The Science of Deep Learning


Peter Bartlett
University of California, Berkeley
Peter Bartlett is a professor in the Division of Computer Science and the Department of Statistics. He is the co-author of the book Learning in Neural Networks: Theoretical Foundations. He has served as associate editor of the journals Machine Learning, Mathematics of Control Signals and Systems, Journal of Machine Learning Research, Journal of Artificial Intelligence Research, and the IEEE Transactions on Information Theory.
Regina Barzilay
Massachusetts Institute of Technology
Regina Barzilay is a Delta Electronics professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Her research interests are in natural language processing, applications of deep learning to chemistry and oncology. She is a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship. She received her Ph.D. in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University.
Leon Bottou
Research Scientist
FaceBook AI Research
I am a research scientist with broad interests in machine learning and artificial intelligence. My work on large scale learning and stochastic gradient algorithms has received attention in the recent years. I am also known for the DjVu document compression system. I joined Facebook AI Research in March 2015.
Rodney Brooks
Professor Emeritus
Massachusetts Institute of Technology
Rodney Brooks is the Panasonic Professor of Robotics (emeritus) at MIT, where he was for ten years the director of the Artificial Intelligence Lab and then the Computer Science and Artificial Intelligence Lab (CSAIL) until 1997. He received a PhD in computer science from Stanford in 1981, was a post doc at CMU and MIT, and a faculty member at Stanford, before joining the MIT faculty in 1984. His research areas have been in machine learning, computer vision, robotics, AI, and Artificial Life. He was co-founder, CTO and Chairman of iRobot. He is a member of the National Academy of Engineering., @rodneyabrooks.
Ronald Coifman
Phillips Professor of Math and Computer Science
Yale University
Ronald Coifman’s research interests include: nonlinear Fourier analysis, wavelet theory, singular integrals, numerical analysis and scattering theory, real and complex analysis; new mathematical tools for efficient computation and transcriptions of physical data, with applications to numerical analysis, feature extraction recognition and denoising. He is currently developing analysis tools for spectrometric diagnostics and hyperspectral imaging.

Coifman is a member of the American Academy of Arts and Sciences, the Connecticut Academy of Science and Engineering, and the National Academy of Sciences. He is a recipient of the 1996 DARPA Sustained Excellence Award, the 1996 Connecticut Science Medal, the 1999 Pioneer Award of the International Society for Industrial and Applied Science, and the 1999 National Medal of Science.
Kyle Cranmer
Professor Of Physics
New York University
Kyle Cranmer is an American physicist and a professor at New York University at the Center for Cosmology and Particle Physics and Affiliated Faculty member at NYU's Center for Data Science. He is an experimental particle physicist working, primarily, on the Large Hadron Collider, based in Geneva, Switzerland. Cranmer popularized a collaborative statistical modeling approach and developed statistical methodology, which was used extensively for the discovery of the Higgs boson at the LHC in July, 2012.

Cranmer is active in the discussions of data preservation, open access, reproducibility, and e-science in the context of particle physics. Cranmer performed a search for exotic Higgs decays in archived data from the ALEPH experiment ten years after the experiment finalized. He serves on the advisory board for INSPIRE, the literature database for high energy physics, and is a member of the Data Preservation in High Energy Physics study group as well as [Data and Software Preservation for Open Science].

Since the discovery of the Higgs boson, Cranmer has been a popular choice as a guest on science television programming. In July, 2011, Cranmer appeared in a special episode of Neil deGrasse Tyson's StarTalk Live alongside Bill Nye the Science Guy, Eugene Mirman, and Sarah Vowell. In a special video created for Science Nation, the online magazine of the National Science Foundation, Cranmer was featured discussing the Higgs boson in November, 2012. Cranmer also discussed the discovery of the Higgs boson in a TedxTalk in February, 2013.[3]

Cranmer obtained his Ph.D. in Physics from the University of Wisconsin-Madison in 2005 under Sau Lan Wu and his B.A. in Mathematics and Physics from Rice University. He was a Goldhaber Fellow at Brookhaven National Lab from 2005-2007. In 2007, he was awarded the Presidential Early Career Award for Scientists and Engineers from President George W. Bush via the Department of Energy's Office of Science and in 2009 he was awarded the National Science Foundation's Career Award. Cranmer is also a graduate of the Arkansas School for Mathematics, Sciences, and the Arts
David Donoho
Stanford University
David Leigh Donoho is a professor of statistics at Stanford University, where he is also the Anne T. and Robert M. Bass Professor in the Humanities and Sciences. His work includes the development of effective methods for the construction of low-dimensional representations for high-dimensional data problems (multiscale geometric analysis), developments of wavelets for denoising and compressed sensing.

