MB
Advisory Board
Organizers
NIPS 2018
PB
Advisory Board
Peter Bartlett
SB
Advisory Board
Sue Becker
YB
Advisory Board
Yoshua Bengio
YB
Advisory Board
Yoshua Bengio
YB
Advisory Board
Yoshua Bengio
Yoshua Bengio (PhD'1991 in Computer Science, McGill University). After two post-doctoral years, one at MIT with Michael Jordan and one at AT&T Bell Laboratories with Yann LeCun, he became professor at the department of computer science and operations research at Université de Montréal. Author of two books (a third is in preparation) and more than 200 publications, he is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks. Since '2000 he holds a Canada Research Chair in Statistical Learning Algorithms, since '2006 an NSERC Chair, since '2005 his is a Senior Fellow of the Canadian Institute for Advanced Research and since 2014 he co-directs its program focused on deep learning. He is on the board of the NIPS foundation and has been program chair and general chair for NIPS. He has co-organized the Learning Workshop for 14 years and co-created the International Conference on Learning Representations. His interests are centered around a quest for AI through machine learning, and include fundamental questions on deep learning, representation learning, the geometry of generalization in high-dimensional spaces, manifold learning and biologically inspired learning algorithms.
Yoshua Bengio (PhD'1991 in Computer Science, McGill University). After two post-doctoral years, one at MIT with Michael Jordan and one at AT&T Bell Laboratories with Yann LeCun, he became professor at the department of computer science and operations research at Université de Montréal. Author of two books (a third is in preparation) and more than 200 publications, he is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks. Since '2000 he holds a Canada Research Chair in Statistical Learning Algorithms, since '2006 an NSERC Chair, since '2005 his is a Senior Fellow of the Canadian Institute for Advanced Research and since 2014 he co-directs its program focused on deep learning. He is on the board of the NIPS foundation and has been program chair and general chair for NIPS. He has co-organized the Learning Workshop for 14 years and co-created the International Conference on Learning Representations. His interests are centered around a quest for AI through machine learning, and include fundamental questions on deep learning, representation learning, the geometry of generalization in high-dimensional spaces, manifold learning and biologically inspired learning algorithms.
LB
Advisory Board
Leon Bottou
Léon Bottou received a Diplôme from l'Ecole Polytechnique, Paris in 1987, a Magistère en Mathématiques Fondamentales et Appliquées et Informatiques from Ecole Normale Supérieure, Paris in 1988, and a PhD in Computer Science from Université de Paris-Sud in 1991. He joined AT&T Bell Labs from 1991 to 1992 and AT&T Labs from 1995 to 2002. Between 1992 and 1995 he was chairman of Neuristique in Paris, a small company pioneering machine learning for data mining applications. He has been with NEC Labs America in Princeton since 2002. Léon's primary research interest is machine learning. His contributions to this field address theory, algorithms and large scale
applications. Léon's secondary research interest is data compression and coding. His best known contribution in this field is the DjVu document compression technology (http://www.djvu.org.) Léon published over 70 papers and is serving on the boards of JMLR and IEEE TPAMI. He also serves on the scientific advisory board of Kxen Inc .
Léon Bottou received a Diplôme from l'Ecole Polytechnique, Paris in 1987, a Magistère en Mathématiques Fondamentales et Appliquées et Informatiques from Ecole Normale Supérieure, Paris in 1988, and a PhD in Computer Science from Université de Paris-Sud in 1991. He joined AT&T Bell Labs from 1991 to 1992 and AT&T Labs from 1995 to 2002. Between 1992 and 1995 he was chairman of Neuristique in Paris, a small company pioneering machine learning for data mining applications. He has been with NEC Labs America in Princeton since 2002. Léon's primary research interest is machine learning. His contributions to this field address theory, algorithms and large scale applications. Léon's secondary research interest is data compression and coding. His best known contribution in this field is the DjVu document compression technology (http://www.djvu.org.) Léon published over 70 papers and is serving on the boards of JMLR and IEEE TPAMI. He also serves on the scientific advisory board of Kxen Inc .
