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Emtiyaz Khan

I am a (tenured) team director at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where I lead the Adaptive Bayesian Intelligence Team. I am also a visiting professor at Technical University Darmstadt (Germany), and soon will be joining as a full professor (W3) there (affiliated with Hessian.AI and the Center of Excellence on Reasonable AI). I have served as Program Chair of AISTATS 2025, ICLR 2024, and ACML 2022, and regularly serve as a Senior Area Chair, Area Chair, and reviewer in all major machine-learning venues (NeurIPS, ICML, ICLR, and AISTATS). I am an Action Editor for the JMLR and TMLR. I am currently serving as General Chair of AISTATS 2026. Previously, I was a postdoc and then a scientist at Ecole Polytechnique Fédérale de Lausanne (EPFL), where I also taught two large machine learning courses and received a teaching award. I first joined EPFL as a post-doc with Matthias Seeger in 2013, was a scientist at EPFL in Matthias Grossglausser's lab from 2014-2016. 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

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Research

My current research focuses on the development of sustainable and transparent Artificial Intelligent (AI) systems. The goal is twofold: one, to break free from heavy dependence on extremely large data sets, computations, and energy sources, and two, to build systems with transparent functionalities. I hope to achieve these goals by developing AI systems that can learn via quick and continual adaptation, similarly to humans and animals.

I use techniques from a wide variety of fields, such as deep learning, statistics, and optimization, covering both theoretical and practical tools. Over the years, my research has consistently shown that Bayesian principles provide promising ways to address this problem. I have enjoyed this journey and have had my own (personal) breakthroughs along the way. I enjoy making progress towards my goal, one day at a time. I do this because I believe in slow and rigorous scientific process that aims to add value to existing knowledge. I care deeply about people and am committed to promoting well-being, inclusion, diversity, equity, privacy, and justice in society.

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

We are thankful for the following funding (rough approximation because of inflation),

  • (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