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Emtiyaz Khan

I am a team leader (equivalent to Full Professor) at the center for Advanced Intelligence Project (AIP), RIKEN in Tokyo where I lead the Approximate Bayesian Inference (ABI) Team. I am an Action Editor for the Journal of Machine Learning (JMLR). 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.

Contact: emtiyaz [at] gmail.com [or] emtiyaz.khan [at] riken.jp

Research Publications Teaching News Blog


I have multiple open positions for post-docs, research assistants, and interns in my team. Please email me if you are interested. You might also want to see this advert for more details.

(July 24, 2017) I will be giving a talk at ERATO in Tokyo on Aug. 3, 2017

(July 1, 2017) I gave a talk at ATR in Kyoto on July 10, 2017

(June 25, 2017) Salma El Aloui (from École Polytechnique) and Zuozhu Liu (from Singapore University of Technology and Design) join as interns.

(June 19, 2017) Prof. Havard Rue from KAUST visiting from Jun 19-25.

I am an area chair for NIPS 2017, a reviewer for AAAI, ICML and UAI 2017, and an action-editor for JMLR.

New paper accepted in the IEEE conference on Security and Privacy, 2017. Currently most smartphones allow users to set their privacy preferences only during the installation of an app (e.g. answers to questions like "Facebook would like to access your location: allow/deny?"). Such static policies may force users to overshare sensitive data because it does not take the context into account (e.g. sharing location might be ok when we are using the app, but not ok when the app is running in the background). In this paper, we propose a machine learning framework to automatically make such decisions on a user's behalf. Our method uses context to achieve an accuracy of around 80% which is a 50% improvement over the static-policies currently used in Android and iOS.

New paper accepted in the conference Building-Simulation 2017. In this paper, we show that Gaussian Process (GP) regression can reliably predict the energy consumption of a building design. Traditionally, designers rely on simulators to assess energy efficiency of their designs, but this could be painfully slow. We show that a GP based emulator gives accurate results within seconds. This work is in collaboration with Dr. Parag Rastogi and Prof. Marilyn Anderson from EPFL.


"My main goal is to understand the principles of learning from data and use them to develop algorithms that can learn like living beings."

My current focus is to understand the role of uncertainty in learning and to develop fast algorithms for uncertainty estimation.

Research Highlights

Current projects

  • Variational inference for large and complex models.
  • Stochastic algorithms for Bayesian deep learning.
  • Scalable inference for Gaussian process models.
  • Automating Data Science.

We are also working on the following application projects:

  • Machine learning for the design of high-performance buildings.
  • UAVs doing Bayesian optimization to track humans.
  • Context-aware and automatic permissions for mobile devices.

Current team members

  • Nicolas Hubacher (Research Assistant)
  • Wu Lin (Research Assistant)
  • Didrik Nielson (Research Assistant)
  • Kimia Nadjahi (Intern from ENS Cachan)
  • Vaden Masrani (Intern from UBC)
  • Salma El Aloui (Intern from École Polytechnique)
  • Zuozhu Liu (Intern from Singapore University of Technology and Design).

