Center for Mind, Brain, Computation and Technology

Ava is a Ph.D. student in the Stanford Biomechatronics Lab, where she works on lower limb exoskeletons to improve mobility. Her research interests center around human-robot co-adaptation: how people learn to walk with exoskeletons, and how exoskeletons can best adapt to human behavior. A deep proponent of interdisciplinary work, she studies at the intersection of neuroscience, biomechanics, robotics, and controls while drawing on the principles of user-centered design, psychology, and disability studies.

Sreela Kodali is an EE PhD student at Stanford University and recipient of the National Science Foundation Graduate Research Fellowship. She graduated from Princeton University in 2018 with a BSE in Electrical Engineering and is interested in hardware-software co-design for brain computer interfaces. In her current work with Professors Subhasish Mitra and E.J. Chichilnisky, she is developing an embedded hardware system to emulate the neural code for high-acuity vision restoration.

(He, Him)

Pronouns: he/him/his

I am a PhD student working with Anthony Wagner in the Department of Psychology. I leverage neuroimaging (fMRI/scalp EEG) and real-time biofeedback with pupillometry to investigate the neural mechanisms driving the relationship between moment-to-moment fluctuations in preparatory attention and episodic remembering in cognitively healthy young and older adults. Prior to Stanford, I earned both my MS in Evolutionary Biology (2021), and BS in Cognitive Science and Biology (2019), at UCLA.

Corey is a Neuroscience PhD Student co-advised by Anthony Wagner and Lisa Giocomo. She is interested in the processes that organize memory representations into ‘cognitive maps’ that guide future behavior. Her work combines fMRI, virtual reality navigation tasks, and neural pattern similarity analyses to examine how we integrate new experiences with existing memory representations and flexibly update our knowledge across learning.

Shaw is a Biophysics PhD student in the Wagner Lab. He is interested in memory processes and their interactions with selective attention. His work uses a combination of behavioral, neuroimaging, and electrophysiological methods.

Kevin T. Feigelis is a PhD candidate at Stanford University in the Department of Physics. A New Jersey native, he attended Rutgers University where he was awarded his Bachelor's in Physics, Summa Cum Laude. He is currently a member of the Stanford Neuroscience and Artificial Intelligence Laboratory (SNAIL), directed by Prof. Daniel Yamins. Kevin's research is directed at addressing a fundamental question that lies at the intersection of Artificial Intelligence and Neuroscience: What neural mechanisms enable humans to be such wonderfully adaptive learners?

Akshay Jagadeesh is a 4th year PhD student studying cognitive neuroscience, advised by Justin Gardner. His research examines how attention enhances the resolution of visual perception, with a particular emphasis on the neural representation of visual textures. In the future, Akshay hopes to continue to build more sophisticated biologically-plausible deep neural network models of visual perception that account for cognitive factors such as attention.

I am a Neurosciences PhD student and NDSEG fellow working with Dr. Leanne Williams and Dr. Kalanit Grill-Spector. My research focuses on understanding how our brains allocate attention to help us reach our goals and how these processes can become impaired in individuals experiencing mental illness. Toward this goal I use a variety of methods including functional magnetic resonance imaging, electro-encephalography, clinical reports, human genetics, and computational analysis techniques. Previously, I studied multisensory attention with Dr. Robert Sekuler and earned my co-terminal M.S.

I am currently a PhD student in the Neurosciences Graduate Program, co-advised by Surya Ganguli and Dan Yamins. Previously, I completed my M.S. in Computer Science and B.S. in Mathematics & Symbolic Systems at Stanford. My primary interests lie at the intersection of machine learning and neuroscience, where I use tools from deep learning to approach problems in systems neuroscience. Specifically, I am interested in using what we know about the visual system (e.g. the abundance of recurrent connections) to build more biologically realistic models of the ventral visual pathway.