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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

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)
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)

PhD student in Machine Learning at ETH Zürich

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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    Sil C. van de Leemput · Adrian Dalca · Karthik Gopinath
  • 2018 : Lunch »
    Hong Yu · Bhanu Pratap Singh Rawat · Arijit Ukil · Waheeda Saib · Jekaterina Novikova · John Hughes · Yuhui Zhang · Rahul V · Mi Jung Kim · Babak Taati · Hariharan Ravishankar · Harry Clifford · Hirofumi Kobayashi · Babak Taati · Keyang Xu · Yen-Chi Cheng · Timothy Cannings · Jayashree Kalpathy-Cramer · Jayashree Kalpathy-Cramer · Parinaz Sobhani · Kimis Perros · Wei-Hung Weng · Yordan Raykov · Lars Lorch · Mengqi Jin · Xue Teng · Michael Ferlaino · Marek Rei · Cédric Beaulac · Aman Verma · Sebastian Keller · Edmond Cunningham · Luc Evers · Victor Rodriguez · Vipul Satone · Dianbo Liu · Angeline Yasodhara · Geoff Tison · Ligin Solamen · Bryan He · Rahul Ladhania · Yipeng Shi · Md Nafiz Hamid · Pouria Mashouri · Woochan Hwang · Sejin Park · Xu Chen · Rachneet Kaur · Davis Blalock · Holly Wiberg · Parminder Bhatia · Kezi Yu · RUMENG LI · Jun Sakuma · Charles Ding · Aaron Babier · Yong Cai · A Pratap · Luke O'Connor · Allen Nie · Martin Kang · Ian Covert · Xun Wang · Zelun Luo · Serena Yeung · William Boag · Kazuki Tachikawa · Mary Saltz · Owen Lahav · Edward Lee · Eric Teasley · Michael Kamp · Nirmesh Patel · Vishwali Mhasawade · Maxim Samarin · Ryo Uchimido · Farzad Khalvati · Francisco Cruz · Laura Symul · Zaid Nabulsi · Mads Mihailescu · Rosalind Picard
  • 2018 : Poster session: Contributed papers »
    Michael Cvitkovic · Arijit Patra · Yunpeng Li · RAHMAN BANYA SAFF SANYA · Guanghua Chi · Benjamin Huynh · Hamed Alemohammad · Simón Ramírez Amaya · Nazmus Saquib · Jade Abbott · Teo de Campos · Viraj Prabhu · Alvaro Riascos · Hafte Abera · praney dubey · Tanushyam Chattopadhyay · Hsiang Hsu · Mayank Jain · Kartikeya Bhardwaj · Gabriel Cadamuro · Bradley Gram-Hansen · Georg Dorffner
  • 2018 Workshop: Machine Learning for Health (ML4H): Moving beyond supervised learning in healthcare »
    Andrew Beam · Tristan Naumann · Marzyeh Ghassemi · Matthew McDermott · Madalina Fiterau · Irene Y Chen · Brett Beaulieu-Jones · Michael Hughes · Farah Shamout · Corey Chivers · Jaz Kandola · Alexandre Yahi · Samuel Finlayson · Bruno Jedynak · Peter Schulam · Natalia Antropova · Jason Fries · Adrian Dalca · Irene Chen
  • 2018 : Spotlight talks (session 3) »
    Farzaneh Mahdisoltani · Frederik Kratzert · SUBBAREDDY OOTA · Mehul Motani · Tryambak Gangopadhyay · Sathwik Tejaswi Madhusudhan · Marc Rußwurm · Mahta Mousavi · Mihir Jain
  • 2018 Poster: Gaussian Process Prior Variational Autoencoders »
    Francesco Paolo Casale · Adrian Dalca · Luca Saglietti · Jennifer Listgarten · Nicolo Fusi
  • 2018 Poster: Representation Balancing MDPs for Off-policy Policy Evaluation »
    Yao Liu · Omer Gottesman · Aniruddh Raghu · Matthieu Komorowski · Aldo Faisal · Finale Doshi-Velez · Emma Brunskill
  • 2018 Poster: Insights on representational similarity in neural networks with canonical correlation »
    Ari Morcos · Maithra Raghu · Samy Bengio
  • 2018 Poster: 3D-Aware Scene Manipulation via Inverse Graphics »
    Shunyu Yao · Tzu Ming Hsu · Jun-Yan Zhu · Jiajun Wu · Antonio Torralba · Bill Freeman · Josh Tenenbaum
  • 2017 Workshop: Deep Learning: Bridging Theory and Practice »
    Sanjeev Arora · Maithra Raghu · Russ Salakhutdinov · Ludwig Schmidt · Oriol Vinyals
  • 2017 : Contributed talk: Taylor Residual Estimators via Automatic Differentiation »
    Andrew Miller
  • 2017 : Coffee break and Poster Session II »
    Mohamed Kane · Albert Haque · Evangelos Papalexakis · John Guibas · Peter Li · Carlos Arias · Eric Nalisnick · Padhraic Smyth · Frank Rudzicz · Xia Zhu · Theodore Willke · Noemie Elhadad · Hans Raffauf · Harini Suresh · Paroma Varma · Yisong Yue · Ognjen (Oggi) Rudovic · Luca 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
