Vinith Suriyakumar

MIT EECS, CSAIL, LIDS, IMES, Kaiser Permanente

I am a third year PhD student at MIT EECS affiliated with CSAIL, LIDS, and IMES where I am advised by Dr. Ashia Wilson and Dr. Marzyeh Ghassemi. My research focuses on the intersection of machine learning, statistical inference, and society. I am interested in building theoretically principled algorithms to address social concerns about the use of machine learning. Currently, I’m thinking about issues surrounding copyright and attribution, auditing and the fairness of algorithmic decision making. Additionally, I work on developing statistical tools to help address health inequities in maternal health.

Previously, I worked on differentially private machine learning, machine unlearning, and applications to organ procurement processes. 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.

news

Apr 29, 2023 Our work on formalizing and analyzing how to fairly use group attributes in prediction models, When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction, was awarded an Oral presentation at ICML 2023!
May 14, 2022 I’m excited to announce that our work from my Summer 2021 internship at Google on Public Data-Assisted Mirror Descent for Private Model Training was accepted to ICML 2022!
Mar 30, 2022 I’m excited to announce that I’ve accepted an offer to return to Google this summer as a Student Researcher working with Dr. Peter Kairouz and Dr. Galen Andrew!
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!