Institute for Human-Centered Artificial Intelligence (HAI)
Showing 1-10 of 20 Results
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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.
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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. -
Sang Kyun Cha
Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)
BioDr. Sang Kyun Cha is a pioneer in in-memory database technology and a leader in data science and AI education. He earned his Ph.D. from Stanford University, where he conducted early research on generative natural language question answering over structured databases—an early precursor to today’s conversational AI systems. Dr. Cha began his faculty career at Seoul National University (SNU) in the 1990s. Inspired by the rise of Korea’s semiconductor memory industry, he shifted his research focus toward enterprise-scale in-memory database systems. With no prior experience in systems software research, he turned to the Stanford lecture notes on transaction processing by Turing Award winner Jim Gray as his primary guide.
After several years of pioneering research, Dr. Cha founded Transact In Memory, Inc. in Silicon Valley to challenge