This statement is to discuss the research interests and career goals which have led me
to apply to Yale’s Ph.D. program in Biomedical Engineering. It is also intended to provide
a brief description of my background with a focus on what I have learned and how it has
prepared me to succeed as a doctoral candidate.
Shortly after joining Northeastern University Department of Electrical and Computer
Engineering, I met Professor Carey Rappaport who has been my most influential mentor.
At our first meeting he asked me if I wanted to work on a breast cancer detection system
which minimizes harmful ionizing radiation and increases image contrast using both XRay
transmission imaging (CT) and radar-like microwave tomography (MWT). I happily
agreed to join his research team. My on-campus research progressed from informal lessons
on the blackboard to the implementation of wave propagation simulations. I published my
first paper in my second year, and by my third year, I had the unique opportunity as an
undergraduate to play a team leadership role. In my fifth year, along with a team of my
senior capstone classmates, I built a successful proof of concept for MWT breast cancer
detection. My research has been the central pillar of my undergraduate education. I thrived
in the creative freedom facilitated by my colleagues and advisers.
I had the option to graduate in four years, but I chose, like most Northeastern students,
to participate in a five-year program so that I could take part in three six-month full-time
work experiences, or co-ops. This also allowed me to make additional research contributions
and to complete a combined major in Physics.
My first co-op was at a research firm called Radiation Monitoring Devices where I was
part of a research group developing a low-cost hemodynamic monitor using Diffuse Correlation
Spectroscopy with near-infrared coherent sources. For a sophomore student with little
experience, this was a crash course in biomedical optics, computer architecture, and digital
signal processing. When I ran into an unknown topic, I read about it in scientific articles and
textbooks. This experience taught me to let a research project drive my learning process,
rather than the other way around. In the end, I published results in a peer-reviewed scientific
journal article of my own.
My second co-op was at Laboratoire des Signaux et Systemes (L2S) which was part of
a university called Supelec near Paris, France. My co-workers were researchers from around
the globe. At Supelec, I investigated the potential for data fusion between CT and MWT
measurements. In addition to peer-reviewed publications, I wrote a lengthy report describing
my findings. Going through such a rigorous academic experience was formative for me as a
researcher. It taught me to dig deep into the physics and mathematics of a topic even when
it seems daunting and it gave me a new sense of confidence, resilience, and a drive for a
future career in research. I am grateful to have been a part of such a diverse and intelligent
group of scientists who supported me and pushed me to succeed.
For my third co-op, I wanted to learn more about practical applications, so I accepted
a position as an Apprentice Imaging Engineer at Photo Diagnostic Systems, Inc. (PDSI), a
contract engineering and manufacturing firm specializing in Positron Emission Tomography
(PET) and CT scanners. My first task was to help build a CT scanner from the ground
up. After that, my primary responsibility was to conduct physical experiments and to
design and implement image reconstruction algorithms. I was hired for a one-year term after
graduation as a full-time Imaging Engineer where I am developing a statistically-weighted
iterative reconstruction platform for an image-guided radiation therapy system. My professional and academic experiences have given me a foundation in physical sciences
including optics, x-ray/high-energy physics, and electromagnetic fields and waves. I
am interested in learning more about magnetic resonance and acoustics. Regarding mathematical
tools, I am well-trained in inverse problems, Bayesian methods, Fourier analysis,
and numerical optimization, and I am interested in learning more about machine learning
and pattern recognition.
As for my future research interests within medical image reconstruction and processing,
I am particularly interested in making better images by finding useful information in new
places. As an example, my proposed research plan for my application to the NSF Graduate
Research Fellowship Program is a maximum a posteriori iterative reconstruction algorithm
that maximizes a joint likelihood function based on both the statistical distribution of the
measurements and a novel “genomic likelihood” objective function. The goal would be to
use machine learning to extract some information from a patient’s genetic profile that could
be useful for image reconstruction.
I am choosing to pursue a doctoral degree because I want to go beyond solving difficult
technical problems. I want to be the one asking difficult questions and posing new challenges.
In the future, I see myself as a professor at a university, a researcher at a hospital, or a research
engineer at a company in the medical imaging industry. In any case, my professional ambition
is to become a genuine thought leader in the field of medical imaging and image analysis. It
is important to me to find a program which will train me to be a leader as well as a scientist.
After meeting with James Duncan, I am very interested in the Image Processing and
Analysis Group. I understand that this is a slight deviation from my background in image
reconstruction, but they are working with the same applications and the same mathematical
tools that I am interested in. I would be happy to make the transition to image analysis. I
am also interested in the work taking place at the PET Center and I think my background
at PDSI would make me an excellent fit.
Yale has a reputation of preparing students to be leaders in their fields. This is exactly
what I am looking for. I look forward to hearing about opportunities to join Yale’s Biomedical
Engineering research community.