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Poster Session
Ethan Harris · Tom White · Oh Hyeon Choung · Takashi Shinozaki · Dipan Pal · Katherine L. Hermann · Judy Borowski · Camilo Fosco · Chaz Firestone · Vijay Veerabadran · Benjamin Lahner · Chaitanya Ryali · Fenil Doshi · Pulkit Singh · Sharon Zhou · Michel Besserve · Michael Chang · Anelise Newman · Mahesan Niranjan · Jonathon Hare · Daniela Mihai · Marios Savvides · Simon Kornblith · Christina M Funke · Aude Oliva · Virginia de Sa · Dmitry Krotov · Colin Conwell · George Alvarez · Alex Kolchinski · Shengjia Zhao · Mitchell Gordon · Michael Bernstein · Stefano Ermon · Arash Mehrjou · Bernhard Schölkopf · John Co-Reyes · Michael Janner · Jiajun Wu · Josh Tenenbaum · Sergey Levine · Yalda Mohsenzadeh · Zhenglong Zhou

Fri Dec 13 02:00 PM -- 03:00 PM (PST) @
in Shared Visual Representations in Human and Machine Intelligence »

Author Information

Ethan Harris (University of Southampton)
Tom White (University of Wellington School of Design)

Tom is a New Zealand based artist investigating machine perception. His current work focuses on creating physical artworks that highlight how machines “see” and thus how they think, suggesting that these systems are capable of abstraction and conceptual thinking. He has exhibited computer based artwork internationally over the past 25 years with themes of artificial intelligence, interactivity, and computational creativity. He is currently a lecturer and researcher at University of Wellington School of Design where he teaches students the creative potential of computer programming and artificial intelligence.

Oh Hyeon Choung (École Polytechnique Fédérale de Lausanne (EPFL))
Takashi Shinozaki (NICT CiNet)
Dipan Pal (Carnegie Mellon University)
Katherine L. Hermann (Stanford University)
Judy Borowski (University of Tuebingen)
Camilo Fosco (Massachusetts Institute of Technology)
Chaz Firestone (Johns Hopkins University)
Vijay Veerabadran (University of California, San Diego)

Ph.D. candidate in Cognitive Science with an interest in biological and artificial intelligence (particularly interested in vision). I work on developing recurrent neural networks for efficient learning of long-range spatial dependencies and on quantifying behavioral similarity between human and machine vision.

Benjamin Lahner (MIT)

I investigate human audition and vision using fMRI and MEG to better understand how humans make sense of their world. I then look for ways to apply our understanding of human perception to develop novel AI solutions. I am a graduate student at MIT under the direction of Aude Oliva.

Chaitanya Ryali (UC San Diego)
Fenil Doshi (Harvard University)
Pulkit Singh (Princeton University)

I am a senior in the Computer Science department at Princeton University, interested in machine learning, computational cognitive science, and AI. I'm looking for full time roles post-graduation in data science and ML.

Sharon Zhou (Stanford University)
Michel Besserve (MPI for Intelligent Systems)
Michael Chang (University of California, Berkeley)

Ph.D. student at Berkeley AI Research, U.C. Berkeley B.S. in Computer Science from MIT Former research intern under Juergen Schmidhuber, Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA) Former undergraduate researcher under Joshua Tenenbaum and Antonio Torralba, MIT

Anelise Newman (Massachusetts Institute of Technology)
Mahesan Niranjan (University of Southampton)
Jonathon Hare (University of Southampton)
Daniela Mihai (University of Southampton)
Marios Savvides (Carnegie Mellon University)
Simon Kornblith (Google Brain)
Christina M Funke (University of Tuebingen)
Aude Oliva (MIT)
Virginia de Sa (University of California, San Diego)
Dmitry Krotov (IBM Research)
Colin Conwell (Harvard University)
George Alvarez (Harvard University)
Alex Kolchinski (Stanford University)
Shengjia Zhao (Stanford University)
Mitchell Gordon (Stanford University)
Michael Bernstein (Stanford University)
Stefano Ermon (Stanford)
Arash Mehrjou (Max Planck Institute for Intelligent Systems)

I am a PhD student of Machine Learning at Max Planck Institute for Intelligent Systems working at Empirical Inference Group under supervision of Prof. Bernhard Scholkopf.

Bernhard Schölkopf (MPI for Intelligent Systems)

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.

John Co-Reyes (UC Berkeley)
Michael Janner (UC Berkeley)
Jiajun Wu (MIT)

Jiajun Wu is a fifth-year Ph.D. student at Massachusetts Institute of Technology, advised by Professor Bill Freeman and Professor Josh Tenenbaum. His research interests lie on the intersection of computer vision, machine learning, and computational cognitive science. Before coming to MIT, he received his B.Eng. from Tsinghua University, China, advised by Professor Zhuowen Tu. He has also spent time working at research labs of Microsoft, Facebook, and Baidu.

Josh Tenenbaum (MIT)

Josh Tenenbaum is an Associate Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 1999, and was an Assistant Professor at Stanford University from 1999 to 2002. He studies learning and inference in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. He focuses on problems of inductive generalization from limited data -- learning concepts and word meanings, inferring causal relations or goals -- and learning abstract knowledge that supports these inductive leaps in the form of probabilistic generative models or 'intuitive theories'. He has also developed several novel machine learning methods inspired by human learning and perception, most notably Isomap, an approach to unsupervised learning of nonlinear manifolds in high-dimensional data. He has been Associate Editor for the journal Cognitive Science, has been active on program committees for the CogSci and NIPS conferences, and has co-organized a number of workshops, tutorials and summer schools in human and machine learning. Several of his papers have received outstanding paper awards or best student paper awards at the IEEE Computer Vision and Pattern Recognition (CVPR), NIPS, and Cognitive Science conferences. He is the recipient of the New Investigator Award from the Society for Mathematical Psychology (2005), the Early Investigator Award from the Society of Experimental Psychologists (2007), and the Distinguished Scientific Award for Early Career Contribution to Psychology (in the area of cognition and human learning) from the American Psychological Association (2008).

Sergey Levine (UC Berkeley)
Yalda Mohsenzadeh (The University of Western Ontario)
Zhenglong Zhou (University of Pennsylvania)

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