Donoho did his undergraduate studies at Princeton University, graduating in 1978. His undergraduate thesis advisor was John W. Tukey. Donoho obtained his Ph.D. from Harvard University in 1983, under the supervision of Peter J. Huber. He was on the faculty of the University of California, Berkeley from 1984 to 1990 before moving to Stanford.

He has been the Ph.D. advisor of at least 20 doctoral students, including Jianqing Fan and Emmanuel Candès.

In 1991, Donoho was named a MacArthur Fellow. He was elected a Fellow of the American Academy of Arts and Sciences in 1992. He was the winner of the COPSS Presidents' Award in 1994. In 2001, he won the John von Neumann Prize of the Society for Industrial and Applied Mathematics. In 2002, he was appointed to the Bass professorship. He was elected a SIAM Fellow and a foreign associate of the French Académie des sciences in 2009, and in the same year received an honorary doctorate from the University of Chicago. In 2010 he won the Norbert Wiener Prize in Applied Mathematics, given jointly by SIAM and the American Mathematical Society. He is also a member of the United States National Academy of Science In 2012 he became a fellow of the American Mathematical Society. In 2013 he was awarded the Shaw Prize for Mathematics. In 2016, he was awarded an honorary degree at the University of Waterloo. In 2018, he was awarded the Gauss Prize from IMU.
Matan Gavish
Assistant Professor of Psychology
Hebrew University
Harmonic analysis, applied and actually applied math, statistics and computing.
Isabelle Guyon
Paris-Sud University & ClopiNet
Isabelle Guyon is professor of data science at the Université Paris-Saclay (UPSud/INRIA, Orsay), specialized in statistical data analysis, pattern recognition and machine learning. Her areas of expertise include computer vision, bioinformatics, and power systems. Her recent interest is in applications of machine learning to the discovery of causal relationships. Prior to joining Paris-Saclay she worked as an independent consultant and was a researcher at AT&T Bell Laboratories, where she pioneered applications of neural networks to pen computer interfaces (with collaborators including Yann LeCun and Yoshua Bengio) and co-invented with Bernhard Boser and Vladimir Vapnik Support Vector Machines (SVM), which became a textbook machine learning method. She is also the primary inventor of SVM-RFE, a variable selection technique based on SVM. The SVM-RFE paper has thousands of citations and is often used as a reference method against which new feature selection methods are benchmarked. She also authored a seminal paper on feature selection that received thousands of citations. She organized many challenges in Machine Learning since 2003 supported by the EU network Pascal2, NSF, and DARPA, with prizes sponsored by Microsoft, Google, Facebook, Amazon, Disney Research, and Texas Instrument. Isabelle Guyon holds a Ph.D. degree in Physical Sciences of the University Pierre and Marie Curie, Paris, France. She is president of Chalearn, a non-profit dedicated to organizing challenges, action editor of the Journal of Machine Learning Research, editor of the Springer series of Challenges in Machine Learning, and served recently as program co-chair of NIPS 2016 and general co-chair of NIPS 2017.
Anders Hansen
Associate Professor
Cambridge University
Anders leads the Applied Functional and Harmonic Analysis group within the Cambridge Centre for Analysis at DAMTP. He is a Reader (Associate Professor) in mathematics at DAMTP, Professor of Mathematics at the University of Oslo, a Royal Society University Research Fellow and also a Fellow of Peterhouse.
Moritz Hardt
Assistant Professor
University of California, Berkeley
Researcher. Machine learning, optimization, privacy and social questions in computation.
Julia Kempe
Director of the Center for Data Science; Professor of Computer Science and Mathematics
New York University Center for Data Science
Julia Kempe is a French, German, and Israeli researcher in quantum computing. Born in East Berlin to a Russian family there, and educated in Austria, Australia, France, and the US, she holds positions as a researcher at CNRS and Paris Diderot University and as Director of the Center for Data Science at NYU.

