Greetings! I am a 3rd year Computer Science PhD student at MIT fortunate enough to be advised by Professors Adrian Dalca and John Guttag as a part of the Clinical and Applied Machine Learning (CAML) Group. I am interested in problems lying at the core of machine learning, computer vision, and statistical inference, often with applications to healthcare. My work is supported by the NSF Graduate Research Fellowship.

Previously, I studied Computer Science at Cornell University advised by Mert Sabuncu, where I was a Merril Presidential Scholar for the Computer Science department and a Tanner-Dean Scholar for the School of Arts and Sciences.

My current focus is on solving traditional machine learning problems by re-thinking neural network inference. Here are some directions I am excited about:

• In-Context Adaptation: How can we improve (and understand) neural networks' ability to meta-solve tasks at inference, without additional training?
• Controllable Generation: How can we guide powerful models to generate in a controlled way, choosing what styles or objects are included/excluded? What mechanisms can we create that allow for consistency in generated outputs?
• Applied Uncertainty Quantification: How can we extend theoretical insights from statistics, such as calibration and proper scoring rules, derived in simple (x,y) classification settings, to a broad class of high-dimensional and structured machine learning problems, in a way that quantifiably improves downstream outcomes?

If you are interested in collaborating, please do reach out!