the interface of biologically inspired materials and stem cells. Examples of ongoing
research activities in her laboratory involve developing functional biomaterials such as
self-healing materials; technologies to improve stem cell based therapies including stem
cell differentiation, cell transplantation, activating endogenous stem cells; and organ-on-a
chip systems. She is currently serving as an associate editor of Biomaterials Science (an
RSC journal).

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Poster Session Exhibit

1 Large Purcell Enhancement of SiVs in Diamond Coupled to Nanogap Plasmonic
Cavities

Andrew M. Boyce1, Nathan C. Wilson,2 Amirhassan Shams-Ansari,3 Marko Loncar3 & Maiken H. Mikkelsen1,2
1Department of Electrical & Computer Engineering, Duke University
2Department of Physics, Duke University
3Department of Electrical Engineering, Harvard University

Single photon sources could be crucial components of quantum computing and quantum information systems. Naturally occurring
single photon sources are insufficient for many applications because of their low photon count rates, short coherence times and lack of
a directional emission pattern. A novel nanostructure, the nanopatch antenna (NPA) consists of metallic nanoparticles separated from
a metallic film by a nanoscale gap layer and has demonstrated the largest emission rate enhancements of spontaneous emitters via the
Purcell effect to date, including 540-fold enhancement of single photon emission from colloidal quantum dots. However, applications
of colloidal quantum dots are limited due to their susceptibility to photobleaching and degradation over time. On the other hand,
color centers in diamond offer long coherence times and excellent photostability. Thus, combining these color centers with NPAs
could yield robust single photon sources with high photon count rates and directional emission. Our approach for integrating diamond
color centers into the NPA involves etching single-crystal diamond slabs with implanted silicon vacancies (SiVs). Nanoparticles are
fabricated by EBL and transferred onto the etched diamond slab on gold using a PDMS stamp. Fluorescence lifetime measurements
were carried out by using pulsed laser excitation at 514 nm. SiV emission was detected by low-jitter avalanche photodiodes and
tagged using a high-resolution time to digital converter. Fast decay components were measured on many different spots on the sample
that indicated instrument response limited lifetimes on the order of 10 ps, corresponding to ~100-fold enhancement compared to the
intrinsic lifetime of SiVs.

2Programming temporal DNA barcodes for single-molecule fingerprinting

Shalin Shah 1,2, Abhishek Dubey 2,3 and John Reif 1,2
1 Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States - 27701
2 Department of Computer Science, Duke University, Durham, NC, United States - 27701
3 Computational Sciences and Engineering Division, Health Data Sciences Institute, Oak Ridge National Lab, Oak Ridge, Tennessee
37831, United States

Fluorescence microscopy offers a versatile solution to study the dynamics of biology at the mesoscale. An important challenge in the
field is the simultaneous study of several objects of interest, referred to as optical multiplexing. For improved multiplexing, some prior
techniques used repeated reporter washing or the geometry of nanostructures; however, these techniques require complex nanostructure
assembly, multiple reporters, or advanced multistep drift correction. Here we propose a time-based approach, for improved optical
multiplexing, that uses readily available inexpensive reporters and requires minimal preparation efforts. We program short DNA strands,
referred hereby as DNA devices, such that they undergo unique conformation changes in the presence of the dye-labeled reporters. The
universal fluorescent reporter transiently binds with the devices to report their activity. Since each device is programmed to exhibit
different hybridization kinetics, their fluorescent time trace, referred to as the temporal barcode, will be unique [1]. We model our
devices using continuous-time Markov chains and use stochastic simulation algorithm to generate their temporal patterns [2]. We first
ran simulation experiments with a small number of DNA devices, demonstrating several distinct temporal barcodes, all of which use
a single dye color. Later, using nanostructure-based devices, we designed a much larger pool of temporal barcodes and used machine
learning for classification of these barcodes. Using our simulation experiments and design principles we also experimentally demonstrate
our DNA devices. We show DNA strands can be programmed for generating a multitude of uniquely identifiable molecular barcodes.
Our technique can be easily incorporated with the existing orthogonal methods that use wavelength or geometry to generate a large
pool of distinguishable molecular barcodes thereby enhancing the overall multiplexing capabilities of single-molecule imaging [3].

3Physics-enhanced machine learning for virtual fluorescence microscopy

Colin Cooke, Fanjie Kong, Amey Chaware, Rong Xu, Kanghyun Kim, Pavan Konda and Roarke Horstmeyer
Electrical and Computer Engineering, Duke University

This paper introduces a supervised deep-learning network which jointly optimizes the physical setup of an optical microscope to infer
fluorescence image information. Specifically, we design a bright-field microscope’s illumination module to maximize the accuracy of both
image segmentation and for virtual fluorescence labeling. We take advantage of the wide degree of flexibility available in illuminating
a sample to optimize for programmable patterns of light from a customized LED array, which produce better task-specific performance
than standard lighting techniques. We achieve illumination pattern optimization by including a physical model of image formation within
the initial layers of a deep convolutional network. Our optimized illumination patterns result in up to a 45% performance improvement
as compared to standard methods, and we additionally explore how the optimized patterns vary in form as a function of inference task.
This work demonstrates the importance of optimizing the process of image capture via programmable optical elements to improve
automated analysis, and offers new physical insights into expected performance gains of recent fluorescence image inference work.

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FIP 2020 Annual Symposium

4 Deep learning-based automatic segmentation of retinal cavitations on OCT
images of MacTel2

Jessica Loo1, Cindy X. Cai2, Emily Y. Chew3, Martin Friedlander4,5, Glenn J. Jaffe2, and Sina Farsiu1,2
1Department of Biomedical Engineering, Duke University, Durham, NC
2Department of Ophthalmology, Duke University Medical Center, Durham, NC
3Division of Epidemiology and Clinical Applications, National Eye Institute, Bethesda, MD
4Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA
5The Lowy Medical Research Institute, La Jolla, CA

Retinal cavitations are a MacTel2 biomarker visible on OCT images as hypo-reflective spaces that are often angular or irregular in
shape. They differ from cysts seen in exudative conditions which tend to be round or oval and are less irregular. The dataset consisted
of 9501 B-scans from OCT volumes of 99 eyes enrolled in an international, multi-center, phase 2 interventional MacTel2 clinical
trial (NCT01949324). Cavitations tend to be located in the macula, and in particular, the temporal fovea. In this dataset, they were
present in 8.1% of the B-scans. Therefore, we undertook a two-stage approach to automatically segment the images. In the first stage,
a convolutional neural network (CNN1) classified if B-scans contained cavitations. Only B-scans which were classified as containing
retinal cavitations proceeded to the second stage where a second network (CNN2) segmented the cavitations in B-scans. Performance
was evaluated using gold standard manual segmentations by a trained Reader and compared to the alternative one-stage approach,
whereby all B-scans were segmented by CNN2 without being first classified by CNN1. Using the proposed approach, there was good
agreement between the manual and automatic segmentations and the average Dice similarity coefficient (DSC) was 0.94 ± 0.21. Using
the alternative one-stage approach, the average DSC was 0.85 ± 0.34. This algorithm will be useful to quantify retinal cavitations
and to assess longitudinal structure-function correlations in MacTel2 as they can potentially be a negative predictor of visual acuity.