Kempe grew up in East Berlin, but as a teenager in 1990 she moved with her parents to Vienna. She did her undergraduate studies in mathematics and physics at the University of Vienna from 1992 to 1995, with a year as an exchange student in physics at the University of Technology Sydney. She then earned two Master of Advanced Studies (DEA) degrees in France, one in mathematics in 1996 from Pierre and Marie Curie University and another in 1997 in physics from the École Normale Supérieure. She completed two doctorates in 2001. The dissertation for her Ph.D. in computer science from the École Nationale Superieure des Telecommunications was entitled Quantum Computing: Random Walks and Entanglement, and was supervised by Gérard Cohen. Her second Ph.D., in mathematics, was from the University of California, Berkeley, with a dissertation entitled Universal Noiseless Quantum Computation: Theory and Applications and was jointly supervised by Elwyn Berlekamp and chemist K. Birgitta Whaley.[6]

She joined CNRS at the University of Paris-Sud in 2001 (overlapping with postdoctoral studies at Berkeley and the Berkeley Mathematical Sciences Research Institute), joined the Tel Aviv University faculty in 2007, and moved her CNRS position from Paris-Sud to Paris Diderot in 2010. She became Director of the Center of Data Science at NYU and a Professor at the Courant Institute in September 2018.

In 2006, Kempe won the bronze medal of CNRS and the Irène Joliot-Curie Prize of the French government. In 2009 she won the Krill Prize of the Wolf Foundation, and in 2010 she won the "Women in Gold" trophy [fr] for her research. Also in 2010, she became a knight in the National Order of Merit.
Jon Kleinberg
Tisch University Professor
Cornell University
I am a professor at Cornell University. My research focuses on the interaction of algorithms and networks, and the roles they play in large-scale social and information systems. My work has been supported by an NSF Career Award, an ONR Young Investigator Award, a MacArthur Foundation Fellowship, a Packard Foundation Fellowship, a Simons Investigator Award, a Sloan Foundation Fellowship, and grants from Facebook, Google, Yahoo, the MacArthur Foundation, the ARO, and the NSF. I am a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences.
Konrad Kording
Penn Integrated Knowledge Professor
University of Pennsylvania
Konrad Kording runs the Kording lab at the University of Pennsylvania. Konrad is trying to foster an environment of creativity in which important problems get solved in interesting ways and unimportant problems get solved in fun ways. Konrad believes in the role of playful approaches towards science. He also believes that ultimately we need to solve important medical problems. His lab focuses on Big Data approaches to neuroscience and really anything. Massive amounts of data exist in neuroscience and work in the lab is focused on making sense out of such datasets (and obtaining such sets).
Jitendra Malik
Arthur J. Chick Professor of EECS
Univeristy of California, Berkeley
Jitendra Malik was born in Mathura, India in 1960. He received the B.Tech degree in Electrical Engineering from Indian Institute of Technology, Kanpur in 1980 and the PhD degree in Computer Science from Stanford University in 1985. In January 1986, he joined the university of California at Berkeley, where he is currently the Arthur J. Chick Professor in the Department of Electrical Engineering and Computer Sciences. He is also on the faculty of the department of Bioengineering, and the Cognitive Science and Vision Science groups. During 2002-2004 he served as the Chair of the Computer Science Division, and as the Department Chair of EECS during 2004-2006 as well as 2016-2017. Since January 2018, he is also Research Director and Site Lead of Facebook AI Research in Menlo Park.

Prof. Malik's research group has worked on many different topics in computer vision, computational modeling of human vision, computer graphics and the analysis of biological images. Several well-known concepts and algorithms arose in this research, such as anisotropic diffusion, normalized cuts, high dynamic range imaging, shape contexts and R-CNN. He has mentored more than 60 PhD students and postdoctoral fellows.

