Mohammad Emtiyaz Khan
1-4-1 Nihonbashi Chuo-ku, Tokyo 103-0027
Japan, Phone: +818094409503
Education

emtiyaz.khan@riken.jp
http://emtiyaz.github.io

University of British Columbia (UBC)
Sep. 2006 - Dec. 2012
PhD, Computer Science
Vancouver, Canada
Research Area Machine learning and statistics
Thesis topic
Variational learning for discrete-data latent Gaussian models
Supervisor
Kevin Patrick Murphy
Committee
Arnaud Doucet (Oxford), Nando D. Freitas (Oxford)
Examiners
David Blei (Columbia), Michael Friedlander (UBC),
Alexandre Bouchard-Côté (UBC), Nando D. Freitas (Oxford)
Indian Institute of Science (IISc)
Jan. 2002 - June 2004
M.Sc. Electrical Communication
Bangalore, India
Research area Statistical signal processing
Thesis topic
Probabilistic models for brain-computer interfaces
G.S. Institute of Technology & Science
B.E. Electronics & Instrumentation

Aug. 1997 - Dec. 2001
Indore, India

Research and RIKEN Center for AI Project
Teaching
Indefinite-Term Team Leader (Tenured)Experience
RIKEN Center for AI Project
Team Leader

Oct. 2022 – present
Tokyo, Japan
Oct. 2016 – Sep. 2022
Tokyo, Japan

Okinawa Institute of Science and Tech.
External Professor

May. 2021 – Aug. 2022
Tokyo, JapanTokyo University of Agriculture and Tech.
Visiting Professor

Apr. 2018 – Mar. 2021
Tokyo, Japan

Waseda University
Part-Time Lecturer

Apr. 2018 – Dec 2018
Tokyo, Japan

University of Tokyo
Visiting Scientist

Mar. 2015 – May 2015
Tokyo, Japan

École Polytechnique Fédérale de Lausanne
Scientific Collaborator

Jan. 2015 – Sep. 2016
Lausanne, SwitzerlandÉcole Polytechnique Fédérale de Lausanne
Post-Doctoral Fellow

Nov. 2012 – Dec. 2014
Lausanne, Switzerland

Xerox Research Center Europe
Research Intern

Aug. 2009 – Dec. 2009
Grenoble, France

Honeywell Technology Solutions Lab
Senior Engineer

Aug. 2004 – Aug. 2006
Bangalore, India

Selected
Works andPublications

N. Daheim, T. Möllenhoff, E. M. Ponti, I. Gurevych, M.E. Khan
Model Merging by Uncertainty-Based Gradient Matching

Under review

Daxberger, Swaroop, Osawa, Yokota, Turner, Hernández-Lobato, Khan
TMLR 2023
Improving Continual Learning by Accurate Gradient Reconstructions of the Past
M.E. Khan and H. Rue
The Bayesian Learning Rule

JMLR 2023

P. Nickl, L. Xu, D. Tailor, T. Möllenhoff, M.E. Khan
NeurIPS 2023
The Memory Perturbation Equation: Understanding Model’s Sensitivity to Data
Piché, Thomas, Pardinas, Marino, Marconi, Pal, Khan
Bridging the Gap Between Target Networks and Functional Regularization

TMLR 2023

M.E. Khan
Variational Bayes Made Easy

AABI 2023

D. Tailor, M.E. Khan, E. Nalisnick
Exploiting Inferential Structure in Neural Processes

UAI 2023

W. Lin, V. Duruisseaux, M. Leok, F. Nielsen, M.E. Khan, M. Schmidt
ICML 2023
Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications
to Deep Learning
P. E. Chang, P. Verma, S. T. John, A. Solin, M.E. Khan
Memory-Based Dual Gaussian Processes for Sequential Learning (Oral)
E. M. Kiral T. Möllenhoff, M.E. Khan
Lie-Group Bayesian Learning Rule

ICML 2023
AISTATS 2023

T. Möllenhoff, M.E. KhanICLR 2023
SAM as an Optimal Relaxation of Bayes (Oral, top 75/5000 submissions)
M.E. Khan and S. Swaroop
Knowledge-Adaptation Priors

NeurIPS, 2021

P. Chang, V. Adam, M.E. Khan, A. Solin
Dual Parameterization of Sparse Variational Gaussian Processes

NeurIPS, 2021

A. Jain, P.K. Srijith, M.E. Khan
Subset-of-Data Variational Inference for Deep Gaussian-Process Regression

UAI, 2021

A. Immer, M. Bauer, V. Fortuin, G. Ratsch, M.E. Khan
ICML, 2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
W. Lin, F. Nielsen, M.E. Khan, M. Schmidt
Tractable structured natural gradient descent using local parameterizations

ICML, 2021

Pan, Swaroop, Immer, Eschenhagen, Turner, Khan
Continual Deep Learning by Functional Regularisation of Memorable Past

NeurIPS, 2020

P. E. Chang, W. J. Wilkinson, M.E. Khan, A. Solin
Fast Variational Learning in State-Space Gaussian Process Models

MLSP, 2020

With N. Tomasev and many others...
Nature Communications, 2020
AI for Social Good: Unlocking the Opportunity for Positive Impact
X. Meng, R. Bachmann, M.E. Khan
Training Binary Neural Networks using the Bayesian Learning Rule

ICML, 2020

W. Lin, M. Schmidt, M.E. Khan
Handling the Positive-Definite Constraint in the Bayesian Learning Rule

ICML, 2020

V. Tangkaratt, B. Han, M.E. Khan, M. Sugiyama
VILD: Variational Imitation Learning with Diverse-quality Demonstrations

ICML, 2020

C. Li, M.E. Khan, Z. Sun, G. Niu, B. Han, S. Xie, Q. Zhao
Exact Recovery of Low-rank Tensor Decomposition under Reshuffling

AAAI, 2020

Osawa, Swaroop, Jain, Eschehagen, Turner, Yokota, Khan
Practical Deep Learning with Bayesian Principles

NeurIPS, 2019

M. E. Khan, A. Immer, E. Abedi, M. Korzepa
Approximate Inference Turns Deep Networks into Gaussian Processes

NeurIPS, 2019

B. Chérief-Abdellatif, Pierre Alquier, M. E. Khan
ACML, 2019
A Generalization Bound for Online Variational Inference (Best paper award)
W. Lin, M. E. Khan, M. Schmidt
ICML, 2019
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family
Approximations
J. Shi, M. E. Khan, J. Zhu
Scalable Training of Inference Networks for Gaussian-Process Models
S. Parisi, V. Tangkaratt, J. Peters, M. E. Khan
TD-Regularized Actor-Critic Methods

ICML, 2019

Machine Learning Journal, 2019

A. Mishkin, F. Kunstner, D. Nielsen, M. Schmidt, M. E. Khan
NeurIPS, 2018
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
M. E. Khan, D. Nielsen
ISITA, 2018
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models
M. E. Khan, D. Nielsen, V. Tangkaratt, W. Lin, Y. Gal, A. Srivastava
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

ICML, 2018

W. Lin, N. Hubacher, M. E. Khan
Variational Message Passing with Structured Inference Networks

ICLR, 2018

H. Ding, M. E. Khan, I. sato, M. Sugiyama
AI-Stats, 2018
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling
M. E. Khan, W. Lin
AISTATS, 2017
Conjugate-Computation Variational Inference : Converting Variational Inference in NonConjugate Models to Inferences in Conjugate Models
P. Rastogi, M. E. Khan, M. Anderson
Building Simulations 2017
Gaussian-Process-Based Emulators for Building Performance Simulation
Olejnik, Petrocelli, Machado, Huguenin, Khan, Hubaux
IEEE S&P, 2017
SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices
M. E. Khan, R. Babanezhad, W. Lin, M. Schmidt, M. Sugiyama
UAI, 2016
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions
V. Etter, M. E. Khan, P. Thiran, M. Grossglauser
Online collaborative prediction of regional vote outcomes.
M. E. Khan, P. Baque, F. Fleuret, P. Fua

DASA, 2016
NIPS 2015

Kullback-Leibler Proximal Variational Inference
M. E. Khan
Decoupled variational inference

NIPS 2014

Y. Ko, M. E. Khan
Variational Gaussian inference for bilinear models of count data (oral)

ACML 2014

M. E. Khan, Y. Ko, M. Seeger
Scalable collaborative Bayesian preference learning (oral)
M. E. Khan, A. Aravkin, M. Friedlander, M. Seeger
Fast dual variational inference for non-conjugate latent Gaussian models
M. E. Khan
Variational learning for discrete-data latent Gaussian models.
M. E. Khan, S. Mohamed, K. Murphy
Fast Bayesian inference for non-conjugate Gaussian process

AISTATS 2014
ICML 2013
PhD thesis, 2012
NIPS 2012

M. E. Khan, S. Mohamed, K. Murphy
ISBA 2012
Large-scale approximate Bayesian inference for exponential family latent Gaussian models
M. E. Khan, S. Mohamed, B. Marlin, K. Murphy
AISTATS 2012
A stick breaking likelihood for categorical data analysis with latent Gaussian models
B. Marlin, M. E. Khan, K. Murphy
ICML 2011
Piecewise bounds for estimating Bernoulli-Logistic latent Gaussian models (oral)
M. E. Khan, B. Marlin, G. Bouchard, K. Murphy
Variational bounds for mixed-data factor analysis

NIPS 2010

B. Mogaddham, B. Marlin, M. E. Khan, K. Murphy
NIPS 2009
Accelerating Bayesian structural inference for non-decomposable Gaussian graphical model
M. E. Khan and D. N. DuttIEEE Transactions on Biomedical Engineering, 2007
An expectation-maximization algorithm based Kalman smoother approach for event-related
desynchronization (ERD) estimation from EEG
M. E. Khan and D. N. DuttEUSIPCO 2004
An EM algorithm for instantaneous frequency estimation with Kalman smoother
M. E. Khan and D. N. DuttEURASIP conference BIOSIGNAL 2004
Estimation of ERS/ERD with Kalman Smoother: An EM Algorithm Approach
Research
Grants

The funding amounts are based on an approximate conversion to USD, and may not be up
to date.
(2021-2027, USD 2.23M) We received a CREST-ANR grant (Japan-France joint call)on “A new Bayes-duality principle for adaptive, robust, and lifelong learning of AI”
a total of USD 2.7 mllion. I serve as the research director on the Japan side which
receives around USD 2.23 million.
(2020-2023, USD 167K) We received a Grant-in-Aid for Scientific Research (Kakenhi)
Project, Category B on “Lifelong Deep Learning using Bayesian Principles”. I am themain PI of this project.
(2020-2023, USD 11K) I am a Co-PI in a Grant-in-Aid for Challenging Research (Kakenhi) with Prof. Rio Yokota from Tokyo Tech as the main PI. The project is on “Application of Modern Linear Algebra for large Deep continual learning”, and started in2020.

(2019-2022, USD 250K) We received this external funding from a company that specializes in manufacturing (name cannot be disclosed unfortunately). We help the company
build new tools for data analysis based on Bayesian methodologies.
(2020, USD 37K) We received external funding from Fujitsu-UK to work on “active deeplearning for engine design”. We helped the company in the deployment of some
Bayesian deep learning methods.
(2015, CHF 250K) Our project secured CHF 620,000 under Swiss-Korea EUREKA initiative on the project titled “DomoHyo : a neurological disease monitoring service
for healthcare professionals”. Our lab in EPFL obtained about half (around CHF
250,000). From 2016, I moved to Japan and I was not involved in this project.
Teaching andSupervision• Offered a course on “The Foundations of Machine Learning” at OIST from May, 2022.
• Tutorial at ACML2021 on “Machine-Learning from Bayesian Perspective” on Nov 9,2021.
• A lecture on “Deep Learning with Bayesian Principles” at SMILES summer school in
Skoltech University on August 17, 2020.
• A lecture on “Fundamentals of ML” at TUAT on Jan 30, 2019.
• A lecture on “Bayesian deep learning” at the Uni. of Tokyo on June 12, 2019.
• A 2 day tutorial on “Approximate Bayesian Inference” at the Data Science Summer
School in ENSAE, Paris on 28-29 June, 2018.
• A lecture on “Bayesian Deep Learning” in University of Tokyo in June, 2018.
• 2 lectures course on “Approximate Bayesian Inference” in Waseda University in June,
2018.
• Offered a course on “The Fundamentals of Machine Learning” in Waseda University
in April, 2018 (a total of 8 lectures).
• A lecture on “Approximate Bayesian Inference” at the University of Tokyo on April
19, 2017.
• A lecture on “The Fundamentals of Machine Learning” at the University of Tokyo on
Jan. 20, 2017.
• A short course on “The Fundamentals of Machine Learning” at ETH, Zurich on Aug.
25, 2015. More than 200 students registered and around 130 attended. The event wasorganized by the group “ML-meetup” from ETH Zurich.
• The graduate course on “Pattern Classification and Machine Learning” (CS-433) at
EPFL during Fall 2014 and Fall 2015.
– In 2015, 189 students registered for the course. I received a teaching award anda teaching prize of CHF 4000 for my teaching efforts.
– In 2014, 176 Master and 16 PhD students registered for the course. I received ateaching prize of CHF 4000, and student ratings with median 5 out of 6.
– It was a 7 credit course with a total 6 hours every week for 14 weeks.
– Assessment was based on multiple assignments, 2 projects, and an exam.
– I made extensive use of in-class polling tools for instruction.
– My course notes were used in the EPFL course taught by Prof. Martin Jaggi andProf. Rudiger Ubanke from 2016 onwards (last time checked in 2022).
• I was a guest lecturer for CS-433 in 2013 and 2014. I was assisting Prof. M. Seeger.
• I was a teaching assistant for the following course at computer science, UBC: Machine Learning (CPSC540), Computer vision (CPSC425), Models of Computation(CPSC121), Introduction to Computation (APSC160).
• I organized refresher courses for the new students in computer science (UBC).

• I have attended two workshops on teaching: “Instructional Skills Workshop” at UBC
in 2012, “University Teaching Workshop” at EPFL in 2015.
External
ThesisExamination• Thesis reviewer and examiner for Mariia Vladimirova’s PhD defense at INRIA (Grenoble, France) on Mar 22, 2022
• Thesis reviewer and examiner for Matthew Wicker’s PhD viva at Oxford University
on Nov 23, 2021
• Thesis pre-examiner for Toni Karvonen’s thesis at Aalto University in 2019
• Thesis reviewer for Quoc Phong Nguyen’s thesis at NUS in 2018

Supervision• Current Team Members
1. Dr. Thomas Möllenhoff (Tenured research scientist, since April 2022, started asa post-doc in Aug. 2020).
2. Dr. Geoffrey Wolfer (Post-Doc, since Mar 2022).
3. Dr. Hugo Daniel Monson Maldonado (Post-Doc, since June 2022).
4. Dr. Keigo Nishida (April 2023-, Concurrent Post-Doc with Makoto Taiji fromRIKEN BDR)
5. Dr. Zhedong Liu (Post-doc, since Nov 2023)
6. Peter Nickl (Research assistant, since May 2021).
7. Prof. Pierre Alquier (Visiting scientist, since Aug 2022).
8. Prof. Joe Austerweil (Visiting scientist, since Aug 2023).
• Current Internship/remote-collaboration students:
1. Dharmesh Tailor (from UvA, Oct 2021-Dec 2023, supervisor: Erik Nalisnick)• Past Team Members
1. Dr. Gian-Maria Marconi (Post-Doc, Aug 2020-Sep 2023).
2. Dr. Lu Xu (Post-Doc, since Nov 2021-Dec 2023).
3. Dr. Wu Lin (PhD student at UBC co-supervised with Prof. Mark Schmidt, Jan2018-June 2023, started a post-doc position at Vector Institute).
4. Dr. Negar Safinianaini (Jan-July 2023, Post-Doc for the Bayes-duality project)
5. Dr. Happy Buzaaba (Post-Doc, June 2022-Aug 2023, started as a post-doc at
Princenton).
6. Dr. Pierre Alquier (Research scientist, Aug 2019-Dec 2022, started as a professor
at ESSEC Business school, Singapore).
7. Dr. Jooyeon Kim (Post-Doc, Dec 2021-Feb 2022, started as an assistant professor
at AIGS, UNIST, S. Korea).
8. Dharmesh Tailor (Research assistant, May 2019-Aug 2021, started PhD at UoA).
9. Dr. Xiangming Meng (Post-Doc, June 2019-March 2020).
10. Vincent Tan (Research assistant, May 2019-Jan 2020).
11. Dr. Hongyi Ding (Post-Doc, June 2019-Jan 2020).
12. Dr. Parag Rastogi (Visiting scientist, April 2017-March 2019).
13. Didrik Nielsen (Research assistant, March 2017-August 2018).
14. Nicolas Hubacher (Research assistant, Jan 2017-Dec 2017).
15. Si Kai Lee (Research assistant, Nov 2017-Aug 2018).
• Past Internship students:

1. Etash Guha (May-Oct 2023, from Georgia Tech.)
2. Erik Daxberger (from UCambridge, Jun 2020-Oct 2023, supervisor: Miguel Hernandez Lobato, originally accepted as intern, but changed to an unofficial collaboration due to Covid)
3. Naima Elosegui (June-Aug 2023, Research Intern from ETH, started as a PhD
student at TU Berlin)
4. Ang Ming Liang (from NUS, July-Dec 2022, supervisor: Wee-Sun Lee, started
as a PhD student at UCL)
5. Yuesong Shen (May-June 2023, Research Intern from TU Munich)
6. Paul Chang (from UAalto, Mar 2021-May 2023, supervisor: Arno Solin)
7. Ramansh Sharma (Aug 2022-Mar 2023, remote collaborator from SRM Institute)
8. Alexander Piche (from MILA, Sep. 2020-Mar 2023, supervisor: Chris Pal, originally accepted as intern, but changed to an unofficial collaboration due to Covid)
9. Kenneth Chen (from UCLA, at OIST, June-Aug 2022)
10. Ali Unlu (at OIST, Apr-Aug 2022, started as a PhD student at Bilkent Uni.)
11. Siddharth Swaroop (from UCambridge, Nov. 2018-July 2022, supervisor: Rich
Turner, planned as Internation Program Associated, but changed to an unofficial
collaboration due to Covid)
12. David Tomàs Cuesta (Jan 2022-Apr 2022, Rotation PhD student from OIST).
13. Tojo Rakotoaritina (Jan 2022-Apr 2022, Rotation PhD student from OIST)
14. Happy Buzaaba (Part-Time Student from Tsukuba Universtiy, July 2020-Mar
2022, started a full-time postdoc position in our group).
15. Ted Tinker (Visiting PhD student, Sep-Dec 2021).
16. Evgenii Egorov (Jun 2020-May 2021, Remote collaborator, originally accepted asintern, but changed to an unofficial collaboration due to Covid).
17. Fariz Ikhwantari (Part-Time Student from TokyoTech, August 2020-Mar 2021).
18. Dimitri Meunier (May 2020-Nov 2020, Remote collaborator, originally accepted
as intern, but changed to an unofficial collaboration due to Covid).
19. Maciej Korzepa (Feb-Dec 2019, PhD intern from DTU, Copenhagen).
20. Alexander Immer (Mar 2019 to Mar 2020, MSc intern from EPFL, Switzerland).
21. Anshuk Uppal (June-Dec 2019, MSc Intern from IIIT Bangalore, India).
22. Michael Przystupa (July-Dec 2019, Intern from UBC Vancouver).
23. Roman Bachmann (July 2019-Feb 2020, MSc Intern from EPFL, Switzerland).
24. Lucie Perrota (Sep 2019-Feb 2020, MSc Intern from EPFL, Switzerland).
25. Farzaneh Mahdisoltani (Sep 2019-Feb 2020, PhD Intern from UofT, Canada).
26. Pingbo Pan (May-Sep 2019, PhD Intern from UT Sydney, Australia).
27. Ehsan Abedi (MSc intern from EPFL, Switzerland, Mar-Aug 2019).
28. Benjamin Bray (May-Aug 2019, PhD Intern from Georgia Tech, USA).
29. Pierre Orenstein (May-Sep 2019, MSc Intern from ENS-Cachan, France).
30. Mark Goldstein (June-Sep 2019, PhD Intern from NYU, USA).
31. Anand Subramanian (Part-time student from JAIST, Japan, Feb-May 2019).
32. Anirudh Jain (MSc intern from ISM, Dhanbad, India, Dec 2018-Aug 2019).
33. Runa Eschenhagen (Undergraduate intern from University of Strathclyde, Oct
2018-May 2019).
34. Jiaxin Shi (PhD intern from Tsinghua University, China, July-Sep. 2018).
35. Ohiremen Dibua (PhD intern from Stanford University, USA, July-Sep. 2018).

36. Frederik Kunster (MSc intern from EPFL, Switzerland, Feb-Aug. 2018).
37. Aaron Mishkin (Undergraduate intern from UBC, Canada, Jan-June, 2018).
38. Hanna Tseran (MSc intern from UTokyo, Japan, Nov-2017 to June 2018).
39. Zuozhu Liu (PhD intern from SUTD, Singapore, July-Dec. 2017).
40. Salma El Aloui (Master thesis from Ecole Polytechnique, July-Oct. 2017).
41. Vaden Masrani (Master intern from UBC, Vancouver, Canada, May-Oct. 2017).
42. Kimia Nadjahi (Master thesis from ENS Cachan, France, May-Sep. 2017).
43. Arnaud Robert (Master thesis from EPFL, Oct. 2016 to Apr. 2017).
• Student Projects in EPFL:
1. Hubacher Nicholas Daniel (Master thesis, Feb.-Aug. 2016).
2. Arnaud Robert (Master project, Feb.-Aug. 2016).
3. Thibaut Loiseleur (Master project, Feb.-Aug. 2016).
4. Mattia Carpin (Master thesis, Feb.-Aug. 2015 with Prof. B. Rimoldi).
5. Marc Bourqui (Master project, Feb.-Aug. 2015).
6. Nezihe Merve Gurel (Master project, Feb.-Aug. 2015).
7. Wu Lin (Mentee for Google-summer-of-code along with Dr. Heiko Strathmannfrom Gatsby Neuroscient Unit at London in 2014).
8. Young-Jun Ko (PhD co-supervised with Prof. M. Grossglauser and Prof. M.
Seeger, finished 2017)
9. Otilia Stretcu (Summer Intern, 2013).

Awards andAchievements• Our group won first prize (USD 3000) at the NeurIPS 2021 competition on “Approximate Inference in Bayesian Deep Learning”. I received a Letter of Appreciation frompresident of RIKEN.
• Invited to give a tutorial at NeurIPS 2019 on “Deep Learning with Bayesian Principles”. This was among 9 other tutorials that year, and was one of the most popular andwell-attended tutorials (total attendance at NeurIPS was around 13,000 that year).
• Best paper award at ACML 2019 for the paper “A Generalization Bound for Online
Variational Inference” coauthored with B. Chérief-Abdellatif and P. Alquier.
• I received teaching award in 2015 at IC, EPFL. I also received two teaching prizes(total worth CHF 8000) during 2014-2015.
• I received a graduate teaching assistant award in 2009 at UBC.
• I received a student service award in 2008 at UBC for starting the “undistinguishedlecture series (UDLS)”. The series is successfully running since 2007 (>15 years).
• Secured All-India Rank of 49 in Graduate Aptitude Test in Engineering (GATE), 2001.

Service to theCommunity• Program Chair for AISTATS 2025.
• Program Chair for ICLR 2024.
• Senior Area-Chair for NeurIPS 2023.
• ISBA 2024 Scientific Committee.
• Reviewer for ICML 2023 Workshop Proposals.
• Senior Area-Chair for AISTATS 2023.
• Associate Editor of Information Geometry Journal.
• Senior Area Chair for ICLR 2023.
• Area Chair for AISTATS 2023.

• Senior Area Chair for NeurIPS 2022.
• Program chair for ACML 2022.
• Senior Area Chair for ICLR 2022.
• Workshop co-chair for ACML 2021.
• Senior Area Chair for NeurIPS 2021.
• Co-organized “Well-Being in ML” sessions at Neurips 2021.
• Meet co-chair for NeurIPS 2021.
• Mentorship chair for AIStats 2021.
• Co-founder of the Mentorship portal MeMentor (used at NeurIPS 2020).
• Action editor for Journal of Machine Learning Research (JMLR) from Nov. 2016.
• Equity, Diversity and Inclusion Chair for ICLR 2021.
• Meetup Chair for NeurIPS 2020.
• Workshop Chair for ICML 2020.
• Senior Area Chair for IJCAI-PRECAI 2020.
• Area Chair for ICML 2021, AIstats 2021, ICLR 2020, NeurIPS 2019, ACML 2019,
ICML 2019, ICLR 2019, NeurIPS 2018, ACML 2018, ICML 2018, NIPS 2017, and
NIPS 2015.
• Tutorials Chair for ACML 2019.
• Organized a half-day “PreAISTATS ML Seminar at RIKEN AIP” on April 15, 2019.
• Organized IIT-Hyderabad and RIKEN-AIP Joint Workshop on ML and Applicationsin Hyderabad, India on March 15-16, 2019.
• Organized Dagstuhl Seminar on “AI for Social Good” in Feb 2019. Attended by 23
participants, half were from ML/AI and the other half were from NGOs.
• Served as a reviewer for many conferences and journals: NeurIPs-2020 workshops, Nature Communications (1), NeurIPS-Workshops-2020, AI-stats 2020, UAI-2019, IJCAI2019, ICASSP-2018, UAI-2018, ICLR-2018, AAAI-2018, ICML-2017, UAI-2017, ICML2016, AI-Stats-2016, ICML-2015, IJCAI-2015, NIPS-2014, NIPS-2013, JMLR, Bayesiananalysis, Transactions of Neural Networks, Journal of Machine Learning (JMLR), Neural Computation, Transaction on Pattern Analysis and Machine Intelligence (TPAMI),IEEE Transaction of Signal Processing, Medical and Biological Engineering and Computing (MBEC).
• Member of the committee for the “data-science initiative” at EPFL. I helped thecommittee with the curriculum-design of the Data-Science Masters program scheduled
to be launched in 2017.
• Mentor at Google Summer of Code, 2014 for Shogun Machine learning Toolbox.
• Vice-President for Computer Science Graduate Students Association at UBC in 2007.
• Started the “Undistinguished lecture series” in Computer Science, UBC in 2007. The
series is successfully running for 8 years.

Selected
Presentations• (01 Nov, 2023) Keynote speaker at IBIS, Kita-Kyushu, Japan [Slides]• (18 Jul, 2022) Invited speaker at the 41st Maximum Entropy conference, 2022
• (Dec 14, 2021) Invited talk at the NeurIPs 2021 Bayesian Deep Learning Workshop.
• (Aug 15, 2021) Invited talk at KDD Workshop on Model Mining on “K-priors: A
General Principle of Adaptation”.
• (Jul 23, 2021) Invited talk at ICML Workshop on Theory & Foundations of Continual
Learning on “K-priors: A General Principle of Adaptation”.
• (Jul 20, 2020) Invited to give a tutorial at SPCOM 2020 on “Deep Learning with
Bayesian Principles”.
• (Dec 9, 2019) Tutorial at NeurIPS 2019 on “Deep Learning with Bayesian Principles”.
This was among 9 other tutorials that year, and was one of the most popular andwell-attended tutorials (total attendance at NeurIPS was around 13,000 that year).
• (Mar 5, 2019) Invited Talk at “Conference on Algorithms, Optimization and Learningin Dynamics Environments” in Hanoi, Vietnam.
• (Feb 15, 2019) Invited talk at the conference on theoretical foundations of machinelearning (TFML) in Krakow, Poland.
• (Dec 2, 2018) Invited talk at Advances in Approximate Bayesian Inference Symposium
colocated in Montreal with NeurIPS.
• (Sep 29, 2018) Invited talk/paper presentation at ISITA 2018.
• (Sep 20, 2018) Invited talk at the workshop on “AI meets Life-Science”, Karolinska
Institutet.
• (Feb, 2017) Invited talk at the 2017 Probabilistic Graphical Model Workshop, Tokyo.

Other
Presentations• (28 Nov, 2023) Invited talk at the KAIST AI Colloquium• (20 Oct, 2023) Invited talk at the Continual AI Un-conference [Slides]• (21 Sep, 2023) UNIST AI Technology Open Workshop, Korea [Slides]• (24 Aug, 2023) Minisymposium on Interaction between Harmonic Analysis and Data
Science at ICIAM 2023, Tokyo, Japan• (04 Aug, 2023) Machine Learning Research School (MLRS), Bangkok, Thailand• (04 Aug, 2023) 6th Workshop on Tractable Probabilistic Modeling, at UAI-2023• (04 Jul, 2023) The Hessian AICon 2023, Darmstadt, Germany• (12 Jun, 2023) International Chinese Statistical Association (ICSA) 2023, Universityof Michigan, Ann Arbor, USA• (27 Apr, 2023) the One World ABC Seminar• (03 Dec, 2022) NeurIPS 2022 Workshop on Broadening Research Collab. in ML• (24 Oct, 2022) University of Bath in Prof. Olga Isupova’s group• (17 Oct, 2022) Invited talk at ML and its applications, NUS, Singapore• (13 Oct, 2022) Invited tutorial at ML and its applications, NUS, Singapore• (19 Sep, 2022) International Conference on Information Geometry for Data Science,
Hamburg• (27 Jun, 2022) Princeton AI club• (05 Jun, 2022) EcoSta 2022, Kyoto• (28 Apr, 2022) IST Lisbon, MPML seminar series• (13 Apr, 2022) EPFL CIS - RIKEN AIP Joint Seminar Series• (24 Mar, 2022) Talk at MLT init• (16 Mar, 2022) Cambridge CBL Reading Group• (11 Mar, 2022) DeepMind/ELLIS CSML Seminar Series at UCL• (1 Mar, 2022) ATR-AIP Joint Seminar on Neuroscience-Inspired AI• (22 Feb, 2022) AI4Sec Seminar Series at Huwaei Research Munich• (Feb 17, 2022) StatML CDT seminar at Imperial College London and University of
Oxford• (13 Dec, 2021) Invited panelist at the NeurIPs 2021 ”I can’t believe it’s not better”
workshop• (Sep 21, 2021) Tubingen AI Center, “Bayes for learning-machines”• (Sep 16, 2021) Secondmind.ai, “Bayes for learning-machines”• (Sep 16, 2021) Durham CDT data science workshop “Bayes for learning-machines”• (Feb 9, 2021) ML for soft matter “Bayes for learning-machines”• (Dec 21, 2020)CMStatistics Conference “Bayes for learning-machines”• (Nov 17, 2020) Cosy Seminars, Uppsala University “Bayes for Learning Machines”• (Nov 5, 2020) TU Darmstadt “Bayes for Learning Machines”• (Nov 3, 2020) Waterlook AI Institute “Bayes for Learning Machines”• (Dec 16, 2019) CAIDA, UBC• (Nov 18, 2019) Stats and Data Science Workshop, KAUST• (Nov 12-15, 2019) Conference on Data Science, Fields institute, Toronto• (Oct 01, 2019) Talk at Neurotechnology and AI, 2019 at RIKEN-AIP• (Sep 23, 2019) MSR, Cambridge• (Sep 20, 2019) DeepMind, London• (Sep 19, 2019) Gatsby/UCL, London• (Sep 17, 2019) Turing Institute, Edinburgh• (Sep 16, 2019) Imperial College, London• (Sep 12, 2019) IC, EPFL
• (Sep 09, 2019) TU, Berlin.
• (Aug 22, 2019) Tokyo Tech. Workshop• (Jul 29, 2019) Talk at Discrete Optimization Workshop at RIKEN, AIP Tokyo• (Jun 2, 2018) Talk at PEAR-AIP Workshop, Taiwan• (Nov 19, 2018) Talk at IIT Mumbai, India• (Nov 5, 2018) Talk at IIIT Bangalore, India• (Nov 2, 2018) Talk at IISc Bangalore, India• (Nov 1, 2018) Talk at SUTD Singapore• (Sep 23, 2018) Invited talk at RIKEN-NUS Workshop, Singapore• (July 18, 2018) The conference on Discrete Optimization and Machine Learning• (July 18, 2018) DeepMind (London)• (July 17, 2018) Oxford University (Invited by Prof. Arnaud Doucet)• (July 16, 2018) Cambridge University (Invited by Prof. Richard Turner)• (July 12, 2018) Internation Conference on Machine Learning (ICML)• (July 5, 2018) TU, Berlin (Invited by Prof. Klaus-Robert Muller)• (July 2, 2018) DTU, Copenhagen (Visiting Prof. Ole Winther)• (June 2, 2018) ENSAE, Paris (Invited by Prof. Pierre Alquir)• (Mar 8,2018) UBC in Canada (Visiting Prof. Mark Schmidt)• (Mar 19, 2018) Talk at Tokyo Deep Learning Workshop, Japan• (Mar 8, 2018) IIT Hyderabad, India (Invited by Prof. Srijith P.K.)• (Aug-2017) ERATO (Season IV) in Tokyo• (July-2017) Advanced Telicommunication Research, Kyoto• (Jan-2017) Talk at ERATO (Season III) in Tokyo• (July-2016) Department of Computer Science, University of British Columbia• (July-2016) Department of Computer Science, Columbia University• (Apr-2016) Advanced Telecommunication Lab, Kyoto• (Mar-2016) Gatsby Neuroscience Unit, UCL, London• (Mar-2016) School of Informatics, University of Edinbrugh, Edinburgh• (Oct-2015) Amazon, Berlin• (Oct-2015) Technische Universitat, Berlin• (Sep-2015) Theory seminar at Computer Science, EPFL
• (Sep-2015) Department of Computer Science, University of Freiburg• (Sep-2015) Norwegian University of Science and Technology (NTNU)• (Apr-2015) University of Tokyo at Masashi Sugiyama’s lab• (Oct-2014) Invited talk at the E-cardiology conference in Bern
• (Aug-2014) Tokyo Institute of Technology and Science (Masashi Sugiyama’s lab)
• (Apr-2014) Paper presentation in AI-Stats at Reykjavik, Iceland• (Apr-2014) Statistics Department, University of Iceland• (Sep-2013) Invited talk at the Latent Gaussian Model workshop in Iceland• (Dec-2013) Computer Science Department at Columbia University in NY (USA)• (Sep-2013) Norwegian University of Science and Technology NTNU (Norway)• (Mar-2012) Xerox Research Center Europe at Grenoble (France)• (Mar-2012) INRIA-SIERRA at Paris (France) at Francis Bach’s lab• (2011) Microsoft Research at Redmond (USA)• (2011) Paper presentation in ICML at Redmond (USA)