Point Pinole Regional Shoreline (Jan. 2017)
Dan Yamins, PhD
principal investigator
Dan joined Stanford in September 2016. He's a member of the Departments of Psychology and Computer Science, the Wu Tsai Neurosciences Institute, and the Stanford Artificial Intelligence Laboratory. He is a recipient of the McDonnell Foundation Scholar, Sloan Research Fellowship, and NSF CAREER awards, and is a Simons Foundation Investigator. Prior to moving to Stanford, he did his postdoc at MIT in the Department of Brain and Cognitive Sciences. He received his BA in Mathematics and PhD in Applied Mathematics from Harvard University.
principal investigator
Dan joined Stanford in September 2016. He's a member of the Departments of Psychology and Computer Science, the Wu Tsai Neurosciences Institute, and the Stanford Artificial Intelligence Laboratory. He is a recipient of the McDonnell Foundation Scholar, Sloan Research Fellowship, and NSF CAREER awards, and is a Simons Foundation Investigator. Prior to moving to Stanford, he did his postdoc at MIT in the Department of Brain and Cognitive Sciences. He received his BA in Mathematics and PhD in Applied Mathematics from Harvard University.
Dan Bear, PhD
postdoctoral researcher
Is there more than one way to see a cat? For difficult problems in vision, like recognizing objects and high- properties in a complex scene, evolution and machine-learning methods have arrived at a similar solution. I am exploring whether this is true in general: Are the algorithms implemented in our brain tailored to perform particular tasks? And if we find out which tasks, can we train artificial neural networks to act like we do? These questions will illuminate the links between natural and artificial intelligence.
Before coming to SNAIL, I received an A.B. in Molecular and Cellular Biology and a Ph.D. in Neuroscience, both from Harvard University.
postdoctoral researcher
Is there more than one way to see a cat? For difficult problems in vision, like recognizing objects and high- properties in a complex scene, evolution and machine-learning methods have arrived at a similar solution. I am exploring whether this is true in general: Are the algorithms implemented in our brain tailored to perform particular tasks? And if we find out which tasks, can we train artificial neural networks to act like we do? These questions will illuminate the links between natural and artificial intelligence.
Before coming to SNAIL, I received an A.B. in Molecular and Cellular Biology and a Ph.D. in Neuroscience, both from Harvard University.
Judy Fan, PhD
postdoctoral researcher
Judy earned her PhD in cognitive psychology from Princeton University in 2016, and her AB in neurobiology from Harvard College in 2010. She is interested in how visual perception, action, and social inference are coordinated to support learning and communication. After spending the 2018-2019 academic year here at Stanford, she will start her own group in the psychology department at UCSD.
postdoctoral researcher
Judy earned her PhD in cognitive psychology from Princeton University in 2016, and her AB in neurobiology from Harvard College in 2010. She is interested in how visual perception, action, and social inference are coordinated to support learning and communication. After spending the 2018-2019 academic year here at Stanford, she will start her own group in the psychology department at UCSD.
Nick Haber, PhD
postdoctoral researcher
Nick received his PhD in mathematics from Stanford University in 2013, and subsequently worked on the Autism Glass project. He joined the lab in January 2017.
postdoctoral researcher
Nick received his PhD in mathematics from Stanford University in 2013, and subsequently worked on the Autism Glass project. He joined the lab in January 2017.
Kevin Feigelis
PhD student (Physics)
What are the factors which contribute to a flexible intelligence, and how do they arise? Does there exist a set of emergent topologies which facilitate plasticity? If so, what is the mathematical basis underlying their function, and how may we harness them computationally? My research in the Stanford Neuro-AI Lab focuses on addressing these issues by integrating a wealth of techniques from Machine Learning and Neuroscience, to Physics and Mathematics. I hold a degree in Physics from Rutgers University, and currently I am a Ph.D candidate in the Stanford University Physics department. Outside of academia, I enjoy writing, reading, playing music, hiking, and drinking questionable amounts of coffee.
PhD student (Physics)
What are the factors which contribute to a flexible intelligence, and how do they arise? Does there exist a set of emergent topologies which facilitate plasticity? If so, what is the mathematical basis underlying their function, and how may we harness them computationally? My research in the Stanford Neuro-AI Lab focuses on addressing these issues by integrating a wealth of techniques from Machine Learning and Neuroscience, to Physics and Mathematics. I hold a degree in Physics from Rutgers University, and currently I am a Ph.D candidate in the Stanford University Physics department. Outside of academia, I enjoy writing, reading, playing music, hiking, and drinking questionable amounts of coffee.
Eshed Margalit
PhD student (Neuroscience)
Eshed is a PhD student in the Neurosciences Graduate Program. His interests include the spatial organization and function of high-level visual cortex and the development of biologically-inspired neural networks. Prior to his work at Stanford, Eshed studied models of early visual cortex, developmental prosopagnosia, and the representation of objects in the lateral occipital complex.
PhD student (Neuroscience)
Eshed is a PhD student in the Neurosciences Graduate Program. His interests include the spatial organization and function of high-level visual cortex and the development of biologically-inspired neural networks. Prior to his work at Stanford, Eshed studied models of early visual cortex, developmental prosopagnosia, and the representation of objects in the lateral occipital complex.