5Development of A Functional in vitro Kidney Model By Integration of
Electrospinning And Human Stem Cell Differentiation Technologies

Xingrui Mou1, Rohan Bhattacharya1, Bowen Sun2, Po-Chun Hsu3, Samira Musah1,4,*
1Department of Biomedical Engineering, 2Department of Materials Science and Engineering, 3Department of Mechanical
Engineering and Materials Science, 4Department of Medicine, Division of Nephrology, Duke University, Durham, NC, USA.
*Corresponding author: email, [email protected]

The kidney is an essential organ for the maintenance of health and homeostasis through filtration of blood and production of hormones
to regulate organ function. Kidney disease has rapidly become a public health crisis as a growing number of patients develop the disease
with subsequent progression to end stage kidney disease and organ failure. Efforts to study the etiology and progression of kidney
diseases include the application of two-dimensional (2D) cell culture plates and animal models. However, challenges remain as 2D cell
culture systems lack the three-dimensional (3D) tissue structure and organization that play key roles in mediating cellular crosstalk and
cell-matrix interactions. Furthermore, animal models cannot accurately recapitulate human physiology due to species-specific differences
and divergent developmental and cell signaling pathways. Therefore, we aim to engineer a capillary-like 3D in vitro system that mimics
the human kidney glomerulus by applying electrospinning and stem cell differentiation technologies. The human kidney glomerulus
is a capillary-rich structure that serves as the primary site for blood filtration and it is the target of various forms of kidney diseases.
Specifically, we are developing 3D matrices coupled with microfluidic systems to mimic the structure and blood filtration functions
of the human kidney glomerular capillaries. We will integrate these engineered scaffolds with human glomerular podocytes obtained
through our previously reported stem cell differentiation method and an endothelial cell differentiation protocol to generate kidney cells.
The resulting in vitro system could serve as a platform for mechanistic studies and therapeutic discoveries for human kidney diseases.

6Towards an intelligent microscope: adaptively learned illumination for optimal
sample classification

Amey Chaware*, Colin Cooke*, Kanghyun Kim, Dr. Roarke Horstmeyer
Department Electrical and Computer Engineering, Department of Biomedical Engineering, Duke University

Recent machine learning techniques have dramatically changed how we process digital images. However, the way in which we capture
images is still largely driven by human intuition and experience. This restriction is in part due to the many available degrees of freedom
that alter the image acquisition process (lens focus, exposure, filtering, etc.). Here we focus on one such degree of freedom - illumination
within a microscope - which can drastically alter information captured by the image sensor. We present a reinforcement learning
system that adaptively explores optimal patterns to illuminate specimens for immediate classification. The agent uses a recurrent latent
space to encode a large set of variably illuminated samples and illumination patterns. We train our agent using a reward that balances
classification confidence with image acquisition cost. By synthesizing knowledge over multiple snapshots, the agent can classify on the
basis of all previous images with higher accuracy than from naively illuminated images, thus demonstrating a smarter way to physically
capture task-specific information.

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7Designing a task-specific microscope using a deep neural network to improve
image classification accuracies

Kanghyun Kim [1], Pavan Chandra Konda [2], and Roarke Horstmeyer [1, 2]
[1]: Department of Electrical and Computer Engineering, Duke University, Durham NC, USA
[2]: Department of Biomedical Engineering, Duke University, Durham NC, USA

Traditionally, microscopes are optimized to create high-resolution images over a large area for visual inspection, which often leads
to bulky optics and expensive sensors. In many scenarios, machine learning (ML) algorithms do not necessarily need a well-resolved
image for their inference tasks - instead, an optimally coded low-resolution image can contain a sufficient amount of information for
high ML decision accuracy. Here, we present a framework to design a microscope, using a novel deep neural network, which can jointly
optimize the microscope’s hardware for improved ML performance. In this work, we use supervised learning to optimize both the
pupil transmission function and the pattern of sample illumination, which is otherwise a challenging optimization problem due to the
large number of constraints involved. The pupil transmission and illumination patterns are sample-specific and can be easily updated
to optimally classify alternative sample categories in a simple, low-cost, 4F microscope, by changing the pupil plane mask and the
illumination pattern on a programmable LED array. Our preliminary results show that by jointly designing imaging system hardware
with a deep network software, we can develop low-resolution wide-field microscopes that can provide higher task-specific classification
throughput and thus faster diagnoses than alternative approaches.

8The Effect of Magnetic Fields on Plasmon Resonance Frequencies of Noble Metal
Nanostructures

Peter Novello1, Siyuan Zhu1, Pani Varanasi2,3, Jie Liu1
1. Department of Chemistry, Duke University. Durham, NC. 2. Army Research Office, Research Triangle Park, NC. 3. Department of
Physics, Duke University. Durham, NC.

Magnetic fields are demonstrated to affect surface plasmon-polaritons on non-magnetic Au nanostructures. Previously, the magneto-
optical Kerr and Faraday polarization dependent effects have been observed on hybrid magnetic-plasmonic structures, and it has been
theoretically demonstrated that under high magnetic field strength (>10T) a splitting of the plasmon absorption band occurs with
circularly polarized light. Here, the surface plasmon-polariton absorption by Au nanostructures are measured utilizing a magneto-
spectrometer with a magnetic field force up to 1.5T and light with varied polarization. It is found that as the magnetic field strength
increases, the energy and the lifetime of the plasmon resonance are altered. Coupled with theoretical work, our findings suggest a Lorentz
force is acting directly on the elections under a magnetic field causing changes to surface plasmon polariton. Though providing further
insight into surface plasmons, this work grants access to increased understanding across the fields of plasmonic catalysis, sensing, and
optoelectronics.

9Real-time 3D Single Molecule Tracking Microscopy

Shangguo Hou, Jack Exell and Kevin Welsher
Department of Chemistry

Single molecule tracking has the potential to revolutionize the study of biological systems. However, most current single molecule
imaging and tracking methods are limited to two dimensions. Meanwhile, real-time 3D single molecule tracking methods, which use
active feedback to “lock-on” to single molecules in solution have until now been limited to observation times of 500 ms or less, greatly
limiting their utility. Recently we developed a real-time 3D single particle tracking method known as 3D Dynamic Photon Localization
Tracking (3D-DyPLoT). By implementing optimized tracking parameters based on single molecule model simulation, in here we present
a 3D single-molecule active real-time tracking method (3D-SMART) which is capable of track single fluorophores in solution for
several minutes at a time, two orders of magnitudes longer than previously reported methods, with photon limited temporal resolution.
We demonstrate the application of 3D-SMART to track the free, 3D diffusion of DNA, RNA and proteins in solution. With two-color
observation channels, 3D-SMART was also applied to observing the transcription of single DNA molecules in solution by monitoring
the real-time production of mRNA. This real-time 3D single molecule tracking method promises to be powerful tool for capturing the
dynamics of single biomolecules at high speeds and over 3D distances.

10High-Quality Factor All-Dielectric Metamaterial Photonic Biosensor

Natalie Rozman, Kebin Fan, Willie Padilla,
Department of Electrical and Computer Engineering, Duke University.

The overarching objective of the proposed research is to investigate the feasibility of an all-dielectric metasurface with high specificity
and sensitivity for biosensing applications. Metamaterials can be tailored to have an extremely precise response in a desired frequency
range. Using this innovative structure, I will create a photonic biosensor for Ebola virus disease detection. Through understanding of

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FIP 2020 Annual Symposium

the physics underlying the existence of Bound-States-in-the-Continuum (BICs), I have performed initial simulations verifying designs
which may be used to carry out the proposed research. An ideal structure consists of all-dielectric metasurfaces (ADMs) comprised of
a free-standing square array of silicon cylinders attached with thin fins for a varying asymmetry parameter (α). The geometry of the
resonator was altered by removing part of the cylinder by α, while maintaining the periodicity P, and interconnect width w, as depicted
in the inset in Fig. 1. This asymmetry gives rise to several modes that result in an unprecedented high-Q exceeding 106, observed in the
simulated frequency dependent transmission (T(ω)) at f/f_0). Initial simulations were carried out, verifying that this structure can be
designed for the near-infrared range for photonic biosensing applications. Thus the high spectral resolution of this ADM structure shows
great promise for metamaterial applications in biosensing detection.

