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.
I am interested in the question of how to optimally leverage a priori knowledge about atomic systems for machine learning on molecules. Example tasks include the design of proteins and RNA. We are developing a framework for the data-driven design of enhanced fluorescent voltage indicators.
I am a PhD student in the department of Psychology working with Kalanit Grill-Spector. I am interested in the relationship between structure and function in human visual cortex, and in particular, the anatomical structures that scaffold development. Before coming to Stanford, I completed my BA in Cognitive Science at Pomona College and was the lab manager of the Brain Development & Education Lab at the University of Washington.
I am a PhD student studying computational and cognitive neuroscience, advised by Justin Gardner. My primary interest lies in combining computational tools and psychological experiments to understand the neural mechanisms behind complex behaviors in humans. Specifically, my studies focus on how our prior understanding about visual entities affects our perception of them and, to this end, design psychophysical experiments that probe stages of the underlying neural computations. Before coming to Stanford, I studied Mathematics and Cognitive Science at Yale.
I am a PhD student in Psychology at Stanford under the advisory of Professor James L. McClelland. I am interested in systematic reasoning, or the ability to learn and apply rules and variables. My research consists of both empirical studies and computational modeling, gathering data from human experiments to understand attributes of reasoning processes and then aiming to produce similar results through neural network models. I primarily use logic puzzles (e.g. Sudoku) and instruction sets (e.g. recipes, programs) to test for systematic behaviors.