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Poster Session
Pravish Sainath · Mohamed Akrout · Charles Delahunt · Nathan Kutz · Guangyu Robert Yang · Joseph Marino · L F Abbott · Nicolas Vecoven · Damien Ernst · andrew warrington · Michael Kagan · Kyunghyun Cho · Kameron Harris · Leopold Grinberg · John J. Hopfield · Dmitry Krotov · Taliah Muhammad · Erick Cobos · Edgar Walker · Jacob Reimer · Andreas Tolias · Alexander Ecker · Janaki Sheth · Yu Zhang · Maciej Wołczyk · Jacek Tabor · Szymon Maszke · Roman Pogodin · Dane Corneil · Wulfram Gerstner · Baihan Lin · Guillermo Cecchi · Jenna M Reinen · Irina Rish · Guillaume Bellec · Darjan Salaj · Anand Subramoney · Wolfgang Maass · Yueqi Wang · Ari Pakman · Jin Hyung Lee · Liam Paninski · Bryan Tripp · Colin Graber · Alex Schwing · Luke Prince · Gabriel Ocker · Michael Buice · Benjamin Lansdell · Konrad Kording · Jack Lindsey · Terrence Sejnowski · Matthew Farrell · Eric Shea-Brown · Nicolas Farrugia · Victor Nepveu · Jiwoong Im · Kristin Branson · Brian Hu · Ramakrishnan Iyer · Stefan Mihalas · Sneha Aenugu · Hananel Hazan · Sihui Dai · Tan Nguyen · Doris Tsao · Richard Baraniuk · Anima Anandkumar · Hidenori Tanaka · Aran Nayebi · Stephen Baccus · Surya Ganguli · Dean Pospisil · Eilif Muller · Jeffrey S Cheng · Gaël Varoquaux · Kamalaker Dadi · Dimitrios C Gklezakos · Rajesh PN Rao · Anand Louis · Christos Papadimitriou · Santosh Vempala · Naganand Yadati · Daniel Zdeblick · Daniela M Witten · Nicholas Roberts · Vinay Prabhu · Pierre Bellec · Poornima Ramesh · Jakob H Macke · Santiago Cadena · Guillaume Bellec · Franz Scherr · Owen Marschall · Robert Kim · Hannes Rapp · Marcio Fonseca · Oliver Armitage · Jiwoong Im · Thomas Hardcastle · Abhishek Sharma · Wyeth Bair · Adrian Valente · Shane Shang · Merav Stern · Rutuja Patil · Peter Wang · Sruthi Gorantla · Peter Stratton · Tristan Edwards · Jialin Lu · Martin Ester · Yurii Vlasov · Siavash Golkar

Author Information

Pravish Sainath (Mila / University of Montreal)

The purpose of my life and study is to better understand the nature and mechanisms of both natural and artificial intelligence. My broad interests span the intersection between the two vast disciplines of artificial intelligence and neuroscience. I am mostly interested in using computational and cognitive neuroscience to improve deep and reinforcement learning methods and applying deep learning methods on neuroimaging data to better understand the brain. Previously, I was an engineer at the startup [Hammerhead](http://www.hammerhead.io/), where I worked on computing cycling fitness metrics from smart cycling sensors for their flagship product Karoo and developing navigation solutions for the platform.

Mohamed Akrout (University of Toronto)
Charles Delahunt (University of Washington Seattle)
Nathan Kutz (University of Washington)
Guangyu Robert Yang (New York University)
Joseph Marino (California Institute of Technology)
L F Abbott (Columbia University)
Nicolas Vecoven (University of Liège)
Damien Ernst (University of Liège)
andrew warrington (university of oxford / university of british columbia)
Michael Kagan (SLAC / Stanford)
Kyunghyun Cho (New York University)

Kyunghyun Cho is an associate professor of computer science and data science at New York University and a research scientist at Facebook AI Research. He was a postdoctoral fellow at the Université de Montréal until summer 2015 under the supervision of Prof. Yoshua Bengio, and received PhD and MSc degrees from Aalto University early 2014 under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.

Kameron Harris (University of Washington)
Leopold Grinberg (IBM)
John J. Hopfield (Princeton University)

BA Swarthmore 1954; PhD Cornell (theoretical physics) 1958. Member of technical staff Bell Laboratories 1958-1960 & 1973-1996; Faculty positions at UCBerkeley (physics) 1961-1964, Princeton Univ. (physics) 1964-1980, Caltech (chemistry and biology) 1980-1996, Princeton Univ. (molecular biology) 1997-2008, Institute for Advanced Study (2010-2013), now emeritus at Princeton Neuroscience Institute. Served as Chairman of the Faculty, Caltech; President of the American Physical Society; Executive Officer for Computation and Neural Systems, Caltech. Honors include Buckley Prize in Solid State Physics; APS prize in biophysics; Dirac Medal; Einstein Award; MacArthur Fellow; IEEE Rosenblatt Award; Swartz Prize in Computational Neuroscience. Member, National Academy of Science; American Philosophical Society. Research on the interaction of light with solids 1956-1970; biomolecular physics and kinetic proofreading 1970-1980; neural network dynamics and neurobiology 1980-.