Collaborators and past team members



News Archive

  • (Apr 19, 2017) I gave a lecture at the University of Tokyo on Modern Approximate Bayesian Inference Methods. Download slides and their annotated version.
  • (Apr 18, 2017) Prof. Marco Cuturi and Prof. Shun-ichi Amari visited AIP and gave talks about their work on Wasserstein distance.
  • (Apr 17, 2017) Vaden Masrani (from UBC) and Kimia Nadjahi (from ENS Cachan) joined as interns in my group.
  • (Mar 24, 2017) Heiko Strathmann from UCL is visiting from March 24-31, 2017.
  • (Feb 27, 2017) Maja Rudolph from Columbia University visited AIP in March, 2017.
  • (Feb 22, 2017) New talk at the PGM workshop 2017 in ISM about "Conjugate-Computation Variational Inference".
  • (Jan 2016) I presented a poster at the Winter-Festa (YouTube link and "hand-made" poster).
  • (Dec. 2016) New Paper at Bayesian Deep Learning workshop in NIPS 2016 for inference in Deep Exp-Family Models.
  • (Oct-2016) I became a Team-Leader in Tokyo at RIKEN's newly established Center for Advanced Intelligence Project (AIP).
  • (Aug-2016) A new paper at DSAA, 2016.
  • (20-Dec-2015) A new paper at UAI, 2016.
  • (10-Dec-2015) I got the teaching award for 2015!
  • (06-Dec-2015) I am at NIPS 2015.
  • (23-Oct-2015) Talk at Amazon, Berlin.
  • (20-Oct-2015) Talk at TU, Berlin.
  • (11-Oct-2015) I am a reviewer for AI-Stats 2016.
  • (28-Sep-2015) I gave a talk at the theory seminar in EPFL about my research.
  • (18-Sep-2015) I have a new paper in NIPS 2015 on "KL Proximal Variational Inference".
  • (10-Sep-2015) I visited Frank Hutter in Freibourg and gave a talk there.
  • (07-Sep-2015) I gave a talk about my work in NTNU, Norway.
  • (25-Aug-2015) I offered a short course on "Fundamentals of ML" on August 25, 2015 in Zurich . More than 200 people registered and around 120 people attended.
  • (07-Aug-2015) Course webpage for PCML is available.
  • (15-Jul-2015) I attended ICML 2015 in Lille.
  • (30-May-2015) I visited Masashi Sugiyama's lab in University of Tokyo from March-May, 2015.
  • (Apr-2015) I am an area chair for NIPS 2015.
  • (Feb-2015) I am now a 'scientific collaborator' at EPFL.
  • (Dec-2014) I have a new paper at NIPS 2014. Unfortunately, I couldn't attend due to visa issues.
  • (Dec-2014) I taught Pattern Classification and Machine Learning in EPFL from Sep 2014 to Feb 2015. The course had a total 190 Master level students and received a rating of median 5 out of 6. Young-Jun presented our paper in ACML 2014.
  • (Aug-2014) I gave a talk in Shogun-workshop in July 2014 (video link).
  • (May-2014) I presented our paper in AI-Stats-2014 (video link).
  • (May-2014) I mentored a project on variational inference for Google-Summer-of-Code-2014, along with Heiko Strathmann. Check out the Notebook outlining the project for Shogun toolbox.
  • (Feb 2014) I joined as a post-doc with Matthias Grossglauser at LCA lab in EPFL.
  • (Sep-2013) I gave an invited talk at the LGM-2013 workshop in Iceland.
  • (Nov 2012) I joined as a post-doc with Matthias Seeger at LAPMAL lab in EPFL.
  • (11 Mar 2012) Invited talks at EPFL, XRCE, and INRIA-SIERRA [ slides ].
  • (08 Feb 2012) A tutorial report on Approximate message passing from my talk on DNOISE.
  • (29 Sep 2011) Talk at Microsoft Research, Redmond [ video ] [ slides ]
  • (29 Jun 2011) Talk at ICML 2011 [ video ] [ slides ]
  • (22 Apr 2011) Derivation of an EM algorithm for Latent Gaussian Model with Gaussian Likelihood [ pdf ]
  • (14 Sep 2009) Derivation of Variational EM algorithm for Correlated Topic Model [ pdf ]
  • (25 Feb 2009) Derivation of Gaussian likelihood with Gaussian prior on mean [ pdf ]
  • (29 Jan 2009) A note on empirical Bayes estimate of Covariance for Multivariate Normal Distribution [ pdf ]
  • (24 Dec 2008) Tech report on Bayesian search algorithms for decomposable Guassian graphical model [ pdf ]
  • (27 Feb 2008) Updating Inverse of a Matrix when a Column is added/removed [ pdf ] [ code ]
  • (25 Feb 2008) Talk on Kalman filter and demo code [ Slides ] [ Demo ]
  • (25 Feb 2008) Notes on information filter [ pdf ]
  • (30 Oct 2007) Presentation on Variational Bayes and Message passing at Machine learning Reading Group [ slides ]
  • (02 Oct 2007) A note on Exchangeability, Polya’s Urn, and De-Finetti’s Theorem [ pdf ]
  • (28 Sep 2007) Linear Algebra Tutorial [ Outline ] [ slides ]
  • (18 Sep 2007) Probability Tutorial [ Outline ] [ Slides ]
  • (14 June 2007) Talk on Brain-Computer Interface, CIFAR Time-series Workshop, Toronto [ slides ]
  • (18 May 2007) Talk on Signal Compression and JPEG, UDLS [ Abstract ] [ slides ]
  • (April 2007) Compressed Sensing, Compressed Classification and Joint Signal Recovery, Machine Learning course project [ pdf ]
  • (April 2007) Gibbs Sampling for the Probit Regression Model with Gaussian Markov Random Field Latent Variables, Statistical Computation course project [ pdf ] [ slides ]
  • (26 Jan 2007) Talk on "Introduction to probability theory, UDLS [ slides ] [ Abstract ]
  • (Dec 2007) Game theory models for Pursuit-evasion games, Multi-agent systems course project [ pdf ]
  • (Dec 2007) An incremental deployment algorithm for mobile sensors, Optimization course project [ pdf ]