About me

Where I Am Now

I am a MD candidate at Harvard Medical School, where I am interested in applying my computational background to healthcare challenges and designing new ways of investigating, diagnosing, and treating disease.

PhD Work

I earned my PhD from the department of Electrical Engineering at Columbia University, where I was an NSF Graduate Research Fellow. I conducted research in the Neural Acoustic Processing Lab, under the supervision of Dr. Nima Mesgarani. My work was at the intersection of neuroscience, machine learning, and biology. Specifically, I worked on using deep neural networks to model the representations of language and sound in the auditory cortex with the goal of understanding how context influences how different types of sounds are processed. My dissertation was titled Understanding Contextual and Hierarchical Auditory Processing in the Human Brain through Deep Neural Networks.

Undergraduate Experience

In 2020, I graduated from Johns Hopkins University with a B.S. in Biomedical Engineering, where I took part in a number of research projects (see my Publications and Projects pages for more details). I helped co-develop mvlearn, the first major open-source Python package for multiview machine learning. I worked on a spike inference algorithm based on FRI theory with Dr. Benjamín Béjar. I also conducted research in neuroscience under Dr. Kathleen Cullen analyzing the movements of vestibular schwannoma patients to understand the effects of neurectomy and neural compensation mechanism.

At JHU, I was also heavily involved in biomedical design teams, and I am very proud of the progress my teams made toward building products that can help people in the real world. OtoGlobal Health, a startup that I led for a little over a year, has developed a low-cost hearing screening device for low-resource settings. I also developed a low-cost device to treat postpartum hemorrhage in low-resource settings, (details here).