11Meta-Learning Approach to Automatically Register Multi-Vendor Images

Ali Hasan1, Zengtian Deng1, Jessica Loo1, Dibyendu Mukherjee1, Jacque Duncan3, David Birch4, Glenn J. Jaffe2, Sina Farsiu12
1Department of Biomedical Engineering, Duke University, Durham, NC
2Department of Ophthalmology, Duke University, Durham, NC
3Department of Ophthalmology, University of California at San Francisco, San Francisco, CA
4Retina Foundation, Dallas, TX

Automatic retinal image registration is necessary to understand disease progression by comparing images at different points in time
and across different modalities. We present a new, automated approach to register multivendor retinal images. The dataset consisted
of 85 eyes enrolled in an international, multi-center clinical trial (NCT03146078) characterizing the progression of USH2A-related
retinal degeneration. The training set included scanning laser ophthalmoscopic (SLO) images simultaneously acquired with OCT,
where the SLO images were manually registered to microperimetry SLO images. We trained a convolutional neural network (CNN)
to predict the transformation matrix for an affine transformation using a spatial transformer network and trained an additional CNN
as a discriminator to estimate the quality of the registration. We finally used an iterative optimization algorithm based on model
agnostic meta-learning to refine the transformation. For a fair comparison on multivendor image data, we modified the approach
by (G. Balakrishnan et al. IEEE TMI 2019) to predict an affine transformation and to supervise the training. We also compared
our results to the state of the art non-deep learning registration method (GFEMR) described by (J. Wang et al. Signal Processing
157 2019). We considered the root mean squared error (RMSE) between the manually marked points on the moving OCT SLO
image and the fixed microperimetry SLO image as our criteria for success. The median RMSE for the test images was 106.8
± 19.7 before registration, 34.7 ± 10.5 using GFEMR, 17.1 ± 5.6 using the noniterative network, and 13.9 ± 4.5 for our method.

12Passive Cavitation Mapping in Shockwave Lithotripsy

Mucong Li1, Georgy Sankin2, Pei Zhong*2 and Junjie Yao*1
1Duke University Department of Biomedical Engineering
2Duke University Department of Mechanical Engineering and Materials Science

Shockwave lithotripsy enables non-invasive therapy for kidney stone patients. Cavitation generated by the tensile shockwaves plays
an important role in stone erosion. However, it is also the major reason that causes tissue damage and renal hemorrhage due to the
violent collapses of cavitation bubbles. It is therefore crucial to acquire temporal and spatial distributions of cavitation collapsing to
predict possible hemorrhage occurrences during the shockwave treatment. Conventional cavitation detection and mapping approaches,
including photography, active cavitation mapping (ACM) and passive cavitation mapping (PCM), provide incomplete cavitation collapse
information of individual bubbles for biological tissue and degrade the precision of localizing hemorrhages during the treatment due to
the temporal randomness of cavitation collapsing. To image the bubble collapse distributions more precisely, a sliding-window passive
cavitation mapping algorithm is proposed and validated. By sliding the reconstruction window and searching along time, correct time
origin for each individual bubble collapses can be identified. Laser-induced single bubbles were generated in free water space and in a
transparent plastic tube, respectively, and the cavitation collapse signals were captured and reconstructed using the proposed method.
High-speed camera imaging and active cavitation mapping were performed simultaneously as ground-truth to validate the accuracy of
passive method. Furthermore, lithotripter shockwave induced cavitation were acquired using the same methods. Images from all three
modalities match well and the overall experiments demonstrated the reliability and accuracy of the proposed cavitation mapping method.

13Real-Time Tuning of Plasmonic Nanostructures Using Photochromic
Molecules

Wade M. Wilson1,2,†, Jon W. Stewart1,2,† and Maiken H. Mikkelsen1,2,3,*
1Center for Metamaterials and Integrated Plasmonics, Duke University, Durham, NC 27708
2Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708
3Department of Physics, Duke University, Durham, NC 27708
*Correspondence to: [email protected], +1 (919) 660-0185
†These authors contributed equally to this work.

40

Plasmonic nanostructures with actively tunable optical properties could realize beam steering surfaces, on-chip hyperspectral
detectors and dynamically reconfigurable optoelectronic devices. A variety of tunable materials have been integrated with plasmonic
structures, however, the tuning range in the visible regime has been limited to less than the linewidth of the resonance, resulting
in small on/off ratios critical for these applications. Here we demonstrate all optical, real-time tuning of plasmon resonances up to
71 nm in the visible regime by incorporating spiropyran into plasmonic nanopatch antennas. The tuning mechanism is elucidated
by comparing experimental data to finite-element simulations using different models for the dispersion in the photochromic film.
This comparison reveals that the spectral shift is due to resonant coupling between spiropyran and the plasmonic nanoantennas.

14Towards high-speed, high-resolution imaging using binary measurements

Xi Yang, Pavan Chanda Konda, Roarke Horstmeyer
Department of Biomedical Engineering, Duke University

Recently developed Single-photon Avalanche Diode (SPAD) array cameras have single photon sensitivity and can provide time-of-flight
information for LIDAR imaging. These SPAD cameras, however, have very few pixels and readout binary images, which are typically
averaged to provide an image with sufficient dynamic range. Here, we propose to implement a modified version of Fourier ptychography
(FP), a synthetic aperture technique, on SPAD cameras to reconstruct an image with much higher resolution and larger dynamic range
from its binary measurements. We successfully validate this using simulated and experimental results to show its potential for recording
LIDAR images at high resolution and speed.

15Synergistic Immuno Photothermal Nanotherapy (SYMPHONY) for Effective
Brain Cancer Treatment

Yang Liu1, Pakawat Chongsathidkiet2, Paolo Maccarini1, Gregory M. Palmer3, Peter E. Fecci4, and Tuan Vo-Dinh1,5,6*
1. Duke University Biomedical Engineering Department
2. Duke University Medical Center Pathology Department
3. Duke University Medical Center Radiation Oncology Department
4. Duke University Medical Center Neurosurgery Department
5. Duke University Chemistry Department
6. Duke University Fitzpatrick Institute for Photonics
* Corresponding author: [email protected]

Glioblastoma (GBM) is the most common and aggressive primary brain cancer and it has more than 10,000 newly diagnosed patients in
the United States each year. Even with the highest first-year cost (> $120,000), the prognosis for GBM patients is dismal and the median
survival is only 15 months after aggressive treatments including surgery, chemotherapy, radiation therapy, and targeted drug therapy.
Less than 5% of the patients survive for more than 3 years. Despite enormous efforts, there have been no major treatment advances
in over two decades, and GBM is still a devastating disease with essentially 100% mortality. Therefore, there is a clear and urgent
clinical need to develop novel and effective therapeutic methods to dramatically improve GBM treatment efficacy. We have developed
an innovative cancer therapy named as Synergistic Immuno Photothermal Nanotherapy (SYMPHONY) by combining gold nanostars
(GNS)-mediated photothermal ablation with checkpoint inhibitor immunotherapy. GNS nanoparticle has multiple sharp branches for
tip-enhanced plasmonics and tunable plasmonic absorption in the near-infrared (NIR) tissue optical window. GNS has superior photon-
to-heat conversion capability for effective photothermal therapy with NIR laser. Our group has innovated a toxic chemical-free method
to synthesize GNS nanoparticles and apply the biocompatible GNS nanoparticles for SYMPHONY therapy. In vivo experiment with
brain cancer murine animal models demonstrate that our superior GNS-mediated photothermal therapy dramatically amplifies the anti-
cancer immune response in synergy with checkpoint blockade immunotherapy. SYMPHONY therapy results in not only primary tumor
shrinkage but also recurrence prevention, implying the generation of an anti-cancer vaccine effect. As a result, our novel SYMPHONY
therapy has the potential to substantially improve outcomes of brain cancer patients in future clinical applications.

