Vinith Suriyakumar

MIT EECS, LIDS, IMES, Bridgewater Associates

I am a fifth year PhD student at MIT EECS where I am advised by Dr. Ashia Wilson and Dr. Marzyeh Ghassemi. I also collaborate frequently with Dr. Dylan Hadfield-Menell. I’m supported through a fellowship from Bridgewater Associates and AIA Lab led by Dr. Jas Sekhon where I also conduct research part-time.

Broadly, I’m interested in the privacy, security, and safety of machine learning. Throughout my Masters and PhD, I’ve worked on many topics in these areas including differential privacy, auditing, algorithmic fairness, and unlearning.

These days, the goal of my research is to prevent the misuse of foundation models, especially when it comes to open-sourcing these models. I work on questions related to uncovering risks of open-weight models, applying intepretability to understand these risks, and building safeguards to address these risks.

My research advancing the privacy, security and safety of machine learning has received awards at NeurIPS, ICLR, ICML, and FAccT:

  • ICML 2022 Spotlight
  • ICML 2023 Oral
  • ICLR 2024 Oral
  • FAccT 2024 Best Paper Award
  • NeurIPS 2025 Spotlight

I’ll be a Visiting Student Researcher at Stanford with Dr. Sanmi Koyejo from February to June 2026. Afterwards, I’ll be interning at Meta’s Superintelligence Labs working on privacy and security research for AI agents under Dr. Kamalika Chaudhuri’s team from June to August 2026.

news

Oct 18, 2025 I gave two invited talks recently on open-weight model safety and unlearning at the MITAI Conference and the Bridgewater AIA Distinguished Speaker Series.
Sep 18, 2025 Our work uncovering and formalizing spurious correlations originating from syntactic shortcuts in LLMs, Learning the Wrong Lessons: Syntatic-Domain Shortcuts in Language Models
Shaib, was awarded a Spotlight at NeurIPS 2025! This work provides a new perspective on different forms of memorization and uncovers a new type of jailbreak in LLMs.
Jun 3, 2024 Our work on developing a philosophical framework to better reason about the impact of algorithmic decision-making on equal opportunity, Algorithmic Pluralism: A Structural Approach Towards Equal Opportunity, was awarded Best Paper at FAccT 2024!