Stanford University
Showing 17,451-17,500 of 35,011 Results
-
Wei Li
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioSummary
From his Cornell supercomputing PhD to nearly three decades at Intel—the last decade as VP/GM of AI & Analytics—Dr. Wei Li is a Stanford Adjunct Professor partnering with researchers, founders, VCs, and boards to bridge AI innovation with enterprise-scale impact.
Current Academic & Clinical Focus
As an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) and Affiliate Faculty at the Center for Artificial Intelligence in Medicine and Imaging (AIMI), Dr. Li specializes in AI strategy, enterprise governance, and commercial scale. His work centers on Innovation to Impact (I2I) and Trusted AI—bridging the gap between frontier technical research, clinical adoption, and real-world deployment. He collaborates across Stanford Engineering, Medicine, and Stanford Health Care as a strategic architect and educator for AI deployment in high-stakes environments.
Enterprise & Open Source Leadership
Prior to Stanford, Dr. Li spent nearly three decades at Intel Corporation, serving as Vice President and General Manager of AI & Analytics. In this role, he led global engineering teams across 10+ countries building full-stack AI software platforms powering enterprise-scale infrastructure. A long-time advocate for open science and industry oversight, he previously served on the governing boards of the PyTorch Foundation and Linux Foundation AI & Data alongside leaders from Meta, Google, OpenAI, Hugging Face, and Microsoft.
Scholarship & Academic Background
Dr. Li holds a Ph.D. in Computer Science from Cornell University in supercomputing research and completed executive education at the Stanford Graduate School of Business. He is a recipient of the ACM ASPLOS Best Paper Award, a former Associate Editor for ACM TOPLAS, and served as the Intel Executive Sponsor for Cornell University while sitting on the funding committee overseeing $20M in parallel computing research at UC Berkeley and UIUC.
Keynotes & Media Contributions
A prominent voice on AI strategy, enterprise deployment, and governance, Dr. Li is a frequent keynote speaker at major global forums—including CES, the World AI Summit, GITEX, and Harvard Business School. He also regularly contributes expert commentary to leading business and technology outlets, including Bloomberg, Forbes, and Fortune. -
Wen Li
Basic Life Research Scientist, Pathology Operations supported expenses #2
BioWen’s research focuses on mitochondrial reverse electron transport (RET) in neurodegenerative diseases where tau pathology plays either a primary or secondary pathogenic role. Her work seeks to understand how pathological tau disrupts mitochondrial complex I function and RET, and how these alterations contribute to oxidative stress and neurodegeneration. Her research further explores whether targeting RET can provide a shared therapeutic strategy across tau-associated neurodegenerative diseases.
-
Xiang Li
Assoc Sci - Ultrafast AMO, LCLS - Linac Coherent Light Source
BioI am a scientist in the AMO Sciences Department and Data Systems Division at the LCLS. Investigating the ultrafast processes in atoms and molecules with charged-particle spectroscopy at x-ray free-electron lasers is the major theme of my research. It consists of three interconnected endeavors. One is to understand the material response to ultra-intense x-rays at the atomic level, and another is to exploit such x-rays as the probe for unraveling photo-induced molecular dynamics. And the third is to develop machine learning algorithms for solving some of the bottleneck problems in our field. I am involved in the design, assembly, and operation of experimental endstations at the AMO beamline of the LCLS, as well as the software development for AMO experiments performed at free-electron laser facilities.
-
Yijie (Jamie) Li
Postdoctoral Scholar, Anesthesiology, Perioperative and Pain Medicine
BioI am a postdoctoral scholar at Stanford University, working on longitudinal wearable data to study interventions for diabetes and childhood obesity. I use computational and machine learning methods to extract actionable insights from high-resolution health data to improve treatment outcomes. Previously, I completed my Ph.D. in Computer Science at the University of Tulsa, focusing on machine learning for major depressive disorder using genomics, gene age, and neurofeedback. I also hold master’s degrees in Applied Economics and Finance from UC Santa Cruz and in Accountancy from the University of Tulsa, where I worked on financial modeling and stock market analysis.
-
Zewen (Evan) Li
Graduate, Stanford Center for Professional Development
BioZewen (Evan) Li, Deep Learning Engineer @NVIDIA (Santa Clara) & Online Grad student @Stanford, received M.S. degree from Carnegie Mellon University, B.E. degree from Computer Science and Technology of Hohai University.