He received the gold medal for the best graduating student in Electrical Engineering from IIT Kanpur in 1980 and a Presidential Young Investigator Award in 1989. At UC Berkeley, he was selected for the Diane S. McEntyre Award for Excellence in Teaching in 2000 and a Miller Research Professorship in 2001. He received the Distinguished Alumnus Award from IIT Kanpur in 2008. His publications have received numerous best paper awards, including five test of time awards - the Longuet-Higgins Prize for papers published at CVPR (twice) and the Helmholtz Prize for papers published at ICCV (three times). He received the 2013 IEEE PAMI-TC Distinguished Researcher in Computer Vision Award, the 2014 K.S. Fu Prize from the International Association of Pattern Recognition, the 2016 ACM-AAAI Allen Newell Award, and the 2018 IJCAI Award for Research Excellence in AI. He is a fellow of the IEEE and the ACM. He is a member of the National Academy of Engineering and the National Academy of Sciences, and a fellow of the American Academy of Arts and Sciences.
Christopher Manning
Stanford University
Christopher Manning is the inaugural Thomas M. Siebel Professor in Machine Learning in the Departments of Computer Science and Linguistics at Stanford University. His research goal is computers that can intelligently process, understand, and generate human language material. Manning is a leader in applying Deep Learning to Natural Language Processing, with well-known research on Tree Recursive Neural Networks, sentiment analysis, neural network dependency parsing, the GloVe model of word vectors, neural machine translation, question answering, and deep language understanding. He also focuses on computational linguistic approaches to parsing, robust textual inference and multilingual language processing, including being a principal developer of Stanford Dependencies and Universal Dependencies. Manning has coauthored leading textbooks on statistical approaches to Natural Language Processing (NLP) (Manning and Schütze 1999) and information retrieval (Manning, Raghavan, and Schütze, 2008), as well as linguistic monographs on ergativity and complex predicates. He is an ACM Fellow, a AAAI Fellow, and an ACL Fellow, and a Past President of the ACL. Research of his has won ACL, Coling, EMNLP, and CHI Best Paper Awards. He has a B.A. (Hons) from The Australian National University and a Ph.D. from Stanford in 1994, and he held faculty positions at Carnegie Mellon University and the University of Sydney before returning to Stanford. He is the founder of the Stanford NLP group (@stanfordnlp) and manages development of the Stanford CoreNLP software.
Bruno Olshausen
Universitty of California, Berkeley
Professor Bruno Olshausen is a Professor in the Helen Wills Neuroscience Institute, the School of Optometry, and has a below-the-line affiliated appointment in EECS. He holds B.S. and M.S. degrees in Electrical Engineering from Stanford University, and a Ph.D. in Computation and Neural Systems from the California Institute of Technology. He did his postdoctoral work in the Department of Psychology at Cornell University and at the Center for Biological and Computational Learning at the Massachusetts Institute of Technology. From 1996-2005 he was on the faculty in the Center for Neuroscience at UC Davis, and in 2005 he moved to UC Berkeley. He also directs the Redwood Center for Theoretical Neuroscience, a multidisciplinary research group focusing on building mathematical and computational models of brain function (see
Olshausen's research focuses on understanding the information processing strategies employed by the visual system for tasks such as object recognition and scene analysis. Computer scientists have long sought to emulate the abilities of the visual system in digital computers, but achieving performance anywhere close to that exhibited by biological vision systems has proven elusive. Dr. Olshausen's approach is based on studying the response properties of neurons in the brain and attempting to construct mathematical models that can describe what neurons are doing in terms of a functional theory of vision. The aim of this work is not only to advance our understanding of the brain but also to devise new algorithms for image analysis and recognition based on how brains work.
P. Jonathon Phillips
Electronic Engineer
National Institute of Standards and Technology's Information Technology Laboratory
Dr. P. Jonathon Phillips is an Electronic Engineer at the National Institute of Standards and Technology's Information Technology Laboratory. Jonathon is a leading researcher in the fields of computer vision, face recognition, biometrics, and forensics. He has published over 100 peer reviewed papers in face recognition, computer vision, biometrics, psychology, forensics, statistics, and neuroscience. His papers have received over 27,000 Google citations. He is an IEEE Fellow and an International Association of Pattern Recognition (IAPR) Fellow.
Dr. Phillips pioneered competitions to improve technology in face recognition, computer vision, and biometrics. The programs and competitions that Jonathon managed were instrumental in advancing face recognition from its infancy in research labs to deployment in real-world applications. Progress was measured in a series of evaluations from the 1993 FERET competition through the Multiple Biometric Evaluation 2010. The competitions documented a Moore’s law improvement in face recognition accuracy—from 1993 to 2010, the error rate for the technology decreased by half every two-years. For his work on competitions and its influences and adoption by computer vision and biometrics he won the IEEE inaugural Mark Everingham Prize,the Dept. of Commerce Gold Medal, the Dept. of Commerce Bronze Medal, Office of the Secretary of Defense Medal for Exceptional Civilian Service, andFederal Bureau of Investigation CJIS Assistant Director’s Award for Outstanding Scientific Achievement.
A hallmark of Jonathon’s research is collaboration with researchers from related fields. These collaborations have resulted in better understanding of biometric algorithm performance and the relationship between human and algorithm face recognition accuracy.
In law enforcement, border control, and security, humans perform face recognition. To assess the ability of technology to complement or perform face recognition task, it is necessary to know the relative accuracy of machines and humans.
Tomaso Poggio
Massachusetts Institute of Technology
Tomaso Poggio is Eugene McDermott Professor in the Department of Brain and Cognitive Sciences and at the Artificial Intelligence Laboratory. He is a founding member of the McGovern Institute, and is also the director of the Center for Brains, Minds, and Machines, a multi-institutional collaboration headquartered at the McGovern Institute. He joined the MIT faculty in 1981, after ten years at the Max Planck Institute for Biology and Cybernetics in Tubingen, Germany. He received a Ph.D. in 1970 from the University of Genoa. Poggio is a Foreign Member of the Italian Academy of Sciences and a Fellow of the American Academy of Arts and Sciences. He was awarded the 2014 Swartz Prize for Theoretical and Computational Neuroscience.
Doina Precup
Associate Professor
McGill University
Doina Precup is a Romanian researcher currently living in Montreal, Canada. She specializes in artificial intelligence (AI). Precup is associate dean of research at the faculty of science at McGill University, Canada research chair in machine learning and a senior fellow at the Canadian Institute for Advanced Research. She also heads the Montreal office of Deepmind.

