ML Interpretability for Scientific Discovery
Program
Program Schedule
All events will be streamed live via Zoom webinar and Youtube.
Registered ICML attendees please join us at the webinar link here.
Event will be streamed live on Youtube here: https://www.youtube.com/watch?v=0Q-4EQriYJs
Add MLI4SD @ ICML2020 to your calendar. (links to individual events are below)
Invited Speakers
Chicago Booth
Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at Chicago Booth. His current research uses machine learning to understand complex problems in human behavior, social policy, and especially medicine, where computational techniques have the potential to uncover biomedical insights from large-scale health data. In addition to being a co-PI at the joint Berkeley-UChicago Laboratory for Systems Medicine, Mullainathan is the cofounder of the computational medicine initiative, Nightingale. More here.
Cornell
Eun-Ah Kim obtained B.S. and M.S. in Physics from Seoul National University, South Korea and PhD from University of Illinois at Urbana-Champaign in 2005. In 2008, she started her research group at the Cornell University, where she is currently a professor. Dr. Kim’s expertise is in the theory of quantum correlated matter, especially in the subject areas of exotic superconductivty and topological phases. She is known for her breadth and for her synergetic interaction with experimental colleagues. Since 2016, she has been pioneering application of machine learning to the study of correlated quantum matter.
Caltech
Katie Bouman is a Rosenberg Scholar and Assistant Professor of Computing and Mathematical Sciences (CMS) and Electrical Engineering at Caltech in Pasadena, California. Her research focuses on computational imaging: designing systems that tightly integrate algorithm and sensor design, making it possible to observe phenomena previously difficult or impossible to measure with traditional approaches. Her group at Caltech combines ideas from signal processing, computer vision, machine learning, and physics to find and exploit hidden signals for both scientific discovery and technological innovation.
Arunachalam Narayanaswamy is a Software Engineer in Google AI, working on applying machine learning to big data problems in physical sciences with particular focus on interpretable models that help lead to scientific discoveries. He obtained his B.Tech. from Indian Institute of Madras, Chennai in 2006. He obtained his M.S. in 2008 and Ph.D. from Rensselaer Polytechnic Institute (RPI), Troy, N.Y. in 2011 under Badrinath Roysam working on Image Analysis and Computer Vision algorithms for images in microscopy. He has served as a reviewer for several of computer vision / medical imaging journals like TPAMI, TBME, TMI. Google Scholar.
Princeton
Barbara E. Engelhardt, an associate professor, joined the Princeton Computer Science Department in 2014 from Duke University, where she had been an assistant professor in Biostatistics and Bioinformatics and Statistical Sciences. She graduated from Stanford University and received her Ph.D. from the University of California, Berkeley, advised by Professor Michael Jordan. She did postdoctoral research at the University of Chicago, working with Professor Matthew Stephens, and three years at Duke University as an assistant professor. Interspersed among her academic experiences, she spent two years working at the Jet Propulsion Laboratory, a summer at Google Research, and a year at 23andMe, a DNA ancestry service. Professor Engelhardt received an NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, and the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution. As a faculty member, she received the NIH NHGRI K99/R00 Pathway to Independence Award, a Sloan Faculty Fellowship, and an NSF CAREER Award. Professor Engelhardt’s research interests involve developing statistical models and methods for the analysis of high-dimensional biomedical data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human disease.
Accepted Papers
Shusen Liu (Lawrence Livermore National Laboratory)*; Bhavya Kailkhura (Lawrence Livermore National Laboratory); Jize Zhang (Lawrence Livermore National Laboratory); Anna Hiszpanski (Lawrence Livermore National Laboratory); Emily Robertson (Lawrence Livermore National Laboratory); Donald Loveland (Lawrence Livermore National Laboratory); T. Yong-Jin Han (LLNL)
William McCorkindale (University of Cambridge); David P Kovacs (University of Cambridge)*; Alpha Lee (University of Cambridge)
Kshitij Tayal (University of Minnesota)*; Chieh-Hsin Lai (University of Minnesota, Twin Cities); Raunak Manekar (University of Minnesota); Vipin Kumar (University of Minnesota); Ju Sun (University of Minnesota)
Jesse Vig (Salesforce)*; Ali Madani (Salesforce Research); Lav Varshney (UIUC: ECE); Nazneen Fatema Rajani (Salesforce Research)
Explaining Chemical Toxicity Using Missing Features [pdf]
Kar Wai Lim (IBM Singapore)*; Bhanushee Sharma (Rensselaer Polytechnic Institute); Vijil Chenthamarakshan (IBM AI Research); Payel Das (IBM Research); Jonathan S. Dordick (Rensselaer Polytechnic Institute)
Aishwarya H. Balwani (Georgia Institute of Technology)*; Eva Dyer (Georgia Tech)
Joshua Yao-Yu Lin (Physics department, University of Illinois at Urbana-Champaign)*; George Wong (University of Illinois at Urbana-Champaign); Ben Prather (University of Illinois at Urbana-Champaign); Charles Gammie (University of Illinois at Urbana-Champaign)
Weishun Zhong (Massachusetts Institute of Technology)*; Jacob Gold (Massachusetts Institute of Technology); Sarah Marzen (Massachusetts Institute of Technology; Claremont Colleges); Jeremy L England (GlaxoSmithKline); Nicole Yunger Halpern (Harvard University; Massachusetts Institute of Technology )
Sarthak Bhagat (IIIT-Delhi)*; Vishaal Udandarao (IIIT Delhi); Shagun Uppal (IIIT-Delhi)
Look at the Loss: Towards Robust Detection of False Positive Feature Interactions Learned by Neural Networks on Genomic Data [pdf] [video]
Mara R Finkelstein (Stanford University); Avanti Shrikumar (Stanford University)*; Anshul Kundaje (Stanford University)
Vikram Sundar (Google); Lucy Colwell (Google)*
In-Distribution Interpretability for Challenging Modalities [pdf]
Cosmas Heiss (TU Berlin)*; Ron Levie (TU Berlin); Cinjon Resnick (NYU); Gitta Kutyniok (Technische Universität Berlin); Joan Bruna (Courant Institute of Mathematical Sciences, NYU, USA)
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning [pdf] [video]
Peter Y Lu (MIT)*; Samuel Kim (MIT); Marin Soljacic (MIT)
Deepak Sharma (MILA, McGill University)*; Audrey Durand (Université Laval); Marc-Andre Legault (Université de Montréal); Louis-Philippe Lemieux (Montreal Heart Institute); Audrey Lemacon (Montreal Heart Institute); Marie-Pierre Dubé (Montreal Heart Institute); Joelle Pineau (McGill / Facebook)
Sébastien Fabbro (NRC Herzberg)*; Kwang Moo Yi (University of Victoria); Teaghan O'Briain (University of Victoria); Kim Venn (University of Victoria); Yuan-Sen Ting (Institute for Advanced Study Princeton); Spencer Bialek (University of Victoria)
Raunak Manekar (University of Minnesota); Kshitij Tayal (University of Minnesota)*; Vipin Kumar (University of Minnesota); Ju Sun (University of Minnesota)
Yu Wu (Princeton University)*; Le Cong (Stanford University); Mengdi Wang (Princeton University/DeepMind)
Philippe PS Schwaller (IBM Research Europe / University of Bern)*; Benjamin Hoover (IBM Research); Jean-Louis Reymond (University of Bern); Hendrik Strobelt (IBM Research); Teodoro Laino (IBM Research Europe)
We received 29 submissions of which the above 18 were selected based on reviews by 2 or more reviewers.