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Contributed Work
Thaer Moustafa Dieb · Aditya Balu · Amir H. Khasahmadi · Viraj Shah · Boris Knyazev · Payel Das · Garrett Goh · Georgy Derevyanko · Gianni De Fabritiis · Reiko Hagawa · John Ingraham · David Belanger · Jialin Song · Kim Nicoli · Miha Skalic · Michelle Wu · Niklas Gebauer · Peter Bjørn Jørgensen · Ryan-Rhys Griffiths · Shengchao Liu · Sheshera Mysore · Hai Leong Chieu · Philippe Schwaller · Bart Olsthoorn · Bianca-Cristina Cristescu · Wei-Cheng Tseng · Seongok Ryu · Iddo Drori · Kevin Yang · Soumya Sanyal · Zois Boukouvalas · Rishi Bedi · Arindam Paul · Sambuddha Ghosal · Daniil Bash · Clyde Fare · Zekun Ren · Ali Oskooei · Minn Xuan Wong · Paul Sinz · Théophile Gaudin · Wengong Jin · Paul Leu

Sat Dec 08 05:00 AM -- 02:30 PM (PST) @

Author Information

Thaer Moustafa Dieb (National Institute for Materials Science)
Aditya Balu (Iowa State University)
Amir H. Khasahmadi (University of Toronto)
Viraj Shah (Iowa State University)
Boris Knyazev (University of Guelph)
Payel Das (IBM Research)
Garrett Goh (PNNL)
Georgy Derevyanko (Concordia University)
Gianni De Fabritiis (University Pompeu Fabra)
Reiko Hagawa (Panasonic)
John Ingraham (Massachusetts Institute of Technology)
David Belanger (Google Brain)
Jialin Song (Caltech)
Kim Nicoli (Techinsche Universität Berlin)
Miha Skalic (Universitat Pompeu Fabra)
Michelle Wu (Stanford University)
Niklas Gebauer (Technische Universität Berlin)
Peter Bjørn Jørgensen (Technical University of Denmark)
Ryan-Rhys Griffiths (University of Cambridge)
Shengchao Liu (UW-Madison)
Sheshera Mysore (University of Massachusetts Amherst)
Hai Leong Chieu (DSO National Laboratories)
Philippe Schwaller (University of Cambridge / IBM Research)
Bart Olsthoorn (NORDITA)
Bianca-Cristina Cristescu (ETH Zurich)
Wei-Cheng Tseng (National Tsing Hua University)
Wei-Cheng Tseng

I am an incoming PhD student at the University of Toronto advised by Prof. Florian Shkurti. I am interested in Computer Vision, Robot Learning and Multi-Agent System. Previously, I worked with Prof. Phillip Isola on offline learning in a multi-agent scenario. Also, I joined the project working on an adaptation approach in multi-agent reinforcement learning, supervised by Dr. Da-Cheng Juan and Dr. Wei Wei. I graduated with B.S. and M.S. from the Electrical Engineering Department at National Tsing Hua University, supervised by Prof. Min Sun.

Seongok Ryu (KAIST)
Iddo Drori (Columbia University and NYU)

Iddo Drori is a visiting Associate Professor in the School of Operations Research and Information Engineering at Cornell University and adjunct Associate Professor in the Department of Computer Science at Columbia University. Between 2017-2019 he was a research scientist and adjunct Professor at NYU Center for Data Science and Courant Institute while teaching at Columbia University and NYU Tandon. Between 2016-2017 he was a senior lecturer in Computer Science at Colman and lecturer at Tel Aviv University. He did his MSc and BSc in Computer Science and Mathematics at the Hebrew University with honors in the special program for outstanding students, his PhD in Computer Science at Tel Aviv University, and post-doc in Statistics at Stanford University. In the past year he has published 13 new publications in automated machine learning; meta-learning for graph algorithms; synthesizing language, vision, and audio; and protein structure prediction. He also served on five program committees in the past year. In the past two years he taught 13 courses on Deep Learning, Data Science, Machine Learning, and Optimization. Iddo has industry experience, and between 2011-2016 founded and served as CEO of a data science start-up acquired in 2017, after working as a research scientist for companies acquired by Anaplan, Daz3D, and Apple. Iddo enjoys teaching and received awards for mentoring the best capstone project at Colman, best teaching evaluations at Tel Aviv University, and mentored the winning teams in the ICCV 2019 Learning to Drive Challenge in the Deep Learning course at Columbia University. He also spends his time writing a forthcoming book titled The Science of Deep Learning to be published by Cambridge University Press.

Kevin Yang (California Institute of Technology)
Soumya Sanyal (IISc, Bangalore)
Zois Boukouvalas (University of Maryland, College Park)
Rishi Bedi (Stanford University)
Arindam Paul (Northwestern University)
Sambuddha Ghosal (Iowa State University)
Daniil Bash (Institute of Materials Research and Engineering)
Clyde Fare (IBM)

Research Staff working at the interface of chemistry and machine learning. With an emphasis on Bayesian optimisation, deep graph neural networks, transfer learning, task selection in multitask networks.

Zekun Ren (Singapore MIT Alliance for Research and Technology)
Ali Oskooei (IBM Research Zurich Laboratory)
Minn Xuan Wong (DSO National Laboratories)
Paul Sinz (Michigan State University)
Théophile Gaudin (IBM Research)
Wengong Jin (MIT CSAIL)
Paul Leu (University of Pittsburgh)

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