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Poster Session I
Aniruddh Raghu · Daniel Jarrett · Kathleen Lewis · Elias Chaibub Neto · Nicholas Mastronarde · Shazia Akbar · Chun-Hung Chao · Henghui Zhu · Seth Stafford · Luna Zhang · Jen-Tang Lu · Changhee Lee · Adityanarayanan Radhakrishnan · Fabian Falck · Liyue Shen · Daniel Neil · Yusuf Roohani · Aparna Balagopalan · Brett Marinelli · Hagai Rossman · Sven Giesselbach · Jose Javier Gonzalez Ortiz · Edward De Brouwer · Byung-Hoon Kim · Rafid Mahmood · Tzu Ming Hsu · Antonio Ribeiro · Rumi Chunara · Agni Orfanoudaki · Kristen Severson · Mingjie Mai · Sonali Parbhoo · Albert Haque · Viraj Prabhu · Di Jin · Alena Harley · Geoffroy Dubourg-Felonneau · Xiaodan Hu · Maithra Raghu · Jonathan Warrell · Nelson Johansen · Wenyuan Li · Marko Järvenpää · Satya Narayan Shukla · Sarah Tan · Vincent Fortuin · Beau Norgeot · Yi-Te Hsu · Joel H Saltz · Veronica Tozzo · Andrew Miller · Guillaume Ausset · Azin Asgarian · Francesco Paolo Casale · Antoine Neuraz · Bhanu Pratap Singh Rawat · Turgay Ayer · Xinyu Li · Mehul Motani · Nathaniel Braman · Laetitia M Shao · Adrian Dalca · Hyunkwang Lee · Emma Pierson · Sandesh Ghimire · Yuji Kawai · Owen Lahav · Anna Goldenberg · Denny Wu · Pavitra Krishnaswamy · Colin Pawlowski · Arijit Ukil · Yuhui Zhang

Sat Dec 08 08:30 AM -- 09:30 AM (PST) @
in Machine Learning for Health (ML4H): Moving beyond supervised learning in healthcare »

Author Information

Aniruddh Raghu (Massachusetts Institute of Technology)
Daniel Jarrett (University of Oxford)
Kathleen Lewis (MIT)
Elias Chaibub Neto (Sage Bionetworks)
Nicholas Mastronarde (University at Buffalo)

Nick Mastronarde is an Associate Professor in the Department of Electrical Engineering at the University at Buffalo. He received his Ph.D. degree in Electrical Engineering at the University of California, Los Angeles (UCLA) in 2011 and his B.S. and M.S. degrees in Electrical Engineering from the University of California, Davis in 2005 (Highest Honors, Department Citation) and 2006, respectively. He has been the recipient of several awards and honors including a first year department fellowship through the Electrical Engineering department at UCLA, the Dissertation Year Fellowship through the Graduate Division at UCLA, and the Dimitris N. Chorafas Foundation Award for 2011. He has spent four summers (2013, 2015, 2016, 2018) as a faculty fellow at the US Air Force Research Laboratory (AFRL) Information Directorate in Rome, NY. In the summer of 2010, he was a graduate intern at IBM Research Watson Lab in the Exploratory Stream Analytics group where he developed learning algorithms for discovering anomalies in massive volumes of streaming data. In the summer of 2007, he was a graduate student intern at Intel Corporation in the Graphics Architecture Team where he developed and patented an algorithm enabling the selective use of fractional and bidirectional video motion estimation in an H.264/AVC encoder. Prof. Mastronarde's research interests are in the areas of resource allocation and scheduling in wireless networks and systems, UAV networks, 4G/5G networks, dynamic power management, cross-layer design and optimization, Markov decision processes (MDPs), and reinforcement learning.

Shazia Akbar (Sunnybrook Research Institute)
Chun-Hung Chao (National Tsing Hua University)
Henghui Zhu (Boston University)
Seth Stafford (ML/NLP Engineering at ServiceNow)

Math PhD in stochastic processes from Cornell, held an NSF post-doctoral fellowship at MIT. Built many enterprise apps, now applying ML/NLP to solve business problems at scale. Side interest in Finance/trading signals (passed all 3 CFA exams).

