am therapy to manage pain in postoperative patients.

 am applying to the PhD program in Biomedical Engineering at Yale University with a
focus on biomedical imaging. Specifically, I am interested in investigating the interface
between quantitative magnetic resonance imaging (MRI) and biomechanics, as well as
clinical applications of machine learning toward building diagnostic tools. I have a strong
research background with two years of experience in molecular imaging and
publications in other relevant areas.
I earned my BS in Bioengineering from UC Berkeley where I gained an appreciation for
engineering design and computational modeling and how they can accelerate
innovation. For example, as a research assistant in the lab of Professor Richard
Mathies, I aimed to optimize binding kinetics of microfluidic chip-based diagnostics.
Using COMSOL Multiphysics, I developed scalable finite-element models to structure
parametric boundaries of a novel serpentine lab-on-a-chip design. These simulations
proved invaluable when I fabricated the microfluidic devices and evaluated the
diagnostic capabilities of the design in vitro. Likewise, for my senior capstone project, I
used AutoCAD and SolidWorks modeling software to a similar effect. Our group’s
objective was to reduce the prohibitively high rate of catheter-related infection by
investigating the interaction between electric fields and antimicrobial agents. We utilized
the models I created to manipulate voltages and environmental characteristics, guiding
our prototype design and saving valuable educational funds in the process.
I have been able to translate my engineering knowledge into concrete deliverables in
my years of postgraduate work. At the Virtual Reality Medical Center (VRMC), a small
research institute specialized in cyber-therapy and human-computer interactions, I
independently led several data collection and analysis efforts. One of the more
significant projects investigated the effectiveness of virtual distraction therapy to
manage pain in postoperative patients. I correlated physiological and Likert-scale pain
data obtained from sixty-seven patients to ultimately suggest VR therapy as an effective
supplement to conventional opioid routes12. These results invigorated me to extend
our efforts by performing a methodological case study on the use of haptics technology
to increase immersion into the virtual simulations3. The autonomy I experienced in this
group was particularly edifying in my becoming a productive researcher.
My circuitous path has eventually led me back to the Bay Area where I am currently
employed at the UCSF Department of Radiology and Biomedical Imaging. Part of my
attraction to this position derives from the first PET/MRI dual-modality imaging system
by General Electric, validated at UCSF. The ability to combine functional molecular
imaging of Positron Emission Tomography (PET) with superior soft tissue analysis of
MRI opens the doors for various clinical applications. As I collaborated with imaging
specialists, radiologists, and nuclear physicists, I developed a niche for myself in
exploring novel uses of this hybrid system by managing clinical trials and supporting
investigators in realizing these research efforts456. A project particularly meaningful to
me investigates 68Ga-citrate as a molecular imaging agent that can detect actionable
oncogenic drivers for the staging of glioblastoma. For the patients that I enroll to the trial,
the possibility of finding molecular determinants of tumor differentiation in their
heterogeneous disease is clinically significant and spiritually uplifting. Witnessing the  implications of these technologies, I am personally motivated to see the success of
these studies. As I reflect upon my research direction, I intend to contribute to the
growing knowledge-base of quantitative imaging. Multiparametric MRI is especially
interesting to me for its flexibility and the substantial capacity for development of new
acquisition techniques. Eventually, with more autonomy in my creative work, I hope to
build computational models and algorithms that can utilize these parameters to better
characterize clinical disorders.
Though my time at UCSF has presented continuous opportunities to learn, it has also
allowed me to play an advisory role for my job function. As a senior member of our
department’s Clinical Research Coordinator (CRC) group, I provide counsel in aspects
of regulatory practice and clinical trials to research support staff in Radiology. Beyond
our department, I am a member of the UCSF CRC Council and certified as a Clinical
Research Professional (CCRP). Naturally, collaboration and teaching are pillars for
success in research. I hope to exemplify this harmony of active and observant learning
with synergistic collaboration in my growing career, especially as a PhD candidate.
There are many faculty members in this department that I would be excited to work with;
however, three in particular are especially inspiring: (1) Drs. Fahmeed Hyder, and his
work in magnetic resonance spectroscopy and functional MRI, (2) Lawrence Staib, and
his advancement of image segmentation for machine learning, and (3) Douglas
Rothman, for his development of MRI methods for neuroimaging. Realizing the
significance of their efforts and my capabilities to positively contribute to studies here, I
am confident that I would prove to be a prolific PhD candidate at the Yale graduate
program in Biomedical Engineering.


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