CB
Advisory Board
Chris J Burges
Advisory Board
Lee Campbell
CC
Advisory Board
Corinna Cortes
JC
Advisory Board
Jack D Cowan
TD
Advisory Board
Thomas Dietterich
Tom Dietterich (AB Oberlin College 1977; MS University of Illinois
1979; PhD Stanford University 1984) is Professor and Director of
Intelligent Systems Research at Oregon State University. Among his
contributions to machine learning research are (a) the formalization
of the multiple-instance problem, (b) the development of the
error-correcting output coding method for multi-class prediction, (c)
methods for ensemble learning, (d) the development of the MAXQ
framework for hierarchical reinforcement learning, and (e) the
application of gradient tree boosting to problems of structured
prediction and latent variable models. Dietterich has pursued
application-driven fundamental research in many areas including drug
discovery, computer vision, computational sustainability, and
intelligent user interfaces.
Dietterich has served the machine learning community in a variety of
roles including Executive Editor of the Machine Learning journal,
co-founder of the Journal of Machine Learning Research, editor of the
MIT Press Book Series on Adaptive Computation and Machine Learning,
and editor of the Morgan-Claypool Synthesis series on Artificial
Intelligence and Machine Learning. He was Program Co-Chair of
AAAI-1990, Program Chair of NIPS-2000, and General Chair of
NIPS-2001. He was first President of the International Machine
Learning Society (the parent organization of ICML) and served a term
on the NIPS Board of Trustees and the Council of AAAI.
Tom Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Professor and Director of Intelligent Systems Research at Oregon State University. Among his contributions to machine learning research are (a) the formalization of the multiple-instance problem, (b) the development of the error-correcting output coding method for multi-class prediction, (c) methods for ensemble learning, (d) the development of the MAXQ framework for hierarchical reinforcement learning, and (e) the application of gradient tree boosting to problems of structured prediction and latent variable models. Dietterich has pursued application-driven fundamental research in many areas including drug discovery, computer vision, computational sustainability, and intelligent user interfaces.
Dietterich has served the machine learning community in a variety of roles including Executive Editor of the Machine Learning journal, co-founder of the Journal of Machine Learning Research, editor of the MIT Press Book Series on Adaptive Computation and Machine Learning, and editor of the Morgan-Claypool Synthesis series on Artificial Intelligence and Machine Learning. He was Program Co-Chair of AAAI-1990, Program Chair of NIPS-2000, and General Chair of NIPS-2001. He was first President of the International Machine Learning Society (the parent organization of ICML) and served a term on the NIPS Board of Trustees and the Council of AAAI.
ZG
Advisory Board
Zoubin Ghahramani
Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group. He studied computer science and cognitive science at the University of Pennsylvania, obtained his PhD from MIT in 1995, and was a postdoctoral fellow at the University of Toronto. His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His current research interests include statistical machine learning, Bayesian nonparametrics, scalable inference, probabilistic programming, and building an automatic statistician. He has held a number of leadership roles as programme and general chair of the leading international conferences in machine learning including: AISTATS (2005), ICML (2007, 2011), and NIPS (2013, 2014). In 2015 he was elected a Fellow of the Royal Society.
Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group. He studied computer science and cognitive science at the University of Pennsylvania, obtained his PhD from MIT in 1995, and was a postdoctoral fellow at the University of Toronto. His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His current research interests include statistical machine learning, Bayesian nonparametrics, scalable inference, probabilistic programming, and building an automatic statistician. He has held a number of leadership roles as programme and general chair of the leading international conferences in machine learning including: AISTATS (2005), ICML (2007, 2011), and NIPS (2013, 2014). In 2015 he was elected a Fellow of the Royal Society.
Advisory Board
Isabelle Guyon
Isabelle Guyon recently joined Google Brain as a research scientist. She is also professor of artificial intelligence at Université Paris-Saclay (Orsay). Her areas of expertise include computer vision, bioinformatics, and power systems. She is best known for being a co-inventor of Support Vector Machines. Her recent interests are in automated machine learning, meta-learning, and data-centric AI. She has been a strong promoter of challenges and benchmarks, and is president of ChaLearn, a non-profit dedicated to organizing machine learning challenges. She is community lead of Codalab competitions, a challenge platform used both in academia and industry. She co-organized the “Challenges in Machine Learning Workshop” @ NeurIPS between 2014 and 2019, launched the "NeurIPS challenge track" in 2017 while she was general chair, and pushed the creation of the "NeurIPS datasets and benchmark track" in 2021, as a NeurIPS board member.
