About Me

I’m a first-year PhD student at MIT CSAIL, advised by Prof. Sara Beery! I am grateful to be supported by the MIT Jameel Clinic Fellowship and the NSF GRFP.

Previously, I was a master’s student at MIT CSAIL in the HealthyML Lab advised by Prof. Marzyeh Ghassemi, where I worked on detecting & mitigating distribution shift. Last summer, I was a visiting student at ETH Zurich advised by Prof. Fanny Yang, where I worked on optimal transport and differential privacy. I also attended MIT for undergrad and double majored in computer science and mathematics (Course 6 & 18) with a concentration in Ancient and Medieval Studies. I’ve had the pleasure to intern at Microsoft Research (x3) and Apple Research.

I’m also on Twitter and Google Scholar. Feel free to contact me at nhulkund@mit.edu!

Research Interests

I’m interested in building reliable machine learning systems, balancing tradeoffs of performance, efficiency, privacy, and robustness. Some current questions I am thinking about:

Working Papers

  1. Challenges and Pitfalls of Geopolitical Data Sharing Policy for Ecological Data
    Neha Hulkund, Millie Chapman, Ruth Oliver, Sara Beery
    Under Review

Publications

  1. Privacy-preserving data release leveraging optimal transport and particle gradient descent
    Konstantin Donhauser, Javier Abad, Neha Hulkund, Fanny Yang
    ICLR Privacy Regulation and Protection in ML Workshop, Under Review at ICML
    arxiv

  2. Predicting Out-of-Domain Generalization with Local Manifold Smoothness
    Nathan Ng, Neha Hulkund, Kyunghyun Cho, Marzyeh Ghassemi
    TMLR
    arxiv

  3. Interpretable Distribution Shift Detection using Optimal Transport
    Neha Hulkund, Nicolo Fusi, Jennifer Wortman Vaughan, David Alvarez-Melis
    Presented at ICML 2022 DataPerf Workshop
    arxiv

  4. GAN-based Data Augmentation for Chest X-ray Classification
    Shobhita Sundaram*, Neha Hulkund* (equal contribution)
    Spotlight presentation at KDD 2021 DSHealth Workshop
    arxiv

  5. The Limits of Algorithmic Stability for Robustness to Distribution Shift
    Neha Hulkund, Vinith Suriyakumar, Taylor Killian, Marzyeh Ghassemi
    Presented at NeurIPS 2022 Women in Machine Learning Workshop
    pdf poster

Class Projects

  1. Facilitating Fairness through Distributionally Robust Finetuning
    Final Project for MIT class 6.864: Natural Language Processing
    pdf

  2. Extension of a Bayesian Hierarchical Model for Moral Judgments
    Final Project for MIT class 6.804: Computational Cognitive Science
    pdf

  3. Minimum Degree of Generating Matrices
    Final Project for MIT class 18.821: Seminar in Mathematical Research
    pdf

  4. Methods for Clinical Time Series Analysis in Pediatrics
    Final Project for MIT class 6.871: Machine Learning in Healthcare
    pdf

Other

In my free time, I enjoy spending my time outdoors hiking/biking/sailing/kayaking in the Pacific Northwest and (most recently) Switzerland.


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