Luna Zhang (BigBear, Inc.)
Jen-Tang Lu (MGH & BWH Center for Clinical Data Science)
Changhee Lee (University of California, Los Angeles)
Adityanarayanan Radhakrishnan (MIT)
Fabian Falck (Carnegie Mellon University)
Liyue Shen (Stanford University)
Daniel Neil (BenevolentAI)

Daniel Neil is a machine learning researcher who is passionate about bringing transformative technologies to the world. After a foundation in biomedical computation at Stanford, Daniel worked as a technology consultant with Accenture in Silicon Valley before obtaining a Ph.D. in Switzerland at ETH Zurich in machine learning algorithms and neuroscience. At BenevolentAI, he helped to build the New York office's research team and direction. He is the author of more than three dozen publications and patents in research areas spanning biologically-motivated machine learning, methods development, and knowledge graph completion. At BenevolentAI Dan focuses on integrating research teams across information extraction, precision medicine, gene prioritization, and chemistry optimization to deploy machine learning algorithms that improve each step of the drug discovery process.

Yusuf Roohani (GSK)
Aparna Balagopalan (University of Toronto)
Brett Marinelli (Mount Sinai)
Hagai Rossman (Weizmann Institute of Science)
Sven Giesselbach (Fraunhofer IAIS)
Jose Javier Gonzalez Ortiz (MIT)
Edward De Brouwer (KU Leuven)
Byung-Hoon Kim (Korea Advanced Institute of Science and Technology (KAIST))

Byung-Hoon Kim

Rafid Mahmood (University of Toronto)
Tzu Ming Hsu (MIT)
Antonio Ribeiro (UFMG / Uppsala university)
Rumi Chunara (New York University)
Agni Orfanoudaki (Massachusetts Institute of Technology)
Kristen Severson (IBM Research)
Mingjie Mai (University of Toronto)
Sonali Parbhoo (University of Basel)
Albert Haque (Stanford University)
Viraj Prabhu (Georgia Tech)
Viraj Prabhu

I am a fourth year CS Ph.D. student at Georgia Tech, advised by Judy Hoffman. My research interests are in developing data-efficient and reliable computer vision systems that can be deployed in the real world. Specifically, I am interested in sample-efficient learning (particularly few-shot and active learning), adaptation across visual tasks and domains, and reliable and calibrated uncertainty estimation from deep neural networks.

Di Jin (MIT)
Alena Harley (Human Longevity Inc.)
Geoffroy Dubourg-Felonneau (Cambridge cancer genomics)
Xiaodan Hu (University of Waterloo)
Maithra Raghu (Cornell University and Google Brain)
Jonathan Warrell (Yale University)
Nelson Johansen (University of California, Davis)
Wenyuan Li (UCLA)
Marko Järvenpää (Aalto University)
Satya Narayan Shukla (University of Massachusetts Amherst)
Sarah Tan (Cornell University / UCSF)

Research scientist at Facebook working on causal inference and interpretability

Vincent Fortuin (ETH Zürich)

Research Fellow at St John's College, University of Cambridge. Incoming group leader at Helmholtz AI in Munich.

Beau Norgeot (UCSF)
Yi-Te Hsu (Academia Sinica)
Joel H Saltz (Stony Brook University)
Veronica Tozzo (University of Genoa)
Andrew Miller (Columbia)
Guillaume Ausset (Télécom Paristech)
Azin Asgarian (University of Toronto)
Francesco Paolo Casale (Microsoft Research)
Antoine Neuraz (APHP / INSERM / LIMSI)
Bhanu Pratap Singh Rawat (UMass Amherst)
Turgay Ayer (Georgia Institute of Technology)