Precup said she was drawn to the field of artificial intelligence at a young age by her interest in science fiction, where robots are often portrayed as useful and benevolent. Her mother was a university professor of computer science in Romania.

She obtained a bachelor of science degree in computer science and engineering (magna cum laude) at Technical University of Cluj-Napoca in 1994, followed by a master of science in 1995. She left Romania in 1995 on a Fulbright scholarship to pursue graduate studies at the University of Massachusetts Amherst, where she earned a master's degree in 1997 and a PhD in 2000.

Several women in Precup's family have had successful careers in science and the science program in her high school in Romania was well attended by girls. She only became aware of the gender imbalance in sciences and technology when she moved to North America. She decided to get involved in correcting this situation and does so as an advisor for AI4good, an organization that aims at getting more women to study and work in artificial intelligence.

Precup was recruited as an assistant professor by McGill University's School of Computer Science in 2000 and has since been living in the Montreal area.

In 2017, Precup was appointed to lead the Montreal office of the artificial intelligence firm Deepmind, which is owned by Google. She teaches at McGill while conducting fundamental research on reinforcement learning at Deepmind, working in particular on AI applications in areas that have a social impact, such as health care (medical imaging for example). She's interested in machine decision-making in situations where uncertainty is high.

She is a senior fellow of the Canadian Institute for Advanced Research, fellow of the Association for the Advancement of Artificial Intelligence and a member of the Montreal Institute for Learning Algorithms.
Maithra Raghu
PhD Student
Google Brain and Cornell
I am a PhD student in Computer Science at Cornell University, where I am very fortunate to be advised by Jon Kleinberg. I am currently doing extended research with the Google Brain Team, where my mentors are Quoc Le and Samy Bengio. In the past, I’ve collaborated with Jascha Sohl-Dickstein and Surya Ganguli at Stanford.

Before Cornell, I was at the University of Cambridge (Trinity College) where I completed my Bachelors and Masters (Part III of the Tripos) in Mathematics. Prior to that I was very fortunate to spend my final years in high school competing in national and international mathematical Olympaids. The highlight was being part of the UK team at the China Girls Math Olympiad.