  • 2017 : Poster Session »
    Shunsuke Horii · Heejin Jeong · Tobias Schwedes · Qing He · Ben Calderhead · Ertunc Erdil · Jaan Altosaar · Patrick Muchmore · Rajiv Khanna · Ian Gemp · Pengfei Zhang · Yuan Zhou · Chris Cremer · Maria DeYoreo · Alexander Terenin · Brendan McVeigh · Rachit Singh · Yaodong Yang · Erik Bodin · Trefor Evans · Henry Chai · Shandian Zhe · Jeffrey Ling · Vincent ADAM · Lars Maaløe · Andrew Miller · Ari Pakman · Josip Djolonga · Hong Ge
  • 2017 : Poster spotlights »
    Hiroshi Kuwajima · Masayuki Tanaka · Qingkai Liang · Matthieu Komorowski · Fanyu Que · Thalita F Drumond · Aniruddh Raghu · Leo Anthony Celi · Christina Göpfert · Andrew Ross · Sarah Tan · Rich Caruana · Yin Lou · Devinder Kumar · Graham Taylor · Forough Poursabzi-Sangdeh · Jennifer Wortman Vaughan · Hanna Wallach
  • 2017 : Coffee break and Poster Session I »
    Nishith Khandwala · Steve Gallant · Gregory Way · Aniruddh Raghu · Li Shen · Aydan Gasimova · Alican Bozkurt · William 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 · Chen Fang · Sibt ul 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 · Xavier Giro-i-Nieto · David Kale · Michael Hughes · Gabriel Erion · Rishab Mehra · William Zame · Stojan Trajanovski · Prithwish Chakraborty · Kelly Peterson · Muktabh Mayank Srivastava · Amy Jin · Heliodoro Tejeda Lemus · Priyadip Ray · Tamas Madl · Joseph Futoma · Enhao Gong · Syed Rameel Ahmad · Eric Lei · Ferdinand Legros
  • 2017 : Contributed talk: Beyond Sparsity: Tree-based Regularization of Deep Models for Interpretability »
    Mike Wu · Sonali Parbhoo · Finale Doshi-Velez
  • 2017 : Poster Spotlights »
    Francesco Locatello · Ari Pakman · Da Tang · Thomas Rainforth · Zalan Borsos · Marko Järvenpää · Eric Nalisnick · Gabriele Abbati · XIAOYU LU · Jonathan Huggins · Rachit Singh · Rui Luo
  • 2017 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
  • 2017 Poster: Reducing Reparameterization Gradient Variance »
    Andrew Miller · Nick Foti · Alexander D'Amour · Ryan Adams
  • 2017 Poster: SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability »
    Maithra Raghu · Justin Gilmer · Jason Yosinski · Jascha Sohl-Dickstein
  • 2016 Poster: Exponential expressivity in deep neural networks through transient chaos »
    Ben Poole · Subhaneil Lahiri · Maithra Raghu · Jascha Sohl-Dickstein · Surya Ganguli
  • 2016 Poster: Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences »
    Daniel Neil · Michael Pfeiffer · Shih-Chii Liu
  • 2016 Oral: Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences »
    Daniel Neil · Michael Pfeiffer · Shih-Chii Liu
  • 2015 Workshop: Machine Learning in Computational Biology »
    Nicolo Fusi · Anna Goldenberg · Sara Mostafavi · Gerald Quon · Oliver Stegle
  • 2015 Poster: A Gaussian Process Model of Quasar Spectral Energy Distributions »
    Andrew Miller · Albert Wu · Jeffrey Regier · Jon McAuliffe · Dustin Lang · Mr. Prabhat · David Schlegel · Ryan Adams
  • 2014 Workshop: Machine Learning in Computational Biology »
    Oliver Stegle · Sara Mostafavi · Anna Goldenberg · Su-In Lee · Michael Leung · Anshul Kundaje · Mark B Gerstein · Martin Renqiang Min · Hannes Bretschneider · Francesco Paolo Casale · Loïc Schwaller · Amit G Deshwar · Benjamin A Logsdon · Yuanyang Zhang · Ali Punjani · Derek C Aguiar · Samuel Kaski
  • 2013 Workshop: Machine Learning in Computational Biology »
    Jean-Philippe Vert · Anna Goldenberg · Sara Mostafavi · Oliver Stegle
  • 2012 Workshop: Machine Learning in Computational Biology »
    Jean-Philippe Vert · Anna Goldenberg · Christina Leslie
  • 2011 Workshop: Machine Learning in Computational Biology »
    Jean-Philippe Vert · Gunnar Rätsch · Yanjun Qi · Tomer Hertz · Anna Goldenberg · Christina Leslie
  • 2010 Workshop: Networks Across Disciplines: Theory and Applications »
    Edo M Airoldi · Anna Goldenberg · Jure Leskovec · Quaid Morris