Welcome!

I’m a fifth-year PhD student in Prof. Justin Solomon’s Geometric Data Processing Group at the MIT Computer Science and Artificial Intelligence Lab. I work on machine learning problems where scale fails: Where naively scaling up training data, model size, or the number of training iterations is either impossible, fails to yield the promised improvements, or even leads to unexpected – and unwanted – results.

I’m grateful to be supported by a 2024 Exponent Fellowship and an Amazon Research Award. I’ve also been supported by a 2022 Siebel Scholarship and an NSERC Postgraduate Doctoral Scholarship (PGS-D).

I spent Summer 2024 interning with Dr. Paul Zhang at Backflip AI, a 3D generative AI startup based in San Francisco. We developed a state-of-the-art method for orienting 3D shapes, which you can read all about in Orient Anything.

I spent Summer 2021 and 2022 interning with Prof. Michael Bronstein and the Learning Methods team at Twitter Cortex!

I completed my undergrad degree in mathematics and economics at McGill University in Montreal. At McGill, I had the privilege of working with:

• Profs. Rustum Choksi and Tim Hoheisel, with whom I helped develop a state-of-the-art algorithm for QR code deblurring.
• Prof. Adrian Vetta, with whom I worked on efficient approximation schemes for fair chore division and scheduling problems.
• Prof. Prakash Panangaden, with whom I worked on efficiently computable approximations to optimal transport distances.

Check out my research page to learn more about my active projects.

Check out my publications page to learn more about past projects that I’ve worked on.