16Differential phase contrast imaging on a gigapixel microscope

Jaehee Park, Pavan Konda, Roarke Horstmeyer, Mark Harfouche
Biomedical Engineering Department, Duke University

Asymmetric illumination-based differential phase contrast (AIDPC) is a full-field phase-gradient imaging method that can capture phase-
gradient information at microscopic resolution. However, currently explored differential phase contrast systems image over a small field
of view. In this work, we apply AIDPC to images acquired from an array of 96 imagers that exhibit partially overlapping fields-of-view,
enabling high-resolution (10 um) phase gradient measurements over hundreds of square centimeters. We present experimental results
showing how our multi-camera AIDPC approach recovers high-quality surface normals from both diffuse and specular macroscopic
objects.

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17Particle-by-particle in situ characterization of the protein corona via real-time
3D single particle tracking microscopy

Xiaochen Tan and Kevin Welsher
Department of Chemistry, Duke University

Engineered nanoparticles adsorb proteins when they are exposed to biological fluids, forming a dynamic protein corona that
consists of tightly bound “hard” corona, and rapidly exchanged “soft” corona. This full protein corona alters the surface identity of
nanoparticles, affecting their behavior in biological systems. However, a thorough understanding of the protein corona is lacking
due to the insufficiency of current ensemble techniques. Here, we introduce real-time single 3D particle tracking microscopy to
“lock-on” to single freely diffusing polystyrene nanoparticles (PSNPs) and probe their individual protein coronas in solution.
Using this method, we study the protein corona content of individual PSNPs in solutions containing bovine serum albumin (BSA),
immunoglobulins (Ig) and fetal bovine serum (FBS). Using mean squared displacement measurements combined with real-time
protein fluorescence, we quantified “hard” corona growth particle-by-particle. Furthermore, we applied a lock-in type frequency
filtering method to extract the full protein corona signal (i.e. both “soft” and “hard” corona) in high background environments. Our
method enables the study of individual protein coronas without removing the particle or the protein from its native solution, opening
up the possibility to study transient and dynamic protein-nanoparticle interactions which are destroyed by current bulk techniques.

18Weakly Supervised Deep Instance Segmentation of Retinal Ganglion Cells in
Healthy and Glaucomatous Eye

Somayyeh Soltanian-Zadeh,1 Kazuhiro Kurokawa,2 Zhuolin Liu,3 Ricardo Villanueva4, Osamah Saeedi4, Daniel X. Hammer,3
Donald T. Miller,2 and Sina Farsiu1,5
1Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
2School of Optometry, Indiana University, Bloomington, IN 47405, USA
3Center for Devices and Radiological Health (CDRH), U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
4Department of Ophthalmology and Visual Sciences, University of Maryland Medical Center, Baltimore, MD 21201, USA
5Department of Ophthalmology, Duke University Medical Center, Durham, NC 27710, USA 

Population-level and cell-level quantitative features of retinal ganglion cells (GCs) are potentially important biomarkers for improved in
vivo diagnosis and treatment monitoring of neurodegenerative diseases like glaucoma and Alzheimer’s disease. Adaptive optics optical
coherence tomography (AO-OCT) enables in vivo imaging of individual GCs in the human retina. Current manual analysis of GCs from
AO-OCT volumes is a subjective and time-consuming process, thus not practical for large scale studies. Here we present an automated
GC layer (GCL) soma quantification method from AO-OCT volumes based on weakly supervised deep learning, in which we trained our
neural network with click-points to segment individual GCL somas. We show that: (1) our method was highly accurate in counting GCL
somas from healthy and glaucoma subjects, achieving the same or higher than expert-level performance on different imagers, (2) the
measures of soma diameters were in line with previous histological and semi-automatic in vivo studies, and (3) our method generalized
to an unseen retinal location and between cohorts and devices. These results suggest that our method should have broad appeal for long-
term investigations of GC populations.

19Advanced Light Imaging and Spectroscopy (ALIS) at Duke

Xiaolei Wang1, Jun Jiang2, and Martin C. Fischer1,3
1 Department of Physics, 2 Department of Biomedical Engineering, and 3 Department of Chemistry, Duke University, Durham, NC

The Advanced Light Imaging and Spectroscopy (ALIS) facility provides cutting-edge optical imaging technology that is beyond the
capabilities of commercial instruments. The first instruments in the facility include: a lattice light-sheet microscope and a deep-tissue
serial sectioning microscope. The lattice light sheet microscope allows us to image 3D dynamics for hundreds of volumes at the diffraction
limit and beyond with super low photoxicity. The strength of this technology lies in the combination of high spatial resolution and high
acquisition speed. This microscope is ideally suited to image a range of biological samples, ranging from intracellular components
to small organisms (such as fruit fly embryos). The deep-tissue serial sectioning microscope operates at the other end of the spatial
scale: large biological samples (such as organs of a mouse). In such samples, high-resolution optical imaging is traditionally limited to
the surface layer because light scattering and absorption prevents focusing deeper. Our approach is to repeatedly image a small (few
hundred µm thick) layer of the sample at a time and then remove the imaged section to expose deeper layers. Fully automated imaging
and sectioning allows for gap-less, high-resolution imaging of large volumes. The 980-square-foot facility represents a significant
investment in the sciences at Duke and complements the capabilities of the Light Microscopy Core Facility, it will advance research of
many biological and medicine groups significantly.

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20Chalcogenide-based Photonic Quasicrystals for Novel Phase Matching

Jiannan Gao1, Wiktor Walasik1, Mikhail Shalaev1, Jesse Frantz2, Jason D. Myers2, Robel Y. Bekele3, Jasbinder S. Sanghera2, and
Natalia M. Litchinitser1
1Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, 27708, USA
2US Naval Research Laboratory, 4555 Overlook Ave., SW, Washington, DC 20375
3University Research Foundation, Greenbelt, MD, USA 20770, USA

Quasicrystal-based engineered photonic nanostructures are unique structures with long-range order but no periodicity. Quasicrystals
are a class of lattices characterized by a lack of translational symmetry but obeying rotational symmetry. Similar to naturally occurring
quasicrystals, due to their unique symmetry properties, artificial quasicrystals are expected to possess properties that are different from
both crystals and glasses. In this work, we focus on novel regimes of nonlinear light-matter interactions enabled by engineered photonic
quasicrystals. In particular, in the field of nonlinear optics, the advantage of using PQCs is the possibility of supporting unlimited
combinations of wavevectors that may appear in their reciprocal lattices. We report (i) the nonlinear interactions engineering using
two approaches – dispersion engineering utilizing the strongly modified dispersion relations in the vicinity of the bandgaps of the
PQC and quasi-periodic quasi-phase matching; (ii) design and fabrication of PQCs in chalcogenide waveguides; and (iii) experimental
demonstration of the predicted new phase-matching regimes enabled by the PQCs. These structures are realized in arsenic trisulfide
(As2S3) chalcogenide glass that displays a very good nonlinear figure of merit in both the near-infrared and the mid-wave infrared
spectral bands. Here, we show that the proposed structures realized in such a highly nonlinear medium may enable a promising
platform for both fundamental studies of nonlinear light-matter interactions and applications in wavelength conversion, supercontinuum
generation, and development of classical and quantum optical sources.

