Stanford University
Showing 17,801-17,820 of 35,010 Results
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Aaron Lindenberg
Professor of Materials Science and Engineering and of Photon Science
BioLindenberg's research is focused on visualizing the ultrafast dynamics and atomic-scale structure of materials on femtosecond and picosecond time-scales. X-ray and electron scattering and spectroscopic techniques are combined with ultrafast optical techniques to provide a new way of taking snapshots of materials in motion. Current research is focused on the dynamics of phase transitions, ultrafast properties of nanoscale materials, and charge transport, with a focus on materials for information storage technologies, energy-related materials, and nanoscale optoelectronic devices.
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Christian Linder
Professor of Civil and Environmental Engineering
BioChristian Linder is a Professor of Civil and Environmental Engineering and, by courtesy, of Mechanical Engineering. Through the development of novel and efficient in-house computational methods based on a sound mathematical foundation, the research goal of the Computational Mechanics of Materials (CM2) Lab at Stanford University, led by Dr. Linder, is to understand micromechanically originated multi-scale and multi-physics mechanisms in solid materials undergoing large deformations and fracture. Applications include sustainable energy storage materials, flexible electronics, and granular materials.
Dr. Linder received his Ph.D. in Civil and Environmental Engineering from UC Berkeley, an MA in Mathematics from UC Berkeley, an M.Sc. in Computational Mechanics from the University of Stuttgart, and a Dipl.-Ing. degree in Civil Engineering from TU Graz. Before joining Stanford in 2013 he was a Junior-Professor of Micromechanics of Materials at the Applied Mechanics Institute of Stuttgart University where he also obtained his Habilitation in Mechanics. Notable honors include a Fulbright scholarship, the 2013 Richard-von-Mises Prize, the 2016 ICCM International Computational Method Young Investigator Award, the 2016 NSF CAREER Award, and the 2019 Presidential Early Career Award for Scientists and Engineers (PECASE). -
Scott W Linderman
Assistant Professor of Statistics
BioScott Linderman, PhD, is an Assistant Professor at Stanford University in the Statistics Department and the Wu Tsai Neurosciences Institute, as well as the Co-Director of the Stanford Center for Neural Data Science. His research focuses on machine learning, computational neuroscience, and the general question of how computational and statistical methods can help to decipher neural computation. His work combines novel methodological development in the areas of state space models, deep generative models, point processes, and approximate Bayesian inference with applied statistical analyses of large-scale neural and behavioral data. Previously, he was a postdoctoral fellow at Columbia University and a graduate student at Harvard University. His work has been recognized with a Savage Award from the International Society for Bayesian Analysis, an AISTATS Best Paper Award, an NSF CAREER Award, and fellowships from the McKnight, Sloan, and Simons Foundations.
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Malene Lindholm
Sr. Research Engineer, Medicine - Med/Cardiovascular Medicine
Current Research and Scholarly InterestsInterested in the genetics of human performance and the multi-omic response to exercise and training for optimizing human health.
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Steven Lindley
Professor of Psychiatry and Behavioral Sciences (Public Mental Health and Population Sciences)
Current Research and Scholarly InterestsMaximizing the use of evidence-based practices and reducing unnecessary medical burden of psychiatric treatments for stress-related disorders.
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Benjamin Lindquist
Clinical Associate Professor, Emergency Medicine
Current Research and Scholarly InterestsInternational emergency medicine development and education.
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Bruce Ling
Senior Research Scientist, Pediatrics - NeonatologyCurrent Research and Scholarly InterestsMy research focuses on developing AI-enabled translational medicine platforms that integrate real-world electronic health records, wearable biosensor signals, LC-MS/MS-based proteomics and metabolomics, cfDNA molecular profiling, and multimodal medical imaging. The overarching goal is to transform longitudinal clinical, physiological, and molecular data into predictive tools for early disease detection, dynamic risk stratification, digital twin modeling, and precision intervention.