Dmitry Krotov (IBM Research)
Taliah Muhammad (Baylor College of Medicine)
Erick Cobos (Baylor College of Medicine)
Edgar Walker (Baylor College of Medicine)
Jacob Reimer (Baylor College of Medicine)
Andreas Tolias (Baylor College of Medicine)
Alexander Ecker (University of Tuebingen)
Janaki Sheth (University of California at Los Angeles)
Yu Zhang (University de Montreal)

IVADO Postdoctoral Fellow working on applying deep learning tools to Neuroscience research Ph.D. in Pattern Recognition and Intelligent Systems * 10 years of experience in Neuroscience research, working with structural, functional and diffusion MRI * skilled coding with Matlab, R, and Python, familiar with the deep learning platform including Tensorflow, Keras and Pytorch * Currently working on the project of predicting human cognition and behaviors based on Neuroimage data and deep learning models You can check my publications here: https://scholar.google.ca/citations?user=lZwQ9mgAAAAJ&hl=en

Maciej Wołczyk (Jagiellonian University)
Jacek Tabor (Jagiellonian University)
Szymon Maszke (Uniwersytet Jagielloński)
Roman Pogodin (University College London)
Dane Corneil (benevolent.ai)
Wulfram Gerstner (EPFL)
Baihan Lin (Columbia University)
Guillermo Cecchi (IBM Research)
Jenna M Reinen (IBM Research)
Irina Rish (MILA / Université de Montréal)
Guillaume Bellec (Graz University of Technology)
Darjan Salaj (Graz University of Technology)
Anand Subramoney (Graz University of Technology)
Wolfgang Maass (Graz University of Technology)
Yueqi Wang (Columbia University)
Ari Pakman (Columbia University)
Jin Hyung Lee (Columbia University)
Liam Paninski (Columbia University)
Bryan Tripp (University of Waterloo)
Colin Graber (University of Illinois at Urbana-Champaign)
Alex Schwing (University of Illinois at Urbana-Champaign)
Luke Prince (University of Toronto)
Gabriel Ocker (Allen Institute for Brain Science)
Michael Buice (Allen Institute for Brain Science)
Benjamin Lansdell (University of Pennsylvania)
Konrad Kording (Upenn)
Jack Lindsey (Columbia University)
Terrence Sejnowski (Salk Institute)
Matthew Farrell (University of Washington)
Eric Shea-Brown (University of Washington)
Nicolas Farrugia (IMT Atlantique)

Nicolas Farrugia obtained his PhD in 2008, on hardware implementation of convolutional neural networks. In 2010, Nicolas Farrugia moved to the field of neurosciences with a focus on music. He uses a wide range of cognitive neuroscience methods such as EEG, functional MRI, as well as behavioral psychology methods and motion capture. In 2015, he joins Telecom Bretagne to engage into a transdisciplinary effort, combining methods from Neuroscience, Deep Learning and hardware implementations.

Victor Nepveu (IMT Atlantique)
Jiwoong Im (Janelia Research Center)
Kristin Branson (Janelia Research Campus, HHMI)
Brian Hu (Allen Institute for Brain Science)
Ramakrishnan Iyer (Allen Institute for Brain Science)
Stefan Mihalas (Allen Institute for Brain Science)
Sneha Aenugu (University of Massachussets Amherst)
Hananel Hazan (University of Massachusetts Amherst)
Sihui Dai (California Institute of Technology)
Tan Nguyen (Rice University)

I am currently a postdoctoral scholar in the Department of Mathematics at the University of California, Los Angeles, working with Dr. Stanley J. Osher. I have obtained my Ph.D. in Machine Learning from Rice University, where I was advised by Dr. Richard G. Baraniuk. My research is focused on the intersection of Deep Learning, Probabilistic Modeling, Optimization, and ODEs/PDEs. I gave an invited talk in the Deep Learning Theory Workshop at NeurIPS 2018 and organized the 1st Workshop on Integration of Deep Neural Models and Differential Equations at ICLR 2020. I also had two awesome long internships with Amazon AI and NVIDIA Research, during which he worked with Dr. Anima Anandkumar. I am the recipient of the prestigious Computing Innovation Postdoctoral Fellowship (CIFellows) from the Computing Research Association (CRA), the NSF Graduate Research Fellowship, and the IGERT Neuroengineering Traineeship. I received his MSEE and BSEE from Rice in May 2018 and May 2014, respectively.