21Quantitative Phase Imaging provides mechanical phenotyping which aligns
with Atomic Force Microscopy

Silvia Ceballos, Han Sang Park, Will J. Eldridge and Adam Wax
Department of Biomedical Engineering, Duke University

Current standard methods for probing cellular stiffness are slow, difficult, and utilize complex or indirect detection schemes which
limit their ability to conduct large scale, or longitudinal studies of cellular mechanics. To enable direct, non-invasive measurement
of cellular mechanical properties, we previously developed a quantitative phase imaging (QPI) based method to measure cellular
deformation in response to shear stress. The primary mechanical parameters we used to discriminate between different cell and cancer
types was shear stiffness, analogous to a spring constant. Using this approach, we studied changes in cellular mechanical integrity due
to pharmacological treatment and heavy-metal toxicity. Now, we have established a direct relationship between QPI-derived mechanical
parameters and atomic force microscopy (AFM), the gold standard for measuring cellular viscoelastic characteristics. We formulate
a method for measuring shear modulus in live, adherent cells from QPI images of cells subjected to shear flow within a flow cell. To
validate the approach, we evaluate two breast cancer cell lines exposed to various levels of cytochalasin D, an actin depolymerizing
toxin. Each group is characterized by a commercial AFM to measure Young’s modulus and analyzed using our QPI system to quantify
shear modulus. We also compare both of these mechanical parameter to disorder strength, a phase fluctuation metric that reveals
structural homogeneity. Relationships between shear modulus and Young’s modulus measurements are found to strongly agree with
theory, confirming the validity of previous mechanical characterizations using QPI, indicating the utility of QPI as a powerful tool for
cellular mechanical studies.

22A Multi-Resolution Approach to Environment Visualization for Real-Time
Active Feedback Single Particle Tracking

Courtney Johnson, Shangguo Hou, Jack Exell and Kevin Welsher
Department of Chemistry, Duke University

Real-Time active feedback single particle tracking using the 3D-DyPLoT method is a powerful tool for locking-on to moving nanoscale
particles and obtaining quantitative information about their dynamics. However, the tracking system alone is unable to determine the
full nature of interactions between a particle and its environment due to the limited field of view required to track particles moving
at high speeds. Such interactions, as between a virus infecting a cell, underly many interesting biological questions. The integration
of a 3D-FASTR two-photon laser-scanning microscope into the pathway of the 3D-DyPLoT system enables the acquisition of 3D
volumes acquired simultaneously during particle tracking. This capability results in the visualization of co-registered 3D volumes with
trajectories that show the particle’s motion relative to the volume over time.

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23High-sensitivity pump-probe microscopy based on MHz time-delay
modulation

Jun Jiang, Warren S Warren and Martin C Fischer
Duke University Department of Biomedical Engineering, Physics and Chemistry

We present a new imaging method for femtosecond pump-probe microscopy that explores time-delay modulation at a MHz rate. This
method not only greatly reduces the background signal but also enables the real-time calculation of differences computation of images
with respect to the inter-pulse delay. We experimentally demonstrate this method by imaging perovskite solar cell materials as well as
fruit fly larvae, and demonstrate the real-time differencing on melanin nanoparticles and mice skin samples.

24Crossed-beam pump-probe microscopy

Jun Jiang, Warren S Warren and Martin C Fischer
Duke University Department of Biomedical Engineering, Physics and Chemistry

We present a new imaging method for pump-probe microscopy that explores non-collinear excitation. This method (crossed-beam
pump-probe microscopy, or CBPM) can significantly improve the axial resolution when imaging through low-NA lenses, providing
an alternative way for depth resolved, large field-of-view imaging. We performed a proof-of-concept demonstration, characterized
CBPM’s resolution using different imaging lenses, and measured an enhanced axial resolution for certain types of low-NA lenses.

25Wavefront sensorless multimodal handheld adaptive optics scanning laser
ophthalmoscope

Kristen Hagan1, Theodore B. DuBose1, David Cunefare1, Corey Simmerer1, Gar Waterman1, Jongwan Park1, Anthony N. Kuo1,2,
Ryan P. McNabb2, Joseph A. Izatt1,2, and Sina Farsiu1,2
1Department of Biomedical Engineering, Duke University, Durham, North Carolina;
2Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina;

Adaptive optics scanning laser ophthalmoscopy (AOSLO) has enabled in vivo imaging of individual cone and rod photoreceptors in the
human eye, revolutionizing the study of human retinal structure, function, and disease states by vision scientists and physicians. AOSLOs
are usually implemented as tabletop systems, thereby preventing patients who cannot sit upright and/or fixate for extended time periods
from being imaged by these high-resolution systems. Previously, we addressed this limitation and extended AOSLO to excluded patient
populations by designing and fabricating the first confocal handheld AOSLO (HAOSLO) capable of cone photoreceptor visualization in
both supine adults and neonates. However, recent studies have shown that confocal AOSLO images contain imaging artifacts and cannot
obtain information about comprised cone structure in diseased eyes, leading to ambiguous or misleading patient data. These studies have
demonstrated that the collection of multiply backscattered light into two offset non-confocal split-detection (SD) channels can allow for
a more reliable view of the patient retina by imaging the cone inner segment. In this project, we introduce the extension of our HAOSLO
probe for SD and confocal imaging of cone photoreceptors in the first ever multimodal (M)-HAOSLO probe. Confocal and SD images
revealing cone photoreceptors were collected in a tabletop implementation as well as in M-HAOSLO’s handheld operation mode from
healthy adult subjects. Besides the unprecedented miniature form factor and low weight, this is the first portable SD AOSLO system as
well as the first system to collect SD cone inner segment images following sensorless optimization of the wavefront.

26Mapping Solvation Heterogeneity in Live Cells by Hyperspectral Stimulated
Raman Scattering Microscopy

Xiaoqi Lang, Dr. Kevin Welsher
Department of Chemistry, Duke University

Water has long been known not just to be a spectator, but an active participant in biochemical processes. The local structure and
dynamics of intracellular water constitute the cornerstone for understanding cellular function. Fundamentally, direct visualization
of subcellular solvation heterogeneity is essential but remains challenging with commonly used NMR methods due to poor spatial
resolution. The classical physical chemistry tool to probing solvation in molecules is the use of vibrational frequency as a readout of the
molecular environment, particularly using vibration sensors such as nitriles. However, this approach has not been successfully applied
to map intracellular solvation due to the combination of small peak shifts and small Raman scattering cross-sections. Here, we boost
the Raman scattering signal using a near-resonant nitrile-containing molecule (Rhodamine 800) with hyperspectral stimulated Raman
scattering (hsSRS). The near-resonant condition of Rhod800 enables a 200-fold enhancement of the effective Raman scattering cross-
section, allowing solvation sensing in the solution phase, micron-scale droplets, and cellular environments. Through calibration, we
quantify the degree of solvation between the cytoplasm (29.5%, S.E. 1.8%) and the nucleus (57.3%, S.E. 1.0%) and further validate
this heterogeneity is related to the macromolecular crowding within the cytoplasm. These studies represent a new picture of the water
structure within cells, wherein there is less “bulk” or “free” water available for participating in hydrogen-bonding and solvation. This
work opens up new avenues to explore environmental variance in complex systems with high spatiotemporal resolution.

