Vinith Suriyakumar

MIT EECS, CSAIL, LIDS, IMES

I am an incoming PhD student at MIT EECS affiliated with CSAIL, LIDS, and IMES where I will be advised by Dr. Ashia Wilson and Dr. Marzyeh Ghassemi. My research focuses on fundamental questions of trustworthy machine learning. Currently, I focus on the theory and practice of differential privacy and algorithmic fairness in machine learning. I am also interested in optimization and learning theory. I recently completed a research internship at Google Research working with Dr. Om Thakkar and Swaroop Ramaswamy on differentially private federated learning.

I completed my Masters in May 2021 at the University of Toronto & Vector Institute advised by Dr. Marzyeh Ghassemi, Dr. Nicolas Papernot, Dr. Berk Ustun, and Dr. Anna Goldenberg. My thesis was focused on differential privacy and algorithmic fairness in machine learning for healthcare.

Current Projects

  1. Differential Privacy and Distributional Robustness
  2. Fair Personalization in Machine Learning for Healthcare
  3. Adaptive Gradient Boosting Machines

news

Mar 29, 2021 I’m excited to announce that I’ve accepted an offer to join the PhD program starting Fall 2021 at MIT EECS where I will be affiliated with CSAIL, LIDS, and IMES. I will be co-advised by Dr. Ashia Wilson and Dr. Marzyeh Ghassemi. I will continue my research focusing on the methodological and theoretical foundations of machine learning, optimization, sampling, differential privacy, and algorithmic fairness.
Feb 19, 2021 I’m excited to announce that I’ve accepted an offer to join Google this summer as a Research Intern working with Dr. Om Thakkar on differentially private federated learning!
Jan 4, 2021 I’ll be giving a long talk on the “Challenges of Differentially Private Prediction in Health Care” at the IJCAI 2021 AI for Social Good Workshop organized by Harvard CRCS on January 7th during the 8:10-9:10 PM EST session. A longer version of this study was recently accepted for publication at ACM FAccT 2021.
Dec 17, 2020 I had two papers accepted at ACM FAccT 2021: Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings and “Can You Fake It Until You Make It?: Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness” (preprint forthcoming)!
Oct 15, 2020 Our preprint Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings is up! I’ll be giving a talk virtually on our work at the University of Toronto, Centre for Ethics on October 28, 2020 from 4:00 - 5:00 PM EST.