Artificial Intelligence and the Brain: a KIBM Symposium

Event Dates: 
May 3, 2019 -
1:00pm to 5:30pm

Co-Chairs:
Terrence Sejnowski, Salk Institute for Biological Studies
Hava Siegelmann, Defense Advanced Research Projects Agency

Registration for the event is required and can be completed by following this link: https://kibmai2019.eventbrite.com

Limited hosted event parking will be provided at the Sanford Consortium. Please arrive early to ensure a parking spot. If the hosted parking is full, attendees must self-pay for parking.

Invited Speakers:

DiCarlo

Jim DiCarlo

Peter de Florez Professor & Department Head, Brain & Cognitive Sciences
Massachusetts Institute of Technology
dicarlolab.mit.edu

James DiCarlo is the Peter De Florez Professor of Neuroscience, head of the Department of Brain and Cognitive Sciences, and McGovern Institute for Brain Research Investigator at the Massachusetts Institute of Technology. He received his Ph.D. in biomedical engineering and his M.D. from Johns Hopkins University in 1998, and did his postdoctoral training in primate visual neurophysiology at Baylor College of Medicine. He joined the Massachusetts Institute of Technology faculty in 2002. He is an Alfred P. Sloan Research Fellow, a Pew Scholar in the Biomedical Sciences, and a McKnight Scholar in Neuroscience. The research goal of Dr. DiCarlo’s group is a computational understanding of the brain mechanisms that underlie object recognition.

Bethge

Matthias Bethge

Professor of Computational Neuroscience & Chair of the Bernstein Center for Computational Neuroscience Tübingen
Eberhard Karls University of Tübingen
bethgelab.org

Matthias Bethge did his postdoctoral training at the Redwood Neuroscience Institute and University of California, Berkeley before he started working in computational neuroscience when he joined the Max-Planck-Institute for dynamics and self-organization for his diploma project. Since then his research aims at understanding perceptual inference and self-organized collective information processing in distributed systems — two puzzling phenomena that contribute much to our fascination about living systems.

Sussillo

David Sussillo

Computational Neuroscience Researcher
Google Brain
ai.google/research/people/DavidSussillo

David Sussillo (PhD in computational neuroscience, Columbia University, 2009) is a research scientist in the Google Brain Group, since 2014. Previously, he was a post-doctoral researcher at Stanford University. David has studied computational principles underlying recurrent neural network function, and has contributed functional models for motor cortex physiology and decision making in the pre-frontal cortex. Current David is working at the intersection of deep learning and neuroscience.

Tye

Kay Tye

Professor of Systems Neuroscience Laboratory
The Salk Institute for Biological Studies
tyelab.org/kay-m-tye

Kay M. Tye received her bachelor’s degree in Brain and Cognitive Sciences from MIT in 2003 and earned her Ph.D. in 2008 at UCSF with Patricia Janak. She completed her postdoctoral training with Karl Deisseroth at Stanford University in 2011. She became an Assistant Professor at MIT in 2012, and has since been recognized with the NIH Director’s New Innovator Award, Technology Review’s Top 35 Innovators under 35, and has been named a Whitehall, Klingenstein and Sloan Foundation Fellow.

Siegelmann

Hava Siegelmann

Program Manager at the Microsystems Technology Office
Defense Advanced Research Projects Agency
www.darpa.mil/staff/dr-hava-siegelmann

Dr. Siegelmann (Ph.D. in Computer Science at Rutgers University) is a program manager at the MTO of DARPA, developing programs to advance the fields of Neural Networks and Machine Learning. Her research into neural processes has led to theoretical modeling and original algorithms capable of superior computation, and to more realistic, human-like intelligent systems. Dr. Siegelmann acts as a consultant internationally with industry and education. She remains very active in supporting young researchers and encouraging minorities and women to enter and advance in STEM.

Banino

Andrea Banino

Machine Learning Researcher
DeepMind Technologies

Andrea Banino is a machine learning researcher working on artificial general intelligence at DeepMind Technologies. His research focuses primarily on spatial navigation using a deep learning system — a type of AI inspired by the structures in the brain and is working towards the philosophy that algorithms used for AI can meaningfully approximate elements of the brain.

Sejnowski

Terrence Sejnowski

Francis Crick Professor at The Salk Institute for Biological Studies & Director of the Crick-Jacobs Center for Theoretical and Computational Biology
The Salk Institute for Biological Studies
www.salk.edu/scientist/terrence-sejnowski

Terrence Sejnowski received his B.S in Physics from Case Western Reserve University and earned his Ph.D. at Princeton University. The long-range goal of Sejnowski's research is to understand the computational resources of brains and to build linking principles from brain to behavior using computational models. While other scientists have focused on mapping the physical arrangement of neurons, Sejnowski is interested in a more functional mapping of the brain, one that looks at how sets of cells are involved in a processes — from filtering what we see to recalling memories.

Seitz

Aaron Seitz

Professor of Psychology & Director of Brain Game Center at University of California, Riverside
University of California, Riverside
faculty.ucr.edu/~aseitz

Aaron Seitz received his B.A in Mathematics at Reed College in 1994 and his Ph.D. in Cognitive and Neural Systems at Boston University in 2003. Currently, he is the director of the UCR Brain Game Center, which conducts research, tests, and disseminates evidence-based, scientifically optimized brain fitness games that transfer benefits to real life activities. His research aims to clarify the rules by which the brain solves the stability-plasticity dilemma and apply these rules to induce desirable blain plasticity.

Goodfellow

Ian Goodfellow

Machine Learning Researcher
Google Brain
www.iangoodfellow.com

Ian Goodfellow (PhD in machine learning, University of Montreal, 2014) is a research scientist at Google. His research interests include most deep learning topics, especially generative models and machine learning security and privacy. He invented generative adversarial networks, was an influential early researcher studying adversarial examples, and is the lead author of the MIT Press Textbook Deep Learning (www.deeplearningbook.org). He runs the Self-Organizing Conference on Machine Learning, which was founded at OpenAI in 2016.