Isabelle Guyon recently joined Google Brain as a research scientist. She is also professor of artificial intelligence at Université Paris-Saclay (Orsay). Her areas of expertise include computer vision, bioinformatics, and power systems. She is best known for being a co-inventor of Support Vector Machines. Her recent interests are in automated machine learning, meta-learning, and data-centric AI. She has been a strong promoter of challenges and benchmarks, and is president of ChaLearn, a non-profit dedicated to organizing machine learning challenges. She is community lead of Codalab competitions, a challenge platform used both in academia and industry. She co-organized the “Challenges in Machine Learning Workshop” @ NeurIPS between 2014 and 2019, launched the "NeurIPS challenge track" in 2017 while she was general chair, and pushed the creation of the "NeurIPS datasets and benchmark track" in 2021, as a NeurIPS board member.
SH
Advisory Board
Stephen José Hanson
MJ
Advisory Board
Michael Jordan
MK
Advisory Board
Michael Kearns
Michael Kearns is Professor and National Center Chair in the Computer and Information Science department at the University of Pennsylvania. His research interests include topics in machine learning, algorithmic game theory, social networks, and computational finance. Prior to joining the Penn faculty, he spent a decade at AT&T/Bell Labs, where he was head of AI Research. He is co-director of Penn’s Warren Center for Network and Data Sciences (warrencenter.upenn.edu), and founder of Penn’s Networked and Social Systems Engineering (NETS) undergraduate program (www.nets.upenn.edu). Kearns consults extensively in technology and finance, and is a Fellow of the Association for the Advancement of Artificial Intelligence and the American Academy of Arts and Sciences.
Michael Kearns is Professor and National Center Chair in the Computer and Information Science department at the University of Pennsylvania. His research interests include topics in machine learning, algorithmic game theory, social networks, and computational finance. Prior to joining the Penn faculty, he spent a decade at AT&T/Bell Labs, where he was head of AI Research. He is co-director of Penn’s Warren Center for Network and Data Sciences (warrencenter.upenn.edu), and founder of Penn’s Networked and Social Systems Engineering (NETS) undergraduate program (www.nets.upenn.edu). Kearns consults extensively in technology and finance, and is a Fellow of the Association for the Advancement of Artificial Intelligence and the American Academy of Arts and Sciences.
DK
Advisory Board
David Kirkpatrick
Advisory Board
Scott Kirkpatrick
Research Interests:
Design of information appliances
Connections between complexity and statistical physics
Distributed computing on networks
How will "born digital" material be accessed and managed in the libraries of the future?
Research Interests: Design of information appliances Connections between complexity and statistical physics Distributed computing on networks How will "born digital" material be accessed and managed in the libraries of the future?
DK
Advisory Board
Daphne Koller
Daphne Koller is CEO and Founder of insitro, a machine learning-driven drug discovery company. She was the co-founder and co-CEO of Coursera, an online education platform for massive open online courses (MOOCs), which has reached over 100M learners worldwide. Daphne was the Rajeev Motwani Professor of Computer Science at Stanford University, where she served on the faculty for 18 years, and remains an Adjunct Faculty member. She is the author of over 300 refereed publications appearing in venues such as Science, Cell, Nature Genetics, NeurIPS, and ICML, with an h-index of 150. Daphne was recognized as one of TIME Magazine’s 100 most influential people in 2012 and Newsweek’s 10 most important people in 2010. She received the Sloan Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award in 1998, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the IJCAI Computers and Thought Award in 2001, the MacArthur Foundation Fellowship in 2004, the ACM Prize in Computing in 2008, the ACM AAAI Allen Newell Award in 2019, the IEEE CS Women of ENIAC Computer Pioneer award and the AnitaB.org Technical Leadership Abie Award Winner in 2022. Daphne was inducted into the National Academy of Sciences in 2023, the National Academy of Engineering in 2011 and elected a fellow of the American Association for Artificial Intelligence in 2004, the American Academy of Arts and Sciences in 2014 and the International Society of Computational Biology in 2017. Her teaching was recognized via the Stanford Medal for Excellence in Fostering Undergraduate Research, and as a Bass University Fellow in Undergraduate Education.
Daphne Koller is CEO and Founder of insitro, a machine learning-driven drug discovery company. She was the co-founder and co-CEO of Coursera, an online education platform for massive open online courses (MOOCs), which has reached over 100M learners worldwide. Daphne was the Rajeev Motwani Professor of Computer Science at Stanford University, where she served on the faculty for 18 years, and remains an Adjunct Faculty member. She is the author of over 300 refereed publications appearing in venues such as Science, Cell, Nature Genetics, NeurIPS, and ICML, with an h-index of 150. Daphne was recognized as one of TIME Magazine’s 100 most influential people in 2012 and Newsweek’s 10 most important people in 2010. She received the Sloan Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award in 1998, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the IJCAI Computers and Thought Award in 2001, the MacArthur Foundation Fellowship in 2004, the ACM Prize in Computing in 2008, the ACM AAAI Allen Newell Award in 2019, the IEEE CS Women of ENIAC Computer Pioneer award and the AnitaB.org Technical Leadership Abie Award Winner in 2022. Daphne was inducted into the National Academy of Sciences in 2023, the National Academy of Engineering in 2011 and elected a fellow of the American Association for Artificial Intelligence in 2004, the American Academy of Arts and Sciences in 2014 and the International Society of Computational Biology in 2017. Her teaching was recognized via the Stanford Medal for Excellence in Fostering Undergraduate Research, and as a Bass University Fellow in Undergraduate Education.
JL
Advisory Board
John Lafferty
NL
Advisory Board
Neil D Lawrence
DL
Advisory Board
Daniel Lee
TL
Advisory Board
Todd K Leen
RL
Advisory Board
Richard Lippmann
BM
Advisory Board
Bartlett Mel
Advisory Board
John Moody
MM
Advisory Board
Michael C Mozer
FP
Advisory Board
Fernando Pereira
MP
Advisory Board
Mary Ellen Perry
JP
Advisory Board
John Platt
John Platt is best known for his work in machine learning: the SMO algorithm for support vector machines and calibrating the output of models. He was an early adopter of convolutional neural networks in the 1990s. However, John has worked in many different fields: data systems, computational geometry, object recognition, media UIs, analog computation, handwriting recognition, and applied math. He has discovered two asteroids, and won a Technical Academy Award in 2006 for his work in computer graphics.
John currently leads the Applied Science branch of Google Research, which works at the intersection between computer science and physical or biological science.
John Platt is best known for his work in machine learning: the SMO algorithm for support vector machines and calibrating the output of models. He was an early adopter of convolutional neural networks in the 1990s. However, John has worked in many different fields: data systems, computational geometry, object recognition, media UIs, analog computation, handwriting recognition, and applied math. He has discovered two asteroids, and won a Technical Academy Award in 2006 for his work in computer graphics.
John currently leads the Applied Science branch of Google Research, which works at the intersection between computer science and physical or biological science.
LS
Advisory Board
Lawrence Saul
BS
Advisory Board
Bernhard Schölkopf
Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the
Technical University Berlin. He has researched at AT&T Bell Labs, at
GMD FIRST, Berlin, at the Australian National University, Canberra,
and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.
Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.
DS
Advisory Board
Dale Schuurmans
TS
Advisory Board
Terrence Sejnowski
JS
Advisory Board
John Shawe-Taylor
John Shawe-Taylor has contributed to fields ranging from graph theory through cryptography to statistical learning theory and its applications. However, his main contributions have been in the development of the analysis and subsequent algorithmic definition of principled machine learning algorithms founded in statistical learning theory. This work has helped to drive a fundamental rebirth in the field of machine learning with the introduction of kernel methods and support vector machines, driving the mapping of these approaches onto novel domains including work in computer vision, document classification, and applications in biology and medicine focussed on brain scan, immunity and proteome analysis. He has published over 300 papers and two books that have together attracted over 60000 citations.
He has also been instrumental in assembling a series of influential European Networks of Excellence. The scientific coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing.
John Shawe-Taylor has contributed to fields ranging from graph theory through cryptography to statistical learning theory and its applications. However, his main contributions have been in the development of the analysis and subsequent algorithmic definition of principled machine learning algorithms founded in statistical learning theory. This work has helped to drive a fundamental rebirth in the field of machine learning with the introduction of kernel methods and support vector machines, driving the mapping of these approaches onto novel domains including work in computer vision, document classification, and applications in biology and medicine focussed on brain scan, immunity and proteome analysis. He has published over 300 papers and two books that have together attracted over 60000 citations. He has also been instrumental in assembling a series of influential European Networks of Excellence. The scientific coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing.
YS
Advisory Board
Yoram Singer
SS
Advisory Board
Sara A Solla
Sara A. Solla obtained a BSc in Mathematics and Physics and a MSc in Physics at the University of Buenos Aires in Argentina, and a PhD in Physics at the University of Washington in Seattle, where she trained in statistical physics, critical phenomena, and renormalization group. While a postdoc at Cornell University in Ithaca, she heard John Hopfield talk about memory storage and retrieval through attractor dynamics, and life was never the same. She spent fruitful and exciting years as member of the legendary Neural Networks group at Bell Laboratories. She then joined the faculty at Northwestern University, where she is Professor of neuroscience and Professor of Physics and Astronomy. Her work in theoretical and computational neuroscience uses conceptual, mathematical, and computational frameworks from statistical physics, statistical inference, and nonlinear dynamics to investigate information processing in the brain at the systems level.
Sara A. Solla obtained a BSc in Mathematics and Physics and a MSc in Physics at the University of Buenos Aires in Argentina, and a PhD in Physics at the University of Washington in Seattle, where she trained in statistical physics, critical phenomena, and renormalization group. While a postdoc at Cornell University in Ithaca, she heard John Hopfield talk about memory storage and retrieval through attractor dynamics, and life was never the same. She spent fruitful and exciting years as member of the legendary Neural Networks group at Bell Laboratories. She then joined the faculty at Northwestern University, where she is Professor of neuroscience and Professor of Physics and Astronomy. Her work in theoretical and computational neuroscience uses conceptual, mathematical, and computational frameworks from statistical physics, statistical inference, and nonlinear dynamics to investigate information processing in the brain at the systems level.
MS
Advisory Board
Masashi Sugiyama
GT
Advisory Board
Gerald Tesauro
ST
Advisory Board
Sebastian Thrun
DT
Advisory Board
David S Touretzky
Research Professor in the Computer Science Department and the Center for the Neural Basis of Cognition at Carnegie Mellon University, Pittsburgh PA.
Research Professor in the Computer Science Department and the Center for the Neural Basis of Cognition at Carnegie Mellon University, Pittsburgh PA.
UV
Advisory Board
Ulrike von Luxburg
UV
Advisory Board
Ulrike von Luxburg
YW
Advisory Board
Yair Weiss
Yair Weiss is an Associate Professor at the Hebrew University School of Computer Science and Engineering. He received his Ph.D. from MIT working with Ted Adelson on motion analysis and did postdoctoral work at UC Berkeley. Since 2005 he has been a fellow of the Canadian Institute for Advanced Research. With his students and colleagues he has co-authored award winning papers in NIPS (2002),ECCV (2006), UAI (2008) and CVPR (2009).
Yair Weiss is an Associate Professor at the Hebrew University School of Computer Science and Engineering. He received his Ph.D. from MIT working with Ted Adelson on motion analysis and did postdoctoral work at UC Berkeley. Since 2005 he has been a fellow of the Canadian Institute for Advanced Research. With his students and colleagues he has co-authored award winning papers in NIPS (2002),ECCV (2006), UAI (2008) and CVPR (2009).
MW
Advisory Board
Max Welling
CW
Advisory Board
Chris Williams
RZ
Advisory Board
Richard Zemel
MB
Executive Board
Marian S Bartlett
CC
Executive Board
Corinna Cortes
Executive Board
Isabelle Guyon
Isabelle Guyon recently joined Google Brain as a research scientist. She is also professor of artificial intelligence at Université Paris-Saclay (Orsay). Her areas of expertise include computer vision, bioinformatics, and power systems. She is best known for being a co-inventor of Support Vector Machines. Her recent interests are in automated machine learning, meta-learning, and data-centric AI. She has been a strong promoter of challenges and benchmarks, and is president of ChaLearn, a non-profit dedicated to organizing machine learning challenges. She is community lead of Codalab competitions, a challenge platform used both in academia and industry. She co-organized the “Challenges in Machine Learning Workshop” @ NeurIPS between 2014 and 2019, launched the "NeurIPS challenge track" in 2017 while she was general chair, and pushed the creation of the "NeurIPS datasets and benchmark track" in 2021, as a NeurIPS board member.
Isabelle Guyon recently joined Google Brain as a research scientist. She is also professor of artificial intelligence at Université Paris-Saclay (Orsay). Her areas of expertise include computer vision, bioinformatics, and power systems. She is best known for being a co-inventor of Support Vector Machines. Her recent interests are in automated machine learning, meta-learning, and data-centric AI. She has been a strong promoter of challenges and benchmarks, and is president of ChaLearn, a non-profit dedicated to organizing machine learning challenges. She is community lead of Codalab competitions, a challenge platform used both in academia and industry. She co-organized the “Challenges in Machine Learning Workshop” @ NeurIPS between 2014 and 2019, launched the "NeurIPS challenge track" in 2017 while she was general chair, and pushed the creation of the "NeurIPS datasets and benchmark track" in 2021, as a NeurIPS board member.
NL
Executive Board
Neil Lawrence
NL
Executive Board
Neil D Lawrence
DL
Executive Board
Daniel Lee
MM
Executive Board
Michael Mozer
TS
Executive Board
Terrence Sejnowski
MS
Executive Board
Masashi Sugiyama
UV
Executive Board
Ulrike von Luxburg
UV
Executive Board
Ulrike von Luxburg
MW
Executive Board
Max Welling
NC
Program Co-chair
Nicolò Cesa-Bianchi
KG
Program Co-chair
Kristen Grauman
HL
Program Co-chair
Hugo Larochelle
HW
Program Chair
Hanna Wallach
SK
Tutorial Chair
Samuel Kaski
Tutorial Chair
Jennifer Wortman Vaughan
Jenn Wortman Vaughan is a Senior Principal Research Manager at Microsoft Research, New York City, where she studies responsible AI with a focus on transparency, fairness, evaluation, and human-AI interaction. Originally trained in machine learning and algorithmic economics, she now often draws on methods from human-computer interaction to investigate how people engage with AI systems. Before joining MSR in 2012, Jenn completed her Ph.D. at the University of Pennsylvania and was an Assistant Professor of Computer Science at UCLA and a Computing Innovation Fellow at Harvard. Her work has been recognized with the NSF CAREER Award, the Presidential Early Career Award for Scientists and Engineers (PECASE), and Penn’s Rubinoff dissertation award. Beyond her research, Jenn has helped shape the field through her mentorship of junior researchers, her leadership in roles including Program Co-Chair of NeurIPS and FAccT, and as co-founder of the Workshop on Women in Machine Learning (WiML), held annually since 2006.
Jenn Wortman Vaughan is a Senior Principal Research Manager at Microsoft Research, New York City, where she studies responsible AI with a focus on transparency, fairness, evaluation, and human-AI interaction. Originally trained in machine learning and algorithmic economics, she now often draws on methods from human-computer interaction to investigate how people engage with AI systems. Before joining MSR in 2012, Jenn completed her Ph.D. at the University of Pennsylvania and was an Assistant Professor of Computer Science at UCLA and a Computing Innovation Fellow at Harvard. Her work has been recognized with the NSF CAREER Award, the Presidential Early Career Award for Scientists and Engineers (PECASE), and Penn’s Rubinoff dissertation award. Beyond her research, Jenn has helped shape the field through her mentorship of junior researchers, her leadership in roles including Program Co-Chair of NeurIPS and FAccT, and as co-founder of the Workshop on Women in Machine Learning (WiML), held annually since 2006.
JQ
Workshop Chair
Joaquin Quiñonero-Candela
SS
Workshop Chair
Suchi Saria
Suchi Saria is an assistant professor of computer science, health policy and statistics at Johns Hopkins University. Her research interests are in statistical machine learning and computational healthcare. Specifically, her focus is in designing novel data-driven computing tools for optimizing decision-making. Her work is being used to drive electronic surveillance for reducing adverse events in the inpatient setting and individualize disease management in chronic diseases. She received her PhD from Stanford University with Prof. Daphne Koller.
Her work has received recognition in the form of two cover articles in Science Translational Medicine (2010, 2015), paper awards by the the Association for Uncertainty in Artificial Intelligence (2007) and the American Medical Informatics Association (2011), an Annual Scientific Award by the Society of Critical Care Medicine (2014), a Rambus Fellowship (2004-2010), an NSF Computing Innovation fellowship (2011), and competitive awards from the Gordon and Betty Moore Foundation (2013), and Google Research (2014). In 2015, she was selected by the IEEE Intelligent Systems to the AI's 10 to Watch'' list. In 2016, she was selected as a DARPA Young Faculty awardee and to Popular Science'sBrilliant 10’’.
Suchi Saria is an assistant professor of computer science, health policy and statistics at Johns Hopkins University. Her research interests are in statistical machine learning and computational healthcare. Specifically, her focus is in designing novel data-driven computing tools for optimizing decision-making. Her work is being used to drive electronic surveillance for reducing adverse events in the inpatient setting and individualize disease management in chronic diseases. She received her PhD from Stanford University with Prof. Daphne Koller.
Her work has received recognition in the form of two cover articles in Science Translational Medicine (2010, 2015), paper awards by the the Association for Uncertainty in Artificial Intelligence (2007) and the American Medical Informatics Association (2011), an Annual Scientific Award by the Society of Critical Care Medicine (2014), a Rambus Fellowship (2004-2010), an NSF Computing Innovation fellowship (2011), and competitive awards from the Gordon and Betty Moore Foundation (2013), and Google Research (2014). In 2015, she was selected by the IEEE Intelligent Systems to the AI's 10 to Watch'' list. In 2016, she was selected as a DARPA Young Faculty awardee and to Popular Science'sBrilliant 10’’.
SE
Demonstration Chair
Sergio Escalera
Sergio Escalera obtained the P.h.D. degree on Multi-class visual categorization systems at Computer Vision Center, UAB. He obtained the 2008 best Thesis award on Computer Science at Universitat Autònoma de Barcelona. He leads the Human Pose Recovery and Behavior Analysis Group at UB, CVC, and the Barcelona Graduate School of Mathematics. He is an associate professor at the Department of Mathematics and Informatics, Universitat de Barcelona. He is an adjunct professor at Universitat Oberta de Catalunya, Aalborg University, and Dalhousie University. He has been visiting professor at TU Delft and Aalborg Universities. He is a member of the Visual and Computational Learning consolidated research group of Catalonia. He is also a member of the Computer Vision Center at Campus UAB. He is Editor-in-Chief of American Journal of Intelligent Systems and editorial board member of more than 5 international journals. He is advisor, director, and vice-president of ChaLearn Challenges in Machine Learning, leading ChaLearn Looking at People events. He is co-founder of PhysicalTech and Care Respite companies. He is also member of the AERFAI Spanish Association on Pattern Recognition, ACIA Catalan Association of Artificial Intelligence, and he is vice-chair of IAPR TC-12: Multimedia and visual information systems. He has different patents and registered models. He has published more than 150 research papers and participated in the organization of scientific events, including CCIA04, CCIA14, ICCV11, AMDO2016, FG2017, and workshops at ICCV11, ICMI13, ECCV14, CVPR15, ICCV15, CVPR16, ECCV16, ICPR16, NIPS16. He has been guest editor at JMLR, TPAMI, IJCV, TAC, and Neural Comp. and App. He has been area chair at WACV16, NIPS16, and FG17. His research interests include, between others, statistical pattern recognition, visual object recognition, and HCI systems, with special interest in human pose recovery and behavior analysis from multi-modal data.
Sergio Escalera obtained the P.h.D. degree on Multi-class visual categorization systems at Computer Vision Center, UAB. He obtained the 2008 best Thesis award on Computer Science at Universitat Autònoma de Barcelona. He leads the Human Pose Recovery and Behavior Analysis Group at UB, CVC, and the Barcelona Graduate School of Mathematics. He is an associate professor at the Department of Mathematics and Informatics, Universitat de Barcelona. He is an adjunct professor at Universitat Oberta de Catalunya, Aalborg University, and Dalhousie University. He has been visiting professor at TU Delft and Aalborg Universities. He is a member of the Visual and Computational Learning consolidated research group of Catalonia. He is also a member of the Computer Vision Center at Campus UAB. He is Editor-in-Chief of American Journal of Intelligent Systems and editorial board member of more than 5 international journals. He is advisor, director, and vice-president of ChaLearn Challenges in Machine Learning, leading ChaLearn Looking at People events. He is co-founder of PhysicalTech and Care Respite companies. He is also member of the AERFAI Spanish Association on Pattern Recognition, ACIA Catalan Association of Artificial Intelligence, and he is vice-chair of IAPR TC-12: Multimedia and visual information systems. He has different patents and registered models. He has published more than 150 research papers and participated in the organization of scientific events, including CCIA04, CCIA14, ICCV11, AMDO2016, FG2017, and workshops at ICCV11, ICMI13, ECCV14, CVPR15, ICCV15, CVPR16, ECCV16, ICPR16, NIPS16. He has been guest editor at JMLR, TPAMI, IJCV, TAC, and Neural Comp. and App. He has been area chair at WACV16, NIPS16, and FG17. His research interests include, between others, statistical pattern recognition, visual object recognition, and HCI systems, with special interest in human pose recovery and behavior analysis from multi-modal data.
RH
Demonstration Chair
Ralf Herbrich
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Press Chairs
Katherine Gorman
Katherine Gorman is a podcast producer. After a decade in public radio she helped to found the podcast Talking Machines of which she is now the co-host and executive producer. She is the head of podcasting for Collective Next, where she develops shows and solves creates communication solutions.
Katherine Gorman is a podcast producer. After a decade in public radio she helped to found the podcast Talking Machines of which she is now the co-host and executive producer. She is the head of podcasting for Collective Next, where she develops shows and solves creates communication solutions.
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Press Chairs
Neil D Lawrence
RG
Publications Chair
Roman Garnett
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Diversity, Inclusion & Accessibility Chair
Hal Daumé III
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Diversity, Inclusion & Accessibility Chair
Katherine Heller
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Party Chair
Douglas Eck
I am a Senior Research Director at Google, and lead research efforts at Google DeepMind in Generative Media, including image, video, 3D, music and audio generation. My own research lies at the intersection of machine learning and human-computer interaction (HCI). In 2015, I created Magenta, an ongoing research project exploring the role of AI in art and music creation. Before joining Google in 2010, I carried out research in music perception, aspects of music performance, machine learning for large audio datasets and music recommendation. I completed my PhD in Computer Science and Cognitive Science at Indiana University in 2000 and went on to a postdoctoral fellowship with Juergen Schmidhuber at IDSIA in Lugano Switzerland. From 2003-2010, I was faculty in Computer Science in the University of Montreal machine learning group (now MILA machine learning lab, where he became Associate Professor.
For more information see http://g.co/research/douglaseck.
I am a Senior Research Director at Google, and lead research efforts at Google DeepMind in Generative Media, including image, video, 3D, music and audio generation. My own research lies at the intersection of machine learning and human-computer interaction (HCI). In 2015, I created Magenta, an ongoing research project exploring the role of AI in art and music creation. Before joining Google in 2010, I carried out research in music perception, aspects of music performance, machine learning for large audio datasets and music recommendation. I completed my PhD in Computer Science and Cognitive Science at Indiana University in 2000 and went on to a postdoctoral fellowship with Juergen Schmidhuber at IDSIA in Lugano Switzerland. From 2003-2010, I was faculty in Computer Science in the University of Montreal machine learning group (now MILA machine learning lab, where he became Associate Professor. For more information see http://g.co/research/douglaseck.
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