Workshop
Machine Learning for Health (ML4H) - What Parts of Healthcare are Ripe for Disruption by Machine Learning Right Now?
Jason Fries · Alex Wiltschko · Andrew Beam · Isaac S Kohane · Jasper Snoek · Peter Schulam · Madalina Fiterau · David Kale · Rajesh Ranganath · Bruno Jedynak · Michael Hughes · Tristan Naumann · Natalia Antropova · Adrian Dalca · SHUBHI ASTHANA · Prateek Tandon · Jaz Kandola · Uri Shalit · Marzyeh Ghassemi · Tim Althoff · Alexander Ratner · Jumana Dakka

Fri Dec 8th 08:00 AM -- 06:30 PM @ 104 A
Event URL: https://ml4health.github.io/2017/ »

The goal of the NIPS 2017 Machine Learning for Health Workshop (ML4H) is to foster collaborations that meaningfully impact medicine by bringing together clinicians, health data experts, and machine learning researchers. We aim to build on the success of the last two NIPS ML4H workshops which were widely attended and helped form the foundations of a new research community.

This year’s program emphasizes identifying previously unidentified problems in healthcare that the machine learning community hasn't addressed, or seeing old challenges through a new lens. While healthcare and medicine are often touted as prime examples for disruption by AI and machine learning, there has been vanishingly little evidence of this disruption to date. To interested parties who are outside of the medical establishment (e.g. machine learning researchers), the healthcare system can appear byzantine and impenetrable, which results in a high barrier to entry. In this workshop, we hope to reduce this activation energy by bringing together leaders at the forefront of both machine learning and healthcare for a dialog on areas of medicine that have immediate opportunities for machine learning. Attendees at this workshop will quickly gain an understanding of the key problems that are unique to healthcare and how machine learning can be applied to addressed these challenges.

The workshop will feature invited talks from leading voices in both medicine and machine learning. A key part of our workshop is the clinician pitch; a short presentation of open clinical problems where data-driven solutions can make an immediate difference. This year’s program will also include spotlight presentations and two poster sessions highlighting novel research contributions at the intersection of machine learning and healthcare. The workshop will conclude with an interactive a panel discussion where all speakers respond to questions provided by the audience.

08:00 AM Welcome and opening remarks (Talk)
08:20 AM Keynote: Zak Kohane, Harvard DBMI (Talk) Isaac S Kohane
08:55 AM Jennifer Chayes, Microsoft Research New England (Talk) Jennifer Chayes
09:20 AM Keynote: Susan Murphy, U. Michigan (Talk) Susan Murphy
09:55 AM Contributed spotlights (Spotlight)
10:20 AM Coffee break and Poster Session I (Poster Session)
Nishith Khandwala, Steve Gallant, Greg Way, Aniruddh Raghu, Li Shen, Aydan Gasimova, Alican Bozkurt, Willie Boag, Daniel Lopez-Martinez, Ulrich Bodenhofer, Samaneh Nasiri GhoshehBolagh, Michelle Guo, Christoph Kurz, Kirubin Pillay, Kimis Perros, George H Chen, Alexandre Yahi, Madhumita Sushil, Sanjay Purushotham, Elena Tutubalina, Tejpal Virdi, Marc-Andre Schulz, Samuel Weisenthal, Bharat Srikishan, Petar Veličković, Kartik Ahuja, Andrew Miller, Erin Craig, Disi Ji, Filip Dabek, Chloé Pou-Prom, Hejia Zhang, Janani Kalyanam, Wei-Hung Weng, Harish Bhat, Hugh Chen, Simon Kohl, Mingwu Gao, Tingting Zhu, Ming-Zher Poh, Iñigo Urteaga, Antoine Honoré, Alessandro De Palma, Maruan Al-Shedivat, Pranav Rajpurkar, Matthew McDermott, Vincent Chen, Yanan Sui, Yun-Geun Lee, Li-Fang Cheng, David Fang, Sibt Hussain, Cesare Furlanello, Zeev Waks, Hiba Chougrad, Hedvig Kjellstrom, Finale Doshi-Velez, Wolfgang Fruehwirt, Yanqing Zhang, Lily Hu, Junfang Chen, Sunho Park, Gatis Mikelsons, Jumana Dakka, Stephanie Hyland, yann chevaleyre, Hyunwoo Lee, Xavi Giro-i-Nieto, David Kale, Mike Hughes, Gabriel Erion, Rishab Mehra, William Zame, Stojan Trajanovski, Prithwish Chakraborty, Kelly Peterson, Muktabh Srivastava, Amy Jin, Helio Tejeda Lemus, Priyadip Ray, Tamas Madl, Joseph Futoma, Enhao Gong, Syed Rameel Ahmad, Eric Lei, Ferdinand Legros
10:50 AM Invited clinical panel (Panel Discussion) Enrique Velazquez, James Priest, irina strigo
11:50 AM Keynote II: Fei-Fei Li, Stanford (Talk) Li Fei-Fei
01:30 PM Interactive panel (Panel Discussion)
02:30 PM Jill Mesirov, UCSD (Talk) Jill Mesirov
02:55 PM Greg Corrado, Google (Talk) Greg Corrado
03:20 PM Coffee break and Poster Session II (Poster Session)
Mohamed Kane, Albert Haque, Vagelis Papalexakis, John Guibas, Peter Li, Carlos Arias, Eric Nalisnick, Padhraic Smyth, Frank Rudzicz, Xia Zhu, Ted Willke, Noemie Elhadad, hansisnow Raffauf, hsuresh Suresh, Paroma Varma, Yisong Yue, Oggi Rudovic, Evidation Foschini, Syed Rameel Ahmad, Hasham ul Haq, Valerio Maggio, Giuseppe Jurman, Sonali Parbhoo, Pouya Bashivan, Jyoti Islam, Mirco Musolesi, Chris Wu, Alexander Ratner, Jared Dunnmon, Cristóbal Esteban, Aram Galstyan, Greg Ver Steeg, Hrant Khachatrian, Marc Górriz, Mihaela van der Schaar, Anton Nemchenko, Manasi Patwardhan, Tanay Tandon
03:50 PM Award session + A word from our affiliates (Award Session)
04:10 PM Mihaela Van Der Schaar, Oxford (Talk)
04:35 PM Networking Break (Break)
05:00 PM Jure Leskovec, Stanford (Talk) Jure Leskovec
05:25 PM Keynote: Atul Butte (Talk) Atul Butte
06:00 PM Closing Remarks (Talk)

