I am a fifth year PhD student at MIT CSAIL and LIDS advised by Stefanie Jegelka and Suvrit Sra. I completed my undergraduate studies in mathematics at the University of Warwick where I worked with Robert MacKay on probability theory. I spent summer 2022 at Google Research working with Dilip Krishnan.

I am interested in learning and structuring representation spaces. In the process I have worked on self-supervised learning and learning on graphs.

News

• Our Sign and Basis networks for learning graph positional encodings is accepted at ICLR 2023 with a spotlight!
• Excited to share new paper on a new scalable contrastive masked autoencoder!
• New NeurIPS 2022 paper on neural network friendly extensions of non-differentiable set functions!
• Highlighted reviewer at ICLR 2022.
• I am helping organize the LoG Conference, a new conference specifically dedicated to graph machine learning!
• MIT news wrote a story about our NeurIPS 2021 paper on shortcut learning.
• Paper on the use of shortcuts in contrastive learning accepted at NeurIPS 2021.
• reviewer award (top 10% of reviewers) at ICML 2021.
• Awarded the runner-up prize for the 2021 Two Sigma PhD fellowship.
• Paper on contrastive learning with hard negative samples accepted at ICLR 2021!
• Reviewer award (top 10% of reviewers) at NeurIPS 2020.
• Paper on debiased contrastive learning accepted at NeurIPS 2020 with a spotlight!
• Paper on optimal minibatch selection at the ICML 2020 Workshop on Real World Experiment Design and Active Learning.
• Spent the summer 2020 at Amazon Web Services working as an Applied Scientist.
• Paper on understanding theoretical foundations of pretraining embeddings using weak supervision accepted at ICML 2020!
• Paper on a new probabilistic model for diversity accepted at NeurIPS 2019.

Core review service: NeurIPS, ICLR, ICML, JMLR

Other review service: COLT, CVPR, ECCV, TMLR

Teaching: 6.867 machine learning (graduate level) – MIT fall 2021