I example of the Engineering Design process and

I feel an urge to find fundamental patterns, comprehend emergence and explain lack of symmetry. We
were learning about a converging series in MATH 0190, and it occurred to me that it should have a
diverging counterpart. I brought it up with my professor after class, who became excited to google what
others had thought about the idea. There is also the example of the Engineering Design process and the
Scientific method introduced in my capstone class. I needed to understand why they were so different,
which eventually led to me discover the underlying dual components of planning and research, and
discover how the two processes were similarly yet distinctively founded on these building blocks.
The need to understand and discover was also a theme with the data processing algorithms I
investigated in my OCT research. Once I gained a mental facility with them I changed the algorithms into
what seemed to be a more logical sequence. This led me to discover that the original sequence had a
major flaw, one that my professor and I verified though numerical simulations.
It is amazing how there can be so much emergence, so much sophistication and so much theory simply
out of different permutations of physical principles, be it understanding fluid turbulence, electrical
circuits or the myriad of fracture modes and material properties of a single material! Imaging is no
different. It is a very, very rich field. From learning about aperture sizes, shutter speeds and ISO speeds,
to understanding there can be different resolutions across an image and along different axes, to using
Fourier transforms in an amazingly elegant Optical Coherence Tomography design, I know that imaging
is a very exciting field to work in.
I have had to work with large amounts of data in my research, and I have coded late into the night
making sure my programs were executing smoothly. If I returned to my code after a while I became
disoriented at first, but my commitment to organization paid off—for I always found it easy to pick up
the threads again and find my bearings.
My professor suggested me to work with MRI technologies. He noted that MRI researchers work with
large datasets, and that it is the kind of work I would find appealing. He admitted that it would require
me to learn some theory to be able to engage with the technology—but to me, that challenge, or at the
very least adventure, is all part of the excitement. I am excited by the beautiful physics of MRI imaging. I
enjoy math, and I believe that undertaking basic MRI research, navigating the data and algorithms and
finding my way would be a wonderful experience.
In fact, my research in the Lee Lab was initially meant to focus on integrating OCT imaging at different
scales. Part of this project was to optimize the Doppler OCT parameters, which I attempted to do by
running the code for various possible permutations. Investigation of this data led me to notice a bias in
one of the component algorithms. I visualized results from many simulations, which gave me an idea for
correcting the bias. I took the initiative to implement it, and was soon pacing up and down, brows
furrowed, trying to brainstorm ideas for overcoming the challenges with my professor. It did not seem
right that there should be any bias, and I wanted to correct it as much as possible. I have demonstrated
the efficacy of the new algorithm though numerical simulations, and we plan to test it on biological data
next. I have been the principle investigator throughout the process.
I like to think through and digest ideas, to gain a mental facility with them and to organize them. These
traits are part of what have enabled me to, for instance, effectively explain theoretical concepts that I
was engaging with in the Lee Lab. I organized my presentation explaining the principles of Doppler
Optical Coherence Tomography with a pedagogical approach which impressed my professor. He noted that my insight, understanding and elucidation of the autocorrelation function in another one of my
presentations was the best he had ever seen. I feel that teaching and pedagogy is an important part of
the academic experience—be it at a symposium, in a presentation, or as a course TA. It is the other side
of learning, and an important part of scholarship that I would be happy to improve upon and engage in.
Through my own explorations, experiences and even my time at ILURS, I have come to better
understand what research entails. I can attest to the fact that research can be difficult, yet the solutions
they lead to can be elegantly simple. I can attest to the fact that research is not always fruitful, but that
effort and dedication can go a very long way. Success demands sincerity, dedication needs
determination, and commitment needs concentration, no matter what hurdles may appear in your way.
In fact, one of my current hobbies is to look for ways to more efficiently utilize unstructured time—be it
through highlighting progress in a seemingly dry pile of tasks, gamification, or through regulation of
competing demands and interests on my time. I have come to enjoy challenges—through math clubs,
competitions, research, even learning to swim—and my experiences have helped me appreciate both
the rigors and rewards of a graduate experience.
I have recently learned about the NSF GRF and am planning to apply for it next year.
I believe that a doctoral program will give me the opportunity to engage with the things that excite and
interest me, and will enable me to take myself on a path of discovery and fruitful intellectual exchange. I
hope to continue as a researcher beyond grad school, quite possibly as a postdoc on an academic path.