Emti's photo

Emtiyaz Khan

I am a team leader (tenured) at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where I lead the Approximate Bayesian Inference (ABI) Team. I am an Action Editor for the Journal of Machine Learning (JMLR), and have served in organization and reviewing of most major Machine Learning conferences. From April 2018 to March 2021, I was a visiting professor at the EE department in Tokyo University of Agriculture and Technology (TUAT), and a part-time lecturer at Waseda University. From 2014 to 2016, I was a scientist at EPFL in Matthias Grossglausser's lab. During my time at EPFL, I taught two large machine learning courses for which I received a teaching award. I first joined EPFL as a post-doc with Matthias Seeger in 2013 and before that I finished my PhD at UBC in 2012 under the supervision of Kevin Murphy.

emtiyaz [at] gmail.com [or] emtiyaz.khan [at] riken.jp Mastodon ORCID

Research Publications Teaching People News Service CV


Humans, animals, and other living beings have a natural ability to autonomously learn throughout their lives and quickly adapt to their surroundings, but computers lack such abilities. My goal is to bridge such gaps between the learning of living-beings and computers. My current focus is on AI that can autonomously learn to perceive, act, and reason throughout their lives.

I work on problems in several areas of machine learning, such as approximate inference, deep learning, reinforcement learning, active learning, online learning, and reasoning in computer vision. In my recent works, I have worked on ideas from a wide range of fields, such as, optimization, Bayesian statistics, information geometry, signal processing, and control systems.

For details of my research activites, see the following pages,

We are thankful for the following funding (amount is approximate),

  • (2021-2026, USD 2.23 Million) JST-CREST and French-ANR's grant, The Bayes-Duality Project
  • (2020-2023, USD 167,000) KAKENHI Grant-in-Aid for scientific Research (B), Life-Long Deep Learning using Bayesian Principles
  • (2020-2023, USD 11,000) KAKENHI Grant-in-Aid for Chellenging Research (Exloratory), Linear algebra for continuous learning of large neural networks, PI: Rio Yokota, Total Budget: JPY 19,710,000
  • (2019-2022, USD 237,000) External funding through companies for several Bayes related projects

Research Highlights