My research interests are broadly in machine learning, particularly deep learning. A specific research goal is to develop the Science of Deep Learning: understand and improve deep neural networks by combining systematic experiments with principled methods to provide robust conclusions. I am also interested in using these insights in healthcare applications.
Ali Rahimi
Member of Technical Staff
Google, Inc.
Machine learning, computer vision, optics, consumer electronics.
Ben Recht
Associate Professor
University of California, Berkeley
My group and I study the theory and practice of optimization algorithms with a focus on applications in machine learning and data analysis. We are particularly interested in busting machine learning myths and establishing baselines for data analysis. Our work is enriched by collaborations with researchers from applied fields including computational imaging, robotics, and personalized medicine.
Tara Sainath
Senior Staff Research Scientist
Google AI
I received my PhD in Electrical Engineering and Computer Science from MIT in 2009. The main focus of my PhD work was in acoustic modeling for noise robust speech recognition. After my PhD, I spent 5 years at the Speech and Language Algorithms group at IBM T.J. Watson Research Center, before joining Google Research. I have served as a Program Chair for ICLR in 2017 and 2018. Also, I have co-organized numerous special sessions and workshops, including Interspeech 2010, ICML 2013, Interspeech 2016 and ICML 2017. In addition, I am a member of the IEEE Speech and Language Processing Technical Committee (SLTC) as well as the Associate Editor for IEEE/ACM Transactions on Audio, Speech, and Language Processing. My research interests are mainly in acoustic modeling, including deep neural networks, sparse representations and adaptation methods.
Terrence (Terry) Sejnowski
Professor and Laboratory Head
Salk Institute for Biological Studies
Terrence (Terry) Joseph Sejnowski is the Francis Crick Professor at The Salk Institute for Biological Studies where he directs the Computational Neurobiology Laboratory and is the Director of the Crick-Jacobs Center for Theoretical and Computational Biology. His research in neural networks and computational neuroscience has been pioneering.

Sejnowski is also Professor of Biological Sciences and Adjunct Professor in the Departments of Neurosciences, Psychology, Cognitive Science, Computer Science and Engineering at the University of California, San Diego, where he is Co-Director of the Institute for Neural Computation.

With Barbara Oakley, he co-created and taught Learning How To Learn: Powerful mental tools to help you master tough subjects, the world's most popular online course, available on Coursera.
Amnon Shashua
Sachs Professor of Computer Science
Hebrew University / Mobileye
Amnon Shashua is a computer science professor at the Hebrew University in Jerusalem as well as co-founder and President of Mobileye and co-founder, Chairman and CTO of OrCam. As of the Intel acquisition of Mobileye in 2017, he serves as CEO and President of Mobileye, and Senior Vice President, Intel Corporation.

Shashua has been on the computer science faculty at the Hebrew University of Jerusalem since 1996. In 1999 he was appointed as an associate professor and in 2003 received full professorship. Between the years 2002-2005 he was the head of the engineering and computer science school at the Hebrew University. Shashua currently holds the Sachs chair in computer science at the Hebrew University. Over the years, Shashua has published over 100 papers in the field of machine learning and computational vision.

His work includes early visual processing of saliency and grouping mechanisms, visual recognition and learning, image synthesis for animation and graphics, theory of computer vision in the areas of multiple-view geometry and multi-view tensors, multilinear algebraic systems in Vision and Learning and primal/dual optimization for approximate inference in MRF and Graphical models and since 2014 on deep layered networks.

His work on multiple-view geometry received the "best paper award" at ECCV'2000 and the Honorable Mention to the MARR Prize in ICCV'2001. His work on graphical models has received a "Best Paper" award category at UAI'2008. Amnon Shashua received the first prize of the 2004 Kaye Innovation award, and the 2005 Landau Award for Science and Research in the area of exact sciences - Robotics. Since 2007 he became the incumbent of the newly formed Sachs chair in computer science. In 2019 Shashua was recognized by the Society for Imaging Science and Technology (IS&T) as the Electronic Imaging (EI) Scientist of the Year, an annual award honoring a member of the electronic imaging community who demonstrates excellence and commands the respect of his/her peers by making significant and substantial contributions to the field of electronic imaging. He was recognized for his pivotal contributions to computer vision and machine learning, and for advancing autonomous driving and wearable assistive devices for the blind and visually-impaired.
Eero Simoncelli
Investigator / Professor
New York University
Eero Simoncelli is an American computational neuroscientist and Silver Professor at New York University. He is a Fellow of the Institute of Electrical and Electronics Engineers and a Howard Hughes Medical Institute Investigator.

