Hailing originally from the Midwest, Jeffrey is currently an MD/PhD candidate in Stanford’s Biophysics Program. He studied Applied Mathematics as an undergraduate at Harvard University, working with Chris Rycroft on developing numerical methods for modeling biomechanical processes and large biological networks. Since then, Jeffrey’s interests have expanded towards mapping neural circuits mediating the effects of anesthetic and psychiatric drugs.
I am a graduate student in the Applied Physics Department. I received my bachelor's degree in physics from the University of Washington in Seattle. I am interested in nonequilibrium statistical physics and understanding the relationship between thermodynamics and information processing, particularly in biological computation.
Tyler received an ScB in Applied Math-Economics from Brown University in 2014. Previously, he developed a machine learning method for assessing ex vivo retinal acuity in Russel Van Gelder's lab at University of Washington Medicine. In parallel, Tyler served as program chair for more than 25 international conferences in 3D printing and robotics, and is a founding partner at Asimov Ventures. He is currently a PhD student in the Neurosciences Graduate Program at Stanford, and is co-advised by Karl Deisseroth in Bioengineering and Shaul Druckmann in Neurobiology.
As a biologist and a musician, I was drawn to neuroscience with the following question: how does the brain distill complex features in our sensory environment into abstractions and make predictions that guide our behavior? I think about this question while flipping fruit fly vials in the fly room, while singing with Stanford Chamber Chorale, and while talking with my advisors, Shaul Druckmann and Tom Clandinin.
Kyle Yoshida was born and raised in Hawaii. He is currently pursuing his Mechanical Engineering PhD in the Collaborative Haptics and Robotics in Medicine Lab. His current research spans soft robotics and wearable haptic devices. He is a member of the American Indian Science and Engineering Society and the Society for Advancement of Chicanos/Hispanics and Native Americans in Science. In his free time, he outlines science fiction short stories.
Libby is developing statistical frameworks for modelling animal behavior. Experimental neuroscience relies heavily on observed animal behavior -- individual and repeated patterns of actions in response to internal and external stimuli -- to study neural dynamics. These investigations, however, are limited by insufficient objective and analytical descriptions of physical behavior. Libby's work on more efficient estimations methods for joint detection and tracking aims to directly contribute to the systematic investigation of behavioral action patterns and their neural correlates.
Krishnan is a second year PhD candidate advised by Jeannette Bohg in the Computer Science department. His primary research interests include robotics, reinforcement learning, and learning from demonstration, and has previously worked on interactive perception, in-hand manipulation, and human-robot interaction. One of his current research directions is recording and classifying human manipulation skills and primitives that can be extended to robotic manipulation strategies, and identifying what level of structural priors are needed to make learning efficient and practical.
I received my B.Sc. in Electrical Engineering from San Martín de Porres University (2012) in Chiclayo, Peru and received my Masters degree in Digital Signal and Image Processing from Pontificia Universidad Católica del Perú (2017). Between 2015 and 2018, I worked on ultrasound imaging, optimization and signal processing. From 2017 to 2018, I was a research assistant at the Digital Signal Processing laboratory in the same institution, where I was involved with topics of image processing, optimization and inverse problems.