Machine Learning in Neuroscience: Fundamentals and Possibilities

Organized by Kristin Branson and Edda “Floh” Thiels

June 26, 11 a.m.–5 p.m. EDT

Kristin Branson

Kristin Branson, PhD

Kristin Branson is a group leader and the head of computation and theory at the Howard Hughes Medical Institute's Janelia Research Campus. Her lab develops new machine vision and learning technologies to extract scientific understanding from large image data sets. Using these systems, Janelia Research Campus aims to gain insight into behavior and how it is generated by the nervous system. She earned her BA in computer science from Harvard University and her PhD in computer science from the University of California, San Diego, and completed postdoctoral training at the California Institute of Technology.

Beth Cimini

Beth Cimini, PhD

Beth Cimini is the lead image assay developer in the Carpenter Lab at the Broad Institute, collaborating with researchers around the world to build custom open-source image analysis workflows. She is also the co-maintainer of the lab's CellProfiler software. Always passionate about microscopy, she received her BA from Boston University while studying cholinergic signaling in the salamander retina, and her PhD in biochemistry and molecular biology from the University of California, San Francisco, studying telomere biology in cancer cells.

Sanjoy Dasgupta

Sanjoy Dasgupta, PhD

Sanjoy Dasgupta is a professor of computer science and engineering at the University of California, San Diego, where he has been since 2002. Previously, he worked in the machine learning group at AT&T Labs Research. He obtained a BA from Harvard University in 1993 and a PhD from the University of California, Berkeley. Dasgupta works on algorithmic statistics, with a particular focus on unsupervised and minimally supervised learning. He is author (with Christos Papadimitriou and Umesh Vazirani) of a textbook, Algorithms. He was program co-chair of the Conference on Learning Theory in 2009 and of the International Conference on Machine Learning in 2013.

Eyrún Eyjólfsdóttir

Eyrún Eyjólfsdóttir, PhD

Eyrún Eyjólfsdóttir is a research scientist at Vicarious, working on a computational vision system for robots. She completed her PhD at the California Institute of Technology, where she worked with Pietro Perona on automated analysis of behavior, particularly of fruit flies, in a collaboration with the David Anderson Lab. Prior to working at Caltech, she received her MS in computer science from the University of California, Santa Barbara, and her BS in mathematics from the University of Iceland.

Larissa Heinrich

Larissa Heinrich

Larissa Heinrich is a PhD student in the Saalfeld Lab at Janelia Research Campus, where she works on machine learning approaches for automated analysis of large electron microscopy datasets of neural tissue. She earned her BS and MS in physics at the University of Heidelberg, completing her thesis under Winfried Denk at the Max Planck Institute for Medical Research.

Scott Linderman

Scott Linderman, PhD

Scott Linderman is an assistant professor in the department of statistics and the Wu Tsai Neurosciences Institute at Stanford University. Prior to this, he was a postdoctoral fellow with Liam Paninski and David Blei at Columbia University. He completed his PhD in computer science at Harvard University with Ryan Adams and Leslie Valiant, and he received his BS in electrical and computer engineering from Cornell University. His research focuses on machine learning, computational neuroscience, and the general question of how computational and statistical methods can help us decipher neural computation.

Talmo Pereira

Talmo Pereira

Talmo Pereira is a PhD candidate in neuroscience at Princeton University. Advised by Mala Murthy and Joshua Shaevitz, Pereira has developed quantitative methods for studying animal behavior by employing a combination of computer vision and deep learning.



Andrew Saxe

Andrew Saxe, PhD

Andrew Saxe is a postdoctoral research associate in the department of experimental psychology at the University of Oxford. He was previously a Swartz Postdoctoral Fellow in Theoretical Neuroscience at Harvard University. His research focuses on the theory of deep learning, applied to phenomena in neuroscience and psychology. He earned a BSE in electrical engineering from Princeton University and a PhD in electrical engineering from Stanford University.

Sebastian Seung

Sebastian Seung, PhD

Sebastian Seung is Anthony B. Evnin Professor in the Neuroscience Institute and computer science department at Princeton University, and chief research scientist at Samsung Electronics. Seung has conducted influential research in computer science and neuroscience. He helped pioneer the new field of connectomics, applying deep learning and crowdsourcing to reconstruct neural circuits from electron microscopic images. His lab created EyeWire.org, a site that has recruited more than 250,000 players from 150 countries to map neural connections, and his book Connectome: How the Brain's Wiring Makes Us Who We Are was chosen by the The Wall Street Journal as one of the top ten nonfiction books of 2012. Before joining the Princeton faculty in 2014, Seung studied at Harvard University, worked at Bell Laboratories, and taught at the Massachusetts Institute of Technology. He is an external member of the Max Planck Society and the winner of the 2008 Ho-Am Prize in Engineering.

Kimberly Stachenfeld

Kim Stachenfeld, PhD

Kimberly Stachenfeld is a research scientist on DeepMind’s neuroscience team. Her main focus is on representations to support efficient reinforcement learning and planning. She works on both neuroscience and machine learning problems in that space. Her research interests include the hippocampus and entorhinal cortex, reinforcement learning (deep and otherwise), efficient representations for reinforcement learning, and, on good days, fMRI.

Floh Thiels

Floh Thiels, PhD

Edda (Floh) Thiels is an adjunct associate professor of neurobiology at the University of Pittsburgh School of Medicine and a program director in the Directorate for Biological Sciences at the National Science Foundation. Thiels’ main research interests lie in how animals acquire information from the environment and use that information to guide their behavior. She received her undergraduate degree in psychology from the University of Toronto and her PhD in psychology from Indiana University.

Srini Turaga

Srini Turaga, PhD

Srini Turaga is a group leader at the Howard Hughes Medical Institute’s Janelia Research Campus, where his lab conducts research at the intersection of machine learning and neuroscience. He earned his PhD from the Massachusetts Institute of Technology under the supervision of Sebastian Seung. His postdoctoral fellowship at the Gatsby Unit at University College London was mentored jointly by Peter Dayan and Michael Häusser. His lab is currently researching machine learning for neural data analysis and modeling, connectomics, and computational microscopy.

Nicholas Turner

Nicholas Turner

Nicholas Turner is a PhD student in the Seung lab at Princeton University. His research focuses on neural circuit reconstruction of electron microscopy volumes and analysis of the resulting data. Specifically, he designed and implemented an automated system for synapse detection and assignment which has been applied to a petascale volume of mouse visual cortex. He earned a BA in psychology from Stanford University and an MA in Computer Science from Princeton University.

Leila Wehbe

Leila Wehbe, PhD

Leila Wehbe is an assistant professor in the machine learning department at Carnegie Mellon University. Previously, she was a postdoctoral researcher in the Gallant Lab at the University of California, Berkeley. She obtained her PhD from the machine learning department and the Center for the Neural Basis of Cognition at Carnegie Mellon University, where she worked with Tom Mitchell. She studies language representations in the brain when subjects engage in naturalistic language tasks by combining functional neuroimaging with natural language processing and machine learning.

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