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27Applications of Nonlinear Optical Imaging in Cultural Artifacts Science
Yue Zhou1, Jin Yu1, Tana E. Villafana2, Warren S. Warren1-3, and Martin C. Fischer1-2
1Department of Biomedical Engineering, 2Department of Physics and 3Department of Chemistry, Duke University

Pump-probe microscopy is a powerful technique to improve the evaluation of artworks. Conventional research on paintings usually
relied on the physical-removed samples from the artworks, which cannot provide depth information. In pump-probe microscopy, with
the non-linear optical interactions between the ultrafast, pulsed laser beams and paints, high-resolution, three-dimensional images of
paint layers can be obtained without physical removal of samples. By the help of this technology, subsurface paint-layer analysis can
be achieved non-invasively with structural and molecular contrast in a 14th century painting; in addition, the light-induced degradation
of vermilion, a red historical pigment, was investigated and the degradation products were successfully differentiated and mapped on
micro-scale. In the future, the degradation of cadmium sulfide (CdS), a popular yellow pigment, will be imaged under pump-probe
microscopy and the potential degradation mechanisms will be studied. Here we provide an update on our spectroscopic and microscopic
techniques and describe applications to the study of cultural art objects.

28SERS-based Plasmonic Coupling Interference (PCI) Nanoprobes for Multiplex
Detection of MicroRNA Cancer Biomarkers

Hsin-Neng Wang1,2, Bridget M. Crawford1,2, Stephen J. Norton1,2, Tuan Vo-Dinh1,2,3(*)
1. Departments of Biomedical Engineering, Duke University, Durham, NC 27708, USA
2. Fitzpatrick Institute for Photonics, Duke University, Durham, NC 27708, USA
3. Department of Chemistry, Duke University, Durham, NC 27708, USA

MicroRNAs (miRNAs), small non-coding endogenous RNA molecules, are emerging as promising biomarkers for early detection of
various diseases and cancers. Practical screening tools and strategies to detect these small molecules are urgently needed in order to
facilitate the translation of miRNA biomarkers into clinical practice. In this work, a label-free biosensing technique based on surface-
enhanced Raman scattering (SERS), referred to as “plasmonic coupling interference (PCI)”, was applied for the multiplex detection of
miRNA biomarkers. The sensing mechanism of the PCI technique relies on the formation of a nanonetwork consisting of nanoparticles
with Raman labels located between adjacent nanoparticles that are interconnected by DNA duplexes. Due to the plasmonic coupling
effect of adjacent nanoparticles in the nanonetwork, the Raman labels exhibit intense SERS signals. Such effect can be modulated
by the addition of miRNA targets of interest that act as inhibitors to interfere with the formation of this nanonetwork, resulting in a
diminished SERS signal. In this study, the PCI technique is theoretically analyzed and the multiplex capability for detection of multiple
miRNA cancer biomarkers is demonstrated, establishing the great potential of PCI nanoprobes as a useful diagnostic tool for medical
applications.

29Probing the Spatial Heterogeneity of Photoexcitation Dynamics in Perovskite
Thin Films with Femtosecond Time-Resolved Nonlinear Optical Microscopy

Yuheng Liao, Dr. Jin Yu, Charles Kolodziej, Dr. Clemens Burda, Dr. Martin C. Fischer, Dr. Warren S. Warren
Department of Chemistry, Duke University; Department of Physics, Duke University; Department of Chemistry, Case Western Reserve
University

Hybrid organic-inorganic metal halide perovskites (PVSKs), known as the next generation solar cell materials, has been developed
rapidly in the past five years. Its power conversion efficiency (PCE) rises from 3.6% to more than 22.1% and its stability is also
improved. Although many studies of its fabrication methods and PCE are reported, its fundamental photoexcitation dynamics is still
unclear. To investigate its photoexcitation dynamics, conventional time-resolved analysis tools, such as time-resolved transient absorption
(TA) spectroscopy and time-resolved microwave conductivity, are often used. But they can only provide little spatial information of
PVSK. Here, we demonstrate pump-probe microscopy is able to reveal photoexcitation dynamics in PVSKs on the femto- to pico-
second timescale and also provide sub-micro spatial resolution. We investigate crystalline CH3NH3PbI3 perovskite thin layers on glass
substrates and sort the photoexcitation dynamics by different nonlinear processes it causes, such as two photon absorption, ground
state depletion and excited state absorption. We combine time-resolved photoluminescence (TRPL) images and pump-probe images to
visualize the spatial heterogeneity of photoexcitation dynamics.

30Photoacoustic Imaging of tattoo inks and evaluation of tattoo treatment

Daiwei Li, Wes Ross, Jigar Patel, Patrick Codd, Junjie Yao
Department of Biomedical Engineering, Duke University; Duke Dermatology, Duke University School of Medicine

Photoacoustic imaging (PAI), as a new imaging method, has excellent optical contrast and ultrasonic spatial resolution. PAI is being
extensively studied in clinical settings for biomedical diagnostic applications in vivo. In this poster, we illustrate the utility of PAI as
a non-invasive tattoo imaging tool and evaluate the effectiveness of laser tattoo removal therapy. We performed an extracorporeal
photoacoustic performance test on 6 different tattoo ink samples embedded in porcine skin before and after laser treatment, and we
quantitatively determined the effect of laser treatment. By comparing and quantifying tattoo skin samples before and after treatment,
we evaluated the effect of laser treatment on tattoo ink. PAI provides excellent contrast, describing the shape and area of the ink on the

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FIP 2020 Annual Symposium

skin. Our results reveal the photoacoustic properties of tattoo inks and demonstrate the potential clinical value of PAI in intradermal
tattoo imaging. PAI can be used as a preoperative clinical aid in assessing tattoos and may guide the process of laser removal treatment.

31Fiberoptics SERS Sensors using Plasmonic Nanostar Probes for Detection of
Molecular Biotargets

Vanessa Cupil-Garciaab, Pietro Strobbiaac, Yang Ranad, Bridget M. Crawfordac, Hsin-Neng Wangac, Rodolfo Zentellae, Tai-Ping
Sune, and Tuan Vo-Dinhabc
a. Fitzpatrick Institute for Photonics, Duke University, Durham, NC 27708, USA, b. Department of Chemistry, Duke University, Durham,
NC 27708, USA,
c. Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA, d. Guangdong Provincial Key Laboratory
of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou 510632, China, e.
Department of Biology, Duke University, Durham, NC 27708, USA.

Our group has integrated surface-enhanced Raman scattering (SERS) silver coated gold nanostars on an optical fiber. Fiber-based
sensors are an in situ technology that can simultaneously bring the sensor and light to the sample without disturbing the environment
during the analysis. This technology is a multi-use method that does not require complex sample preparation and washing steps. Fiber
sensors, also referred to as optrodes, enable the detection of analytes in samples that are difficult to access. Additionally, fiber-optrodes
allow for specific detection while evading background signals from non-target regions. The fiber-optrode was used to detect miRNA
and illegal food additives, such as Rhodamine B. Our group functionalized the tip of the fiber-optic with inverse molecular sentinel
nanosensors. The fiber-optic biosensor was used to detect miR156 from leaf tissue of N. benthamiana. It is desirable to develop methods
for field detection of miR156 since it is involved in the flowering of plants which is important in regulating plant growth and improving
the production of biomass. The fiber-optic sensor was capable of only binding the target miRNA even in the presence of other miRNA.
This optrode configuration is a promising candidate for remote and in-field analysis of trace chemicals and biotargets.

