What can Cryptography do for Machine Learning?
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What can Cryptography do for Machine Learning?

Speaker: Vinod Vaikuntanathan, MIT

Cryptography, Theoretical Machine Learning
  • Date 23 July 2026
  • Location SSB 334 (AM Turing Hall), CSE Dept.
  • Time 10.00 AM

Abstract

I will discuss the notion of robust embeddings, highlighting how cryptographic perspectives help formalise and reason about robustness in representation learning. Second, I will introduce trapdoored matrices, linear transformations equipped with hidden structure, and explain what they enable in machine learning settings. Together, these two results illustrate how cryptographic thinking can lead to new concepts, tools, and questions at the interface of Cryptography and Machine Learning.

Speaker Bio

Vinod Vaikuntanathan is the Ford Foundation Professor of Engineering in the EECS department at MIT, a principal investigator at MIT CSAIL, and the chief cryptographer at Duality Technologies. He earned his BTech degree from the Indian Institute of Technology Madras in 2003, and his SM and PhD degrees from MIT in 2005 and 2009, respectively. After a postdoctoral stint at IBM Research, a year as a researcher at Microsoft, and two years as a faculty member at the University of Toronto, Vinod joined the faculty of MIT EECS in September 2013.
Vinod's research is on the foundations of cryptography and its applications to theoretical computer science at large. He is known for his work on fully homomorphic encryption (a powerful cryptographic primitive that enables complex computations on encrypted data), as well as lattice-based cryptography (which lays down a new mathematical foundation for cryptography in the post-quantum world). Recently, he has been interested in the interactions of cryptography with quantum computing, as well as with statistics and machine learning.
Vinod's work has been recognized with the Harold E. Edgerton Faculty Award (2018), the Godel Prize (2022), the Simons Investigator Award (2023), the Distinguished Alumnus Award from IIT Madras (2024), a Guggenheim Fellowship (2026), a Best Paper Award from CRYPTO 2024, and test of time awards from IEEE FOCS and CRYPTO conferences. He was also named a MacVicar Faculty Fellow in 2024 for exceptional teaching and mentoring, and an IACR Fellow in 2026.