School of Engineering
Showing 6,921-6,940 of 7,352 Results
-
Lisa Yamada
Ph.D. Student in Electrical Engineering, admitted Autumn 2015
BioLisa Yamada is a PhD candidate in Electrical Engineering, working with Professor Paul Nuyujukian in the Brain Interfacing Laboratory at Stanford University. She is interested in applying data science and engineering tools for medical applications towards higher quality and more equitable care. As a computational neuroscientist and clinical research coordinator, she is currently investigating quantitative measures for seizure analyses using human neuroelectrophysiology data (e.g., intracortical EEGs of participants with refractory epilepsy). She graduated from Trinity College (Hartford, CT) with BS degrees in Electrical Engineering and Mathematics and earned her MS in Electrical Engineering from Stanford University. In her free time, she enjoys outdoor activities like hiking.
-
Yoshihisa Yamamoto
Professor of Electrical Engineering and of Applied Physics, Emeritus
Current Research and Scholarly InterestsExperimental Quantum Optics, Semiconductor Physics, Quantum Information
-
Yusuke Yamashita
Postdoctoral Scholar, Aeronautics and Astronautics
BioYusuke Yamashita is a Postdoctoral Researcher of Aeronautics and Astronautics at Stanford University. He received a Ph.D. and M.S. in Aerospace Engineering and a Graduate Certificate in Engineering from the University of Tokyo, and B.S. in Aeronautics and Astronautics from Osaka Prefecture University. He obtained fellowship as a Japan Society for the Promotion of Science. His interests include electric propulsion, low-temperature plasmas, microwave and plasma interaction, and computational plasma physics. He received Second place award in student competition at International Electric Propulsion Conference, and Japanese Rocket Society Award at 33rd International Symposium on Space Technology and Science.
-
Daniel Yamins
Assistant Professor of Psychology and of Computer Science
Current Research and Scholarly InterestsOur lab's research lies at intersection of neuroscience, artificial intelligence, psychology and large-scale data analysis. It is founded on two mutually reinforcing hypotheses:
H1. By studying how the brain solves computational challenges, we can learn to build better artificial intelligence algorithms.
H2. Through improving artificial intelligence algorithms, we'll discover better models of how the brain works.
We investigate these hypotheses using techniques from computational modeling and artificial intelligence, high-throughput neurophysiology, functional brain imaging, behavioral psychophysics, and large-scale data analysis. -
Michael Yan
Undergraduate, Computer Science
Biohttps://michaelyan.com/