Josh Melander
PhD student (Neuroscience)
Josh was trained in cellular biology and physiology at Whitman College (Walla Walla, WA) and the Vollum Institue (Portland, OR). Co-mentored by Steve Baccus (Neurobiology) Josh applies his passion for experimental science to problems at the interface of neurobiology and machine learning. Combiningin vivo physiology (optical and electrical recording) and state-of-the-art machine-learning techniques, Josh studies the strategies that biological systems (neurons and circuits) employ to facilitate flexible and robust behavior in natural environments.
PhD student (Neuroscience)
Josh was trained in cellular biology and physiology at Whitman College (Walla Walla, WA) and the Vollum Institue (Portland, OR). Co-mentored by Steve Baccus (Neurobiology) Josh applies his passion for experimental science to problems at the interface of neurobiology and machine learning. Combining
Damian Mrowca
PhD student (Computer Science)
Damian received his BA (2012) and MSc (2015) in Electrical Engineering and Information Theory, both from the Technical University of Munich. During 2014-2015 he was a visiting student in machine learning at UC Berkeley. After a year in start-up land, he joined the lab in September 2016.
PhD student (Computer Science)
Damian received his BA (2012) and MSc (2015) in Electrical Engineering and Information Theory, both from the Technical University of Munich. During 2014-2015 he was a visiting student in machine learning at UC Berkeley. After a year in start-up land, he joined the lab in September 2016.
Aran Nayebi
PhD student (Neuroscience)
Aran is a PhD student in the Neurosciences Graduate Program. He earned his Master's degree in Computer Science (AI Specialization), as well as both undergraduate majors in Mathematics and Symbolic Systems, from Stanford University. His primary interests lie at the intersection of machine learning and neuroscience, where he uses tools from deep learning and statistics to approach problems in systems neuroscience. He has also previously worked in theoretical computer science and mathematical logic.
PhD student (Neuroscience)
Aran is a PhD student in the Neurosciences Graduate Program. He earned his Master's degree in Computer Science (AI Specialization), as well as both undergraduate majors in Mathematics and Symbolic Systems, from Stanford University. His primary interests lie at the intersection of machine learning and neuroscience, where he uses tools from deep learning and statistics to approach problems in systems neuroscience. He has also previously worked in theoretical computer science and mathematical logic.
Elias Wang
PhD student (Electrical Engineering)
Elias received his BS in Electrical and Computer Engineering from Cornell University in 2016 and is now pursing his PhD in Electrical Engineering. His interests involve the understanding of natural intelligence and the creation of artificial intelligence. In particular, he hopes to build intelligent systems that are more general, flexible, and robust.
PhD student (Electrical Engineering)
Elias received his BS in Electrical and Computer Engineering from Cornell University in 2016 and is now pursing his PhD in Electrical Engineering. His interests involve the understanding of natural intelligence and the creation of artificial intelligence. In particular, he hopes to build intelligent systems that are more general, flexible, and robust.
Chengxu Zhuang
PhD student (Psychology)
Chengxu received his BEng in Electrical Engineering and BS in Mathematics from Tsinghua University in 2016. He joined the lab in September 2016.
PhD student (Psychology)
Chengxu received his BEng in Electrical Engineering and BS in Mathematics from Tsinghua University in 2016. He joined the lab in September 2016.
Lab Affiliates
Stephanie Wang
research associate
Stephanie graduated in 2017 with her BS in Symbolic Systems from Stanford University. She is now a Masters student in Computer Science studying artificial intelligence and machine learning. Previously she worked with Stanford's InfoLab on weak supervision.
research associate
Stephanie graduated in 2017 with her BS in Symbolic Systems from Stanford University. She is now a Masters student in Computer Science studying artificial intelligence and machine learning. Previously she worked with Stanford's InfoLab on weak supervision.
Nisa Cao
administrative assistant
administrative assistant
Lab Alumni
Lawrence Peirson
rotation student (Physics)
Lawrence is a PhD student in the Physics Graduate Program. Having spent time researching accelerator beam dynamics at CERN and plasma containment at the University of Science and Technology of China, he received his MPhys from the University of Oxford in 2017. Normally interested in theoretical astrophysics (namely compact objects), he now fixes his heavenly gaze earthwards to facilitating the design of novel learning systems.
rotation student (Physics)
Lawrence is a PhD student in the Physics Graduate Program. Having spent time researching accelerator beam dynamics at CERN and plasma containment at the University of Science and Technology of China, he received his MPhys from the University of Oxford in 2017. Normally interested in theoretical astrophysics (namely compact objects), he now fixes his heavenly gaze earthwards to facilitating the design of novel learning systems.
Blue Belmont Sheffer
research assistant
Blue is a recent graduate of the Symbolic Systems Program at Stanford University (B.17) where he studied computational neuroscience and machine learning. While at SNAIL, Blue worked on building modular continual learning agents and PTUtils, a package for reproducible deep learning with PyTorch. He currently works on brain-machine interfaces in Stanford's Neural Prosthetics Systems Laboratory.
research assistant
Blue is a recent graduate of the Symbolic Systems Program at Stanford University (B.17) where he studied computational neuroscience and machine learning. While at SNAIL, Blue worked on building modular continual learning agents and PTUtils, a package for reproducible deep learning with PyTorch. He currently works on brain-machine interfaces in Stanford's Neural Prosthetics Systems Laboratory.