Doris Tsao (Caltech)
Richard Baraniuk (Rice University)
Anima Anandkumar (NVIDIA / Caltech)

Anima Anandkumar is a Bren professor at Caltech CMS department and a director of machine learning research at NVIDIA. Her research spans both theoretical and practical aspects of large-scale machine learning. In particular, she has spearheaded research in tensor-algebraic methods, non-convex optimization, probabilistic models and deep learning. Anima is the recipient of several awards and honors such as the Bren named chair professorship at Caltech, Alfred. P. Sloan Fellowship, Young investigator awards from the Air Force and Army research offices, Faculty fellowships from Microsoft, Google and Adobe, and several best paper awards. Anima received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, a visiting researcher at Microsoft Research New England in 2012 and 2014, an assistant professor at U.C. Irvine between 2010 and 2016, an associate professor at U.C. Irvine between 2016 and 2017 and a principal scientist at Amazon Web Services between 2016 and 2018.

Hidenori Tanaka (Stanford)
Aran Nayebi (Stanford University)
Stephen Baccus (Stanford University)
Surya Ganguli (Stanford)
Dean Pospisil (University of Washington)
Eilif Muller (Element AI)

Applied Research Scientist at Element AI. Looking to neuroscience for inspiration in AI, and vice versa, in particular when it comes to neocortical architectures for learning to represent the world around us. From 2011-2019, I led the team of researchers at the Blue Brain Project developing, and studying state-of-the-art simulations of neocortical brain tissue.

Jeffrey S Cheng (University of Pennsylvania)

I'm a ML consultant at Palantir. My research interests center around applying smart inductive biases to generative natural processes. I'm currently working on creating interpretable models for machine translation and music composition.

Gaël Varoquaux (INRIA)
Kamalaker Dadi (INRIA)
Dimitrios C Gklezakos (University of Washington)
Rajesh PN Rao (University of Washington)
Anand Louis (Indian Institute of Science, Bengaluru)
Christos Papadimitriou (Columbua U)
Santosh Vempala (Georgia Tech)
Naganand Yadati (Indian Institute of Science)
Daniel Zdeblick (University of Washington)
Daniela M Witten (University of Washington)
Nicholas Roberts (Carnegie Mellon University)
Vinay Prabhu (UnifyID Inc)
Pierre Bellec (Université de Montréal)

I am a principal investigator at the Unité de neuroimagerie fonctionnelle, Centre de recherche de l'institut de gériatrie de Montréal, “professeur adjoint sous octroi” with the computer science and operations research department (DIRO) and a member of the Institute of Biomedical Engineering at Université de Montréal. I am developing machine learning tools to study the brain structure and function using magnetic resonance imaging and I use these tools to explore the processes of brain reorganization in healthy aging and neurodegenerative diseases.

Poornima Ramesh (Technical University of Munich)
Jakob H Macke (Technical University of Munich, Munich, Germany)
Santiago Cadena (University of Tübingen)
Guillaume Bellec (TU Graz)
Franz Scherr (Graz University of Technology)
Owen Marschall (New York University)

BA in Mathematics and Physics (Amherst College) PhD student in Neural Science (New York University)

Robert Kim (Salk Institute)
Hannes Rapp (University of Cologne)
Marcio Fonseca (Câmara dos Deputados)

ML Engineer @ Camara dos Deputados. MSc Cognitive Science @ The University of Edinburgh.

Oliver Armitage (BIOS)
Jiwoong Im (Janelia Research Center)
Thomas Hardcastle (BIOS.health)
Abhishek Sharma (University of Massachusetts, Amherst)
Wyeth Bair (University of Washington)
Adrian Valente (ENS)
Shane Shang (University of Washington)
Merav Stern (University of Washington)
Rutuja Patil (Janelia Research Campus)
Peter Wang (Columbia University)
Sruthi Gorantla (National University of Singapore)
Peter Stratton (The University of Queensland)
Tristan Edwards (BIOS Health)
Jialin Lu (Simon Fraser University)

MSc student at Simon Fraser University with Prof. Martin Ester. I am generally interested in interpretability: interpret a complex prediction system into a small and interpretable structure. Recently I got especially interested in integrating symbolic methods with scalable deep learning approaches.

Martin Ester (Simon Fraser University)
Martin Ester

Martin Ester received a PhD in Computer Science from ETH Zurich, Switzerland, in 1989. He has been working for Swissair developing expert systems before he joined University of Munich as an Assistant Professor in 1993. Since November 2001, he has first been an Associate Professor and now a Full Professor at the School of Computing Science of Simon Fraser University. From May 2010 to April 2015, he has served as the School Director. Dr. Ester has published extensively in the top conferences and journals of his field such as ACM SIGKDD, WWW, ACM RecSys, ISMB, and PSB. According to Google Scholar, his publications have received more than 53'000 citations, and his h-index is 70. He received the KDD 2014 Test of Time Award for his paper on DBSCAN, was elected as a Fellow of the Royal Society of Canada in 2019, and was appointed Distinguished Professor at Simon Fraser University in 2021. Martin Ester’s research interests are in the area of data mining and machine learning, with a current focus on transfer learning, causal discovery and inference, explainable machine learning, and clustering. Many of the driving applications of his research are in the biomedical field, and he has an honorary appointment as Senior Research Scientist at the Vancouver Prostate Center.

Yurii Vlasov (University of Illinois at Urbana Champaign)
Siavash Golkar (Flatiron Institute)

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