32High-resolution Deep Learning Approach for Reducing Limited-view and
Band-limited Artifacts in Photoacoustic Computer Tomography

Tri Vu, Yuan Zhou, Mucong Li, Hannah Humayun, and Junjie Yao
Photoacoustic Imaging Laboratory, Department of Biomedical Engineering, Duke University

Photoacoustic imaging (PAI), especially photoacoustic computed tomography (PACT), is quickly growing in clinical application. For
many of its implementations, non-full-view (linear-array, curved-array, etc.) transducers are used for both convenience and ability to
adapt to tissue surfaces. However, these probes have significant drawbacks. They lead to severely limited-view artifacts (LVA) and
distortion from the band-limited frequency-response (BLFR) of the transducer. Existing works in both hardware and software domain
are not sufficient for solving these problems, especially for in vivo data. In this paper, we propose a deep-learning-based method named
PI-Net that utilizes recent advances in generative adversarial networks to address LVA and BLFR artifacts, while improving resolution
by four times. Preliminary results in simulated, phantom, and in vivo trials represent the potential of this model’s application to in vivo
PACT reconstruction. Qualitatively and quantitatively, PI-Net significantly corrects LVA and BLFA, while increasing the resolution by
four times and the contrast-to-noise ratio by six times for in vivo data.

33High-speed, high-sensitivity diffuse correlation spectroscopy using a single-
photon avalanche diode array

Wenhui Liu, Ruobing Qian, Shiqi Xu, Pavan Chandra Konda, Mark Harfouche, Roarke W. Horstmeyer
Department of Biomedial Engineering

Diffuse correlation spectroscopy (DCS) measures rapid changes in scattered coherent light to detect blood flow and functional dynamics
within tissue. While its sensitivity to minute scatterer displacements leads to a number of unique advantages as compared to other
technologies, conventional DCS systems become photon-limited when attempting to probe deep into tissue, which leads to long
measurement windows (1 sec) and low temporal resolution. Here, we present a high-sensitivity DCS system that uses 1024 parallel
detection channels provided by a 32×32 single-photon avalanche diode (SPAD) array. We demonstrate that this new parallelized
measurement strategy is sensitive to mm-scale perturbations up to 1 cm within a tissue-like phantom at up to 100 Hz sampling rate, and
also show that it can measure high-fidelity human pulse signals from the forehead with a temporal resolution of 33~ms. These results
suggest that a highly parallelized approach to DCS can overcome standard sensitivity and temporal resolution limits to open up new
applications for measuring high-speed biological signals.

34Acetylene-bridged Organic and Organometallic Donor-Bridge-Acceptor
Systems for IR-Induced Electron Transfer Modulation

Jesús Valdiviezo1, Xiao Li4, Susannah D. Bazinger5, Peng Zhang1, Igor V. Rubtsov4, Tong Ren5, David N. Beratan1,2,3
1. Department of Chemistry, Duke University, Durham, NC, United States.
2. Department of Physics, Duke University, Durham, NC, United States.
3. Department of Biochemistry, Duke University, Durham, NC, United States.
4. Department of Chemistry, Tulane University, New Orleans, LA, United States.

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5. Department of Chemistry, Purdue University, West Lafayette, IN, United States.

Photoacoustic imaging (PAI), especially photoacoustic computed tomography (PACT), is quickly growing in clinical application. For
many of its implementations, non-full-view (linear-array, curved-array, etc.) transducers are used for both convenience and ability
to adapt to tissue surfaces. However, these probes have significant drawbacks. They lead to severely limited-view artifacts (LVA)
and distortion from the band-limited frequency-response (BLFR) The efficiency of opto-electronic devices strongly depends on the
photoinduced charge separation and charge recombination rates. Current strategies to control electron transfer kinetics have reported to
be chemically invasive or irreversible. In recent years, UV-IR-Vis-3-pulse experiments have indicated that IR perturbations of a specific
molecular vibrational mode can influence photoinduced electron transfer rates in donor-bridge acceptor (D-B-A) assemblies. The
possibility to use infrared excitations to control electron transfer is of substantial interest, since it is a nondestructive method (the energy
absorbed ranges between 2.5-8.5 kcal/mol), spatially selective (specific bonds can be excited) and infrared active functional groups
can be easily incorporated into molecular architectures. Through a collaborative effort between experiment and theory, we combined
synthesis, computational methods and ultrafast spectroscopy techniques to investigate the evolution of excited states in organic and
organometallic D-B-A complexes. Under the presence of strong donors, the optical excitation of the acceptor leads to ultrafast formation
of charge separated states (CSS) These fast dynamics makes them excellent candidates for electron transfer rate modulation by infra-red
excitation. Moreover, it is found that the excitation of the CC stretching mode of the bridge slows the rate of CSS formation, providing
evidence of the possibility to use IR pulses to modulate the electron transfer rate in compounds without rare-earth metals.

35Enhancing Methanol Production by Photothermal Desorption in Solid-Gas
Phase CO2 Hydrogenation

Siyuan Zhu, Peter Novello, Shi He and Jie Liu
Department of Chemistry, Duke University

Currently, burning the fossil fuels all over the world causes excessive emission of CO2 accelerating climate change and causing
detrimental harm to the environment. The capture of CO2 and converting it into a high-value fuel has the potential to close the carbon
cycle and mitigate harm to the climate in the future. Methanol is widely used in the chemical industry for producing chemicals, such as
olefins, formaldehyde, methyl tert-butyl ether, and acetic acid. The reaction which produces methanol by CO2 hydrogenation favors low
temperature and high pressure. Here, we report that using indium oxide as catalyst for CO2 methanolation reaction under a pulsed light
from blue LED. The rapid temperature change due to photothermal effect increases methanol production with pulsed light by desorbing
methanol from the catalyst. By using catalyst’s photothermal effect, the consideration of pulse light has the potential to increase catalyst
activity with minimal energy input.

36Identifying metastatic melanoma early – a novel approach with pump-probe
microscopy

David Grass, Xiaomeng Xia, Martin Fischer, Warren Warren
Department of Chemistry, Duke University

More people die from melanoma after a Stage I (local melanoma) diagnosis than after a Stage IV (distant metastatic disease) diagnosis,
because the tools available to clinicians do not readily identify which early-stage cancers will be aggressive. We pursuit an alternative
approach, complementary to conventional histopathological diagnosis and lymph node biopsy, based on pump-probe microscopy.
Melanocytes, the cells becoming cancerous in melanoma, produce a brown to black colored pigment melanin. Transient absorption
analysis of the electronic and vibrational structure of melanin with ultrashort laser pulses reveals, for example, its state of aggregation.
We identify differences between the aggregation state of localized melanoma (stage I and II) and melanoma that metastasized (stage
III and IV) and can, in principle, tell them apart solely by inspecting the primary tumor. Here we present a detailed analysis of such
measurements, its application in the context of melanoma and our efforts on translation of pump-probe microscopy into clinics.

37Monte Carlo simulation of blood vasculature’s effect on optical fluence
distribution in mouse brain

Yuqi Tang, Junjie Yao
Duke University Department of Biomedical Engineering

Developing deep brain imaging techniques to understand brain functions is of great interest for medical imaging field. Photoacoustic
computed tomography (PACT) uses diffused light as excitation for chromophores and the generated acoustic waves is acquired by the
ultrasonic transducer for subsequent image reconstruction. Due to its relative deep penetration (several centimeters in tissue), PACT has
been gradually used in mouse brain imaging for both structural and functional studies. In conventional PACT reconstruction algorithms
such as delay-and-sum, fluence distribution within the target is assumed to be homogeneous. Other model-based reconstruction methods
use simple model, such as a low order polynomial or a small set of sine and cosine functions, for optical fluence estimation. Either
approach can result in an inaccurate estimation of optical fluence, leading to a reconstructed image that has undesired artefacts and low
image quality. In this work, we use Monte Carlo simulation to estimate the optical fluence distribution within the mouse brain. MCX
Monte Carlo simulation package and a digimouse model with basic brain anatomy information are used. We also extract mouse blood
vasculature information from a two-photon microscopy image of mouse brain and merge it with digimouse model. The effect of blood
vasculature’s effect on optical fluence distribution is also studied. We intend to use this simulation to provide a more precise map for
future PACT image reconstruction.