Turgay Ayer is the George Family Foundation Early Career Professor in H. Milton Stewart School of Industrial and Systems Engineering and is the Director of Business Intelligence and Healthcare Analytics at the Center for Health and Humanitarian Systems at at Georgia Institute of Technology. In addition, Dr. Ayer has a courtesy appointment at Emory Medical School. His research focuses on socially responsible operations and practice-focused research, with a particular emphasis on healthcare analytics. His research papers have been published in top tier management, engineering and medical journals, and covered by popular media outlets, including the Wall Street Journal, Washington Post, US News, and NPR. Dr. Ayer has received over $2 million grant funding and several awards for his work, including an NSF CAREER Award (2015), first place in the MSOM Best Practice-Based Research Competition (2017), INFORMS Franz Edelman Laureate Award (2017), and Society for Medical Decision Making Lee Lusted Award (2009). Ayer serves an associate editor for Operations Research and MSOM (Special Issue), and is a past president of the INFORMS Health Application Society. He received a B.S. in industrial engineering from Sabanci University in Istanbul, Turkey, and his M.S. and Ph.D. degrees in industrial and Systems Engineering from the University of Wisconsin - Madison.

Xinyu Li (Carnegie Mellon University)
Mehul Motani (National University of Singapore)

Mehul Motani received the B.E. degree from Cooper Union, New York, NY, the M.S. degree from Syracuse University, Syracuse, NY, and the Ph.D. degree from Cornell University, Ithaca, NY, all in Electrical and Computer Engineering. Dr. Motani is currently an Associate Professor in the Electrical and Computer Engineering Department at the National University of Singapore (NUS) and a Visiting Research Collaborator at Princeton University. Previously, he was a Visiting Fellow at Princeton University. He was also a Research Scientist at the Institute for Infocomm Research in Singapore, for three years, and a Systems Engineer at Lockheed Martin in Syracuse, NY for over four years. His research interests include information theory, machine learning, wireless and sensor networks, and energy harvesting communications. Dr. Motani was the recipient of the Intel Foundation Fellowship for his Ph.D. research, the NUS Annual Teaching Excellence Award, the NUS Faculty of Engineering Innovative Teaching Award, and the NUS Faculty of Engineering Teaching Honours List Award. He actively participates in the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM). He is a Fellow of the IEEE and has served as the Secretary of the IEEE Information Theory Society Board of Governors. He has served as an Associate Editor for both the IEEE Transactions on Information Theory and the IEEE Transactions on Communications. He has also served on the Organizing and Technical Program Committees of numerous IEEE and ACM conferences.

Nathaniel Braman (Case Western Reserve University)
Laetitia M Shao (Stanford University)
Adrian Dalca (MIT)
Hyunkwang Lee (Harvard)
Emma Pierson (Stanford)
Sandesh Ghimire (Rochester Institute of Technology)
Yuji Kawai (Osaka University)
Owen Lahav (University of Oxford)
Anna Goldenberg (SickKids/University of Toronto)

Dr Goldenberg is a Senior Scientist in Genetics and Genome Biology program at SickKids Research Institute, recently appointed as the first Varma Family Chair in Biomedical Informatics and Artificial Intelligence. She is also an Associate Professor in the Department of Computer Science at the University of Toronto, faculty member and an Associate Research Director, Health at Vector Institute and a fellow at the Canadian Institute for Advanced Research (CIFAR), Child and Brain Development group. Dr Goldenberg trained in machine learning at Carnegie Mellon University, with a post-doctoral focus in computational biology and medicine. The current focus of her lab is on developing machine learning methods that capture heterogeneity and identify disease mechanisms in complex human diseases as well as developing risk prediction and early warning clinical systems. Dr Goldenberg is a recipient of the Early Researcher Award from the Ministry of Research and Innovation. She is strongly committed to creating responsible AI to benefit patients across a variety of conditions.

Denny Wu (University of Toronto & Vector Institute)
Pavitra Krishnaswamy (Institute for Infocomm Research)
Colin Pawlowski (MIT)
Arijit Ukil (Tata Consultancy Services, Kolkata, India)
Yuhui Zhang (Tsinghua University)

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