Institute for Human-Centered Artificial Intelligence (HAI)
Showing 31-40 of 258 Results
-
Erik Brynjolfsson
Jerry Yang and Akiko Yamazaki Professor, Senior Fellow at Stanford Institute for Human-Centered AI, at SIEPR & Professor, by courtesy, of Economics & of Operations, Information & Technology & of Economics at the GSB
BioErik Brynjolfsson is the Jerry Yang and Akiko Yamazaki Professor and Director of the Stanford Digital Economy Lab at HAI. He is also the Ralph Landau Senior Fellow at SIEPR, and a Professor, by courtesy, at the Stanford Graduate School of Business and at the Department of Economics. Prof. Brynjolfsson is a Research Associate at the National Bureau of Economic Research and co-author of six books, including The Second Machine Age. His research, teaching and speaking focus on the effects of digital technologies, including AI, on the economy and business.
-
Jef Caers
Professor of Earth and Planetary Sciences and, by courtesy, of Geophysics
Current Research and Scholarly InterestsMy research focuses on assuring 100% renewable energy through development of geothermal energy and critical mineral supply, developing approaches from data acquisition to decision making under uncertainty and risk assessment.
-
Emmanuel Candes
Barnum-Simons Chair of Math and Statistics, Professor of Statistics, Senior Fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI) and Professor, by courtesy, of Electrical Engineering
BioEmmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics, a professor of electrical engineering (by courtesy) and a member of the Institute of Computational and Mathematical Engineering at Stanford University. Earlier, Candès was the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology. His research interests are in computational harmonic analysis, statistics, information theory, signal processing and mathematical optimization with applications to the imaging sciences, scientific computing and inverse problems. He received his Ph.D. in statistics from Stanford University in 1998.
Candès has received several awards including the Alan T. Waterman Award from NSF, which is the highest honor bestowed by the National Science Foundation, and which recognizes the achievements of early-career scientists. He has given over 60 plenary lectures at major international conferences, not only in mathematics and statistics but in many other areas as well including biomedical imaging and solid-state physics. He was elected to the National Academy of Sciences and to the American Academy of Arts and Sciences in 2014. -
Zach Chandler
Director of Open Scholarship Strategy, Institute for Human-Centered Artificial Intelligence (HAI)
Current Role at StanfordIn collaboration with faculty and research teams, advances open science practices at Stanford and beyond. Data sharing, methods sharing, rigor & reproducibility, open access, open source software, persistent identifiers, &c. Represents Stanford at the HELIOS initiative and other fora. Identifies emerging centers of excellence, and helps good ideas achieve escape velocity. Curiosity, humility, and a thorough understanding of Joy’s Law will be part of our success.
-
Danton Char
Professor of Anesthesiology, Perioperative and Pain Medicine (Pediatric)
Current Research and Scholarly InterestsDr. Char's research is focused on identifying and addressing ethical concerns associated with the implementation of next generation technologies like whole genome sequencing and its attendant technologies like machine learning to bedside clinical care.
-
Akshay Chaudhari
Associate Professor (Research) of Radiology (Integrative Biomedical Imaging Informatics at Stanford) and of Biomedical Data Science
Current Research and Scholarly InterestsDr. Chaudhari is interested in the application of artificial intelligence techniques to all aspects of medical imaging, including automated schedule and reading prioritization, image reconstruction, quantitative analysis, and prediction of patient outcomes. His interests focus on the development and evaluation new self-supervised and representation learning techniques for multi-modal deep learning in healthcare using vision, language, and medical records data
