Stanford University
Showing 11-20 of 267 Results
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Ram Rajagopal
Associate Professor of Civil and Environmental Engineering, of Electrical Engineering and Senior Fellow at the Precourt Institute for Energy
BioRam Rajagopal is an Associate Professor of Civil and Environmental Engineering at Stanford University, where he directs the Stanford Sustainable Systems Lab (S3L), focused on large-scale monitoring, data analytics and stochastic control for infrastructure networks, in particular, power networks. His current research interests in power systems are in the integration of renewables, smart distribution systems, and demand-side data analytics.
He holds a Ph.D. in Electrical Engineering and Computer Sciences and an M.A. in Statistics, both from the University of California Berkeley, Masters in Electrical and Computer Engineering from University of Texas, Austin and Bachelors in Electrical Engineering from the Federal University of Rio de Janeiro. He is a recipient of the NSF CAREER Award, Powell Foundation Fellowship, Berkeley Regents Fellowship and the Makhoul Conjecture Challenge award. He holds more than 30 patents and several best paper awards from his work and has advised or founded various companies in the fields of sensor networks, power systems, and data analytics. -
Ashwin Ramaswami
Affiliate, Program-Liang, P.
BioAshwin is currently CTO and Co-founder at Corridor, a startup using AI to help security teams fix vulnerabilities at scale. With a CS degree at Stanford and law degree at Georgetown, Ashwin worked in the federal government at CISA on cybersecurity and election security. https://ashwin.run/
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Akash Ramdas
Postdoc Res Affiliate, Program-Homrich da Jornada, F.
Postdoc Res Affiliate, Stanford PULSE InstituteBioMy research focuses on the computational discovery of materials for electronic and energy device applications. I leverage both the physical insights provided by many-body perturbation theory–based methods and statistical inference from open materials databases using machine learning. In my research, I have demonstrated that a multi-objective optimization framework can identify experimentally viable sub-5 nm Cu interconnect alternatives, extracted theoretical insights into unconventional resistivity scaling in NbP from experimental data, significantly improved the accuracy of machine-learned interatomic potentials for moiré reconstructions, accelerated the optimization of catalytic process conditions, and identified materials with exceptionally high inductance at the atomic scale.