Simoncelli graduated summa cum laude with a bachelor's degree in physics at Harvard University in 1984. He then attended Cambridge University on a Knox Fellowship to study the Mathematical Tripos, after which he joined the graduate program at the Massachusetts Institute of Technology in electrical engineering and computer science. He received his masters in 1988 and his PhD in 1993. He then joined the faculty at the University of Pennsylvania as an assistant professor, and in 1996 he moved to New York University. In 2009, he became an IEEE Fellow. He received an Engineering Emmy Award in 2015 with Zhou Wang, Alan Bovik, and Hamid Sheikh for the Structural Similarity Video Quality Measurement Model (SSIM).
Haim Sompolinksy
Professor of Physics
Hebrew University of Jerusalem
Haim Sompolinsky received his Ph.D. in physics from Bar-Ilan University in Tel Aviv, Israel in 1980. He then worked as postdoctoral fellow in the physics department at Harvard University until 1982, under the supervision of Professor Bertrand Halperin. He was appointed associate professor of physics at Bar-Ilan University until 1986, when he moved to the Hebrew University of Jerusalem as professor of physics. Sompolinsky’s research in theoretical physics covered the fields of phase transitions, critical phenomena, nonlinear dynamics and the statistical mechanics of spin glasses. Since the mid-1980s, he has pioneered the new field of computational neuroscience, introducing methods and concepts of theoretical physics to the study of neuronal circuits, memory, learning and neuronal information processing.

In 1992, he helped found Hebrew University’s Interdisciplinary Center for Neural Computation and served as its director from 2008 to 2010. He is currently the William N. Skirball Professor of Neuroscience and serves on the executive board of the newly established Edmond and Lily Safra Center for Brain Sciences at the Hebrew University. He was a visiting scholar at several institutions, including Bell Laboratories and New York University, and serves on the faculty of the Methods in Computational Neuroscience Course at the Marine Biology Lab.

Sompolinsky’s research includes spike-based neural learning and computation, neuronal population codes, sensory representations, dynamics and function of sensory and motor cortical circuits, and large-scale structure and dynamics of human brain. He also studies the relation between physics, neuroscience, and human volition, freedom and agency.

From 2006, Sompolinsky has served as a visiting professor in the Center of Brain Science at Harvard University and the director of Harvard’s Swartz Program in Theoretical Neuroscience.
Nathan Srebro
Toyota Technological Institute at Chicago
Dr. Srebro is interested in statistical and computational aspects of machine learning, and the interaction between them. He has done theoretical work in statistical learning theory and in algorithms, devised novel learning models and optimization techniques, and has worked on applications in computational biology, text analysis and collaborative filtering. Before coming to TTIC, Dr. Srebro was a postdoctoral fellow at the University of Toronto and a visiting scientist at IBM Research.
Antonio Torralba
Massachusetts Institute of Technology
My research is in the areas of computer vision, machine learning and human visual perception. I am interested in building systems that can perceive the world like humans do. Although my work focuses on computer vision I am also interested in other modalities such as audition and touch. A system able to perceive the world through multiple senses might be able to learn without requiring massive curated datasets. Other interests include understanding neural networks, common-sense reasoning, computational photography, building image databases, ..., and the intersections between visual art and computation.
Olga Troyanskaya
Professor, Deputy Director of Genomics
Princeton University
Olga Troyanskaya is a professor at the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science at Princeton University, where she has been on the faculty since 2003. In 2014 she became the deputy director of Genomics at the Center for Computational Biology at the Flatiron Institute, a part of the Simons Foundation in NYC. She holds a Ph.D. in Biomedical Informatics from Stanford University, has been honored as one of the top young technology innovators by the MIT Technology Review, and is a recipient of the Sloan Research Fellowship, the National Science Foundation CAREER award, the Overton award from the International Society for Computational Biology, and the Ira Herskowitz award from the Genetic Society of America.
Oriol Vinyals
Google AI