I am a principal researcher at Microsoft Research Cambridge, where I lead project Azua: Efficient Decision Making. Before joining Microsoft Research, I was at Disney Research Pittsburgh located at Carnegie Mellon University. I have received my PhD from the Department of Robotics, Perception and Learning (RPL/ former CVAP), KTH Royal Institute of Technology. My general interests are causal machine learning, deep generative models and approxmiate inference.
I like to work with motivated students. We have internship positions open every year. Curently, we also have multiple senior research software engineer postion open. Please contact me if you want to collaborate.
Jan 2022: Our paper “Optimal Transport for Causal Discovery” is accepted at ICLR and “Local Constraint-Based Causal Discovery under Selection Bias” is accepted at CLearR.
Oct 2021: We (Emre Kiciman, Amit Sharma, Greg Lewis, Vasilis Syrgkanis) organize and give the opening talk in causal ML track in Microsoft Research Summit. Check it out.
Sep 2021: Two papers have been accepted to NeurIPS 2021.
Sep 2021: We have opensouced our project Azua https://github.com/microsoft/project-azua
Jul 2021: I have an invited lecture at Oxford ML summer school.
Sep 2020: Three papers have been accepted to NeurIPS 2020.
Aug 2020: I am co-organizing NeurIPS 2020 hide-and-seek privacy challenge and NeurIPS 2020 Education Challenge. Together with Yingzhen Li, I will give a tutorial on “Advances in Approximate Inference” in NeurIPS 2020.
July 2020: I will give an invited talk on “Divergence Measures in Variational Inference and How to Choose Them ” in ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models on July 18th;
I will give an invited talk on “A Causal View on Robustness of Neural Networks” in Frontiers in Machine Learning on July 21st.
I will give an invited talk on “Efficient element-wise information acquisition with Bayesian experimental design ” in Laplace’s Demon on July 29th
I will give an invited lecture on “Bayesian ML” in Oxford Machine Learning Summer School on Aug 17th.
I will give an invited talk on “Causal discovery: methodology, evaluation and application” in Gaussian Process and Uncertainty Quantification Summer School, on 17th Sep
Feb 2020: One paper has been accepted to ICLR 2020.
Sep 2019: Three papers have been accepted to NeurIPS 2019, and one journal paper has been accepted to JSTAT.
I co-organize the second symposium on Advances in Approximate Bayesian Inference. Welcome to submit papers and attend the symposium
Apr 2019: Our paper EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE has been accepted to ICML 2019. I am also going to give an invited talk at Negative dependence in ML workshop at ICML.
Nov 2018: Our paper Advances in Variational Inference, has been accepted to IEEE Transactions on Pattern Analysis and Machine Intelligence. Also, one paper accepted for AAAI, one for WACV, one for AISTATS 2019.
Sep 2018: I co-organize symposium on Advances in Approximate Bayesian Inference. Welcome to submit papers and attend the symposium
Aug 2018. One paper accepted for CoNLL and one paper accepted for ICTAI.
Aug 2018: I review for ICRA, CVPR, ICLR, ICML, NIPS, ECCV 2018, AAAI 2019
17 Sep, 2017: Co-organizer, NIPS 2017 Workshop on Advances on Approximate Bayesian Inference
4 Sep, 2017: Our new paper “Perturbative Black Box Variational Inference” has got accepted in NIPS. See you in California.
12 June, 2017: Our new paper “Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification” has got accepted in UAI for a plenary talk. See you in Sydney.
2 May, 2017: I review for CVPR, ICML, ICCV, NIPS 2017
9 Jan, 2017: I joined Disney Research Pittsburgh (still part time affiliated with KTH).
30 Nov, 2016: I gave an invited lecture on Machine Learning in HiQ Stockholm today. I am very glad to see so many ppl that are interested.
3 Nov, 2016: I am invited to give a talk on Nov 9th, at IDA Machine Learning Seminars, Linköping University.
26 Sep, 2016: I have passed my dissertation defense and received my Ph.D! Special thanks for my opponent Prof. Jordan Boyd-Graber; my committee members: Dr. Philipp Hennig, Dr. Silvia Chiappa and Prof. Mattias Villani; my chairman Prof. Petter Ögren and my supervisor Prof. Hedvig Kjellstrom.
11 July, 2016: Our paper “Inter-Battery Topic Representation Learning” got accepted in the European Conference on Computer Vision (ECCV) 2016. See you there.
17 June, 2016: Our paper “Diagnostic Prediction Using Discomfort Drawing with IBTM” got accepted in Machine Learning in Health Care Conference.
23 May, 2016: I will present our work “Diagnostic Prediction Using Discomfort Drawing with Multimodal Topic Model” in UAI Machine Leaning for Health workshop.
10 May, 2016: I review for ECCV 2016.
Jan 1, 2016: I received 1 200 000 SEK grant from Stiftelsen Promobilia for 2 years research on robust inference for computer vision tasks.
Nov 30, 2016: I had a Bay area trip during Nov 18th~ Nov 26th and visited multiple labs in Stanford University and University of California, Berkeley.
Nov 30, 2015: Joint project with MD.PhD Bo Christer Bertilson from KI on Patient-centred Decision Support has got Vinnova UDI step 1 support with 444 000 SEK.
July 10, 2015: I received 150 000 SEK grant from Stiftelsen Promobilia for automatic object and scene understanding.
Oct 7, 2014: Our project EyeCam won KTH innovation compitition and got 25 000 SEK award. This project will be led by Me and Dr. Yasemin Bekiroglu.
Sep 27, 2014: I visited The University of Sheffield and The University of Manchester for a week. It was nice to meet Dr. James Hensman, Prof. Neil Lawrence and Prof. Magnus Rattray.
Sep 19, 2014: Research intern in Infer.NET group in Microsoft Research Cambridge. (June 30th, 2014 ~ Sep 19th, 2014). Great time being hosted by John Bronskill, Yordan Zaykov andTom Minka.