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38Improving SNR of Photoacoustic Imaging Using a Matched Filter

Xiaorui Peng, Daiwei Li, Dr. Junjie Yao
Department of Biomedical Engineering, Duke University

Matched filtering is widely used in digital communications, radar and sonar detection, as well as gravitational-wave astronomy, in order
to improve SNR substantially. In our method, we cross-correlate the template signal of the system’s impulse response with the raw
photoacoustic signals, which can suppress the uncorrelated detection noise and signal interference and thus improve the SNR of the PA
images. We obtained the impulse response of our photoacoustic imaging system and validated our method on the mouse ear vascular
image. Our results have demonstrated SNR improvements in PA imaging by rejecting strong background noises.

39High-speed widefield photoacoustic microscopy of small-animal
hemodynamics

Xiaoyi Zhu and Junjie Yao
Department of Biomedical Engineering, Duke University, Duke University

The optical microscopes has been widely used for studying tissue structure and their functional interactions. However, in hemodynamic
studies, many optical microscopes have their own limitations, such as two-photon microscopy (TPM) suffers from slow measurement of
blood oxygenation and wide-field optical microscopy lacks depth resolution. The emerging optical-resolution photoacoustic microscopy
(OR-PAM) has gained much attention in hemodynamic studies. It detects ultrasonic signals after the tissue absorbs photon energy, is
highly sensitive to the optical absorption. As the major light absorber under certain wavelengths, blood vessels have been extensively
imaged by PAM for a number of hemodynamic parameters, such as total hemoglobin concentration, oxygen saturation, blood flow speed
and the metabolic rate of oxygen. Many life activities change quickly, it is desired to capture dynamics with both fast scanning speed
and large scanning range. Recently, many efforts have been invested in improving imaging speed, such as galvanometer scanners and
MEMS (microelectromechanical system) scanning mirror. Here we present a novel OR-PAM system with both high scanning speed
and ultra-wide field of view. In this system, laser is scanned when a polygon mirror is rotating driven by a high precision DC motor.
It has achieved an image rate of 0.5Hz over a 10mm×10mm scanning range with resolution of 10μm. Besides the in vivo structure
imaging of the mouse ear, we also successfully in vivo captured the functional dynamics in mouse cortex-the vasoconstriction induced
by epinephrine. The imaging results demonstrate that the system will be a powerful tool for capturing rapid hemodynamic response
over a large field of view, and it has an abundance of future applications in biomedical imaging about all kinds of diseases studying.

40Surface-Enhanced Raman Scattering of Antibody Functionalized Gold
Nanoparticles for the Detection of Cardiovascular Biomarkers In Vitro

Kristen Dellinger1, Jean-Francois Tanguay2, Maryam Tabrizian3, Marinella Sandros 4
1 North Carolina A&T State University
2 Montreal Heart Institute
3 McGill University
4 University of North Carolina at Greensboro

Accurate detection and localization of inflammatory biomarkers, such as vascular adhesion molecule-1 (VCAM-1), is crucial for early
diagnosis and prevention of atherosclerotic disease. Surface-enhanced Raman scattering (SERS) is an analytical tool well suited for
this goal, since it can provide concurrent target identification and imaging when combined with confocal microscopy. Most SERS
nanoprobes are synthesized via conventional adsorption; however this approach is often lengthy and can result in particulate instability
and aggregation. In the present work, we report the construction and characterization of a robust SERS nanoprobe with strong scattering
signal via microwave technology and suitable stability to support antibody functionalization. The nanoprobe was successfully employed
to map the expression of VCAM-1 in human coronary artery endothelial (HCAE) cells. Results demonstrated that microwave technology
is a viable option for the construction.

41Plasmonic nanoprobes for multimodal sensing and bioimaging of microRNA
in plants

Pietro Strobbia, BridgetM.Crawford,Hsin-NengWang,RodolfoZentella, MaximI.Boyanov, Tai-PingSun, KennethM.Kemner, TuanVo-Dinh
Fitzpatrick Institute for Photonics, Duke University
Department of Biomedical Engineering, Duke University
Department of Biology, Duke University
Department of Chemistry, Duke University
Biosciences Division, Argonne National Laboratory

Monitoring gene expression within whole plants is critical for many applications ranging from plant biology to agricultural biotechnology

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and biofuel development;  however, no method currently exists for  in  vivo  monitoring of genomic targets in plant  systems.  We
developed a unique  multimodal method based on plasmonic nanoprobes  capable of  in vivo  imaging and  biosensing of microRNA
biotargets within whole plant leaves by integrating three different and complementary techniques: surface-enhanced Raman scattering
(SERS), X-ray fluorescence (XRF), and plasmonics-enhanced two-photon luminescence (TPL). The method developed uses plasmonic
nanostars, which  not only provide large Raman signal  enhancement, but also allow for localization  and quantification by XRF and
plasmonics-enhanced TPL, owing to gold content and high two-photon luminescence cross-sections. Our method uses inverse molecular
sentinel (iMS) nanoprobes for SERS bioimaging of microRNA within Arabidopsis thaliana leaves to provide a dynamic SERS map of
detected microRNA targets while also quantifying nanoprobe concentrations using XRF and TPL. The nanoprobes were observed to
occupy the intercellular spaces upon infiltration into the leaf tissues. This report lays the foundation for the use of plasmonic nanoprobes
for  in vivo  functional imaging of nucleic  acid biotargets in whole plants, a tool that will revolutionize bioengineering  research by
allowing the study of these biotargets with previously unmet spatial and temporal resolution.

“Lightning Talks” - Tuesday, March 10th 10:00am
organized by the Duke Optical Student Chapter

A few of these posters will be selected to present
their research during the Lightning Talks session.

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FIP 2020 Annual Symposium

FIP Departments and Programs

The Fitzpatrick Institute for Photonics is an extremely interdisciplinary Duke effort to
advance photonics and optical sciences. The institute leverages Duke’s faculty from the
Pratt School of Engineering, Trinity Arts and Sciences, and the Duke Medical School to
explore problems at the boundary nexus of nano-bio-info-opto convergence.

The Fitzpatrick Institute for Photonics (FIP) has 150 Faculty Members from 39

Participainting Departments, Centers & Institutions at Duke University.

FIP Departments

Anesthesiology Ophthalmology
Art, Art History & Visual Studies Orthopaedic Engineering
Biology Pathology
Biomedical Engineering (BME) Pediatrics
Cell Biology Philosophy
Chemical Biology Physics
Chemistry Radiation Oncology
Civil & Environmental Engineering (CEE) Radiology
Computer Science Surgery
Dermatology Urology
Electrical and Computer Engineering (ECE) Center for Applied Genomics and Precision
Gastroenterology Medicine
Geriatrics Center for Genomic and Computational Biology
Literature (GCB)
Mathematics Center for Metamaterials & Integrated
Mechanical Engineering and Materials Science Plasmonics (CMIP)
(MEMS) Division of Infectious Diseases & International
Molecular Genetics and Microbiology Health
Neurobiology Duke Comprehensive Cancer Center
Neurosurgery Duke Human Vaccine Institute (DHVI)
Obstetrics and Gynecology Duke Immersive Virtual Environment (DiVE)
Oncology Nicholas School of the Environment

FIP Research Programs and Directors

Biophotonics – Joseph Izatt
Nano/Micro Systems – Nan Jokerst
Quantum Optics and Information Photonics – Jungsang Kim
Photonic Materials & Advanced Photonic Systems - Steven Cummer & Charles Gersbach
Nanophotonics – Fan Yuan
Systems Modeling, Theory & Data Treatment – Weitao Yang
Novel Spectroscopies – Warren Warren

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