I’m a final year PhD student in the Machine Learning Department at Carnegie Mellon University advised by Professor Virginia Smith. My primary research interest is in principled machine learning methodologies (algorithm design). A major part of my PhD research focuses on gradient estimation techniques under various practical constraints. I’ve also worked on automated machine learning (AutoML) and more specifically meta-learning in grad school.

Recently I had a great time working as a Quantitative Research Intern in a machine learning team at Cubist Systematic Strategies.

In the past, I have

• worked as a student researcher in the Vizier team at Google DeepMind.
• interned with the Custom Labels team in Amazon Web Services (Rekognition)
• interned with the Applied Machine Learning team (Bellevue, WA) at ByteDance.

Before my PhD, I graduated Summa Cum Laude with double majors in Math and Computer Science from Duke University where I worked with Professor Cynthia Rudin on interpretable deep learning.