Author Information

Jason Fries (Stanford University)
Alex Wiltschko (Google)
Andrew Beam (Harvard Medical School)
Isaac S Kohane (Harvard Medical School)
Jasper Snoek (University of Toronto)
Peter Schulam (Johns Hopkins University)

Peter Schulam is a PhD student in computer science at Johns Hopkins University. His research interests include machine learning and its applications to healthcare. Peter has made methodological contributions to advancing the use of electronic health data for individualizing care in chronic diseases. His current work explores applications in autoimmune diseases. He has won the National Science Foundation (NSF) Graduate Research Fellowship and the Whiting School of Engineering Centennial Fellowship. He is working with Prof. Suchi Saria for his PhD. Prior to that, he received his master’s from Carnegie Mellon University and his bachelor’s from Princeton University.

Madalina Fiterau (UMass Amherst)

Madalina Fiterau is an Assistant Professor at the College of College of Information and Computer Sciences at UMass Amherst, with a focus on AI/ML. Previously, she was a Postdoctoral Fellow in the Computer Science Department at Stanford University, working with Professors Chris Ré and Scott Delp in the Mobilize Center. Madalina has obtained a PhD in Machine Learning from Carnegie Mellon University in September 2015, advised by Professor Artur Dubrawski. The focus of her PhD thesis, entitled “Discovering Compact and Informative Structures through Data Partitioning”, is learning interpretable ensembles, with applicability ranging from image classification to a clinical alert prediction system. Madalina is currently expanding her research on interpretable models, in part by applying deep learning to obtain salient representations from biomedical “deep” data, including time series, text and images. Madalina is the recipient of the GE Foundation Scholar Leader Award for Central and Eastern Europe. She is the recipient of the Marr Prize for Best Paper at ICCV 2015 and of Star Research Award at the Annual Congress of the Society of Critical Care Medicine 2016. She has organized two editions of the Machine Learning for Clinical Data Analysis Workshop at NIPS, in 2013 and 2014.

David Kale (University of Southern California)
Rajesh Ranganath (Princeton University)

Rajesh Ranganath is a PhD candidate in computer science at Princeton University. His research interests include approximate inference, model checking, Bayesian nonparametrics, and machine learning for healthcare. Rajesh has made several advances in variational methods, especially in popularising black-box variational inference methods that automate the process of inference by making variational inference easier to use while providing more scalable, and accurate posterior approximations. Rajesh works in SLAP group with David Blei. Before starting his PhD, Rajesh worked as a software engineer for AMA Capital Management. He obtained his BS and MS from Stanford University with Andrew Ng and Dan Jurafsky. Rajesh has won several awards and fellowships including the NDSEG graduate fellowship and the Porter Ogden Jacobus Fellowship, given to the top four doctoral students at Princeton University.

Bruno Jedynak (Portland state university)
Mike Hughes (Tufts University)
Tristan Naumann (Microsoft Research)
Natalia Antropova (The University of Chicago)
Adrian Dalca (MIT)
Shubhi ASTHANA (IBM Almaden Research Center)
Prateek Tandon (UCSD)

Prateek Tandon is a postdoctoral candidate in the Psychiatry department at the University of California, San Diego. He obtained his PhD in Robotics from Carnegie Mellon University with a dissertation on Bayesian sensor fusion. His current research interests involve exploring the application of machine learning and deep learning to complex, nonlinear, and multimodal data problems in genetics and bioinformatics. His postdoctoral research is on utilizing machine learning to predict the pathogenicity of genetic structural variation for neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) and schizophrenia.

Jaz Kandola (Imperial College London)
Uri Shalit (Technion)
Marzyeh Ghassemi (University of Toronto)
Tim Althoff (Stanford Univesity)
Alexander Ratner (Stanford)
Jumana Dakka (Rutgers University)

Interested in fMRI deep learning

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