Data Institute Seminar: Resa Shokri

on the July 8, 2019

From 10:00 am
Reza Shokri (National University of Singapore) will give a talk on "Trusting Machine Learning: Privacy, Robustness, and Interpretability Challenges". The event will take place on July 8th from 10:00 am in IMAG Building Amphitheatre.


Machine learning algorithms have shown an unprecedented predictive power for many complex learning tasks. As they are increasingly being deployed in large scale critical applications for processing various types of data, new questions related to their trustworthiness would arise. Can machine learning algorithms be trusted to have access to individuals' sensitive data? Can they be robust against noisy or adversarially perturbed data? Can we reliably interpret their learning process, and explain their predictions? In this talk, I will go over the challenges of building trustworthy machine learning algorithms in centralized and distributed (federated) settings, and will discuss the inter-relation between privacy, robustness, and interpretability.


Reza Shokri is an Assistant Professor of Computer Science at the National University of Singapore (NUS), where he holds the NUS Presidential Young Professorship. His research is on adversarial and privacy-preserving computation, notably for machine learning algorithms. He is an active member of the security and privacy community, and has served as a PC member of IEEE S&P, ACM CCS, Usenix Security, NDSS, and PETS. He received the Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies in 2018, for his work on analyzing the privacy risks of machine learning models, and was a runner-up in 2012, for his work on quantifying location privacy. He obtained his PhD from EPFL.
Published on June 27, 2019

Practical informations


IMAG Building Amphitheatre
700 avenue Centrale
Domaine universitaire de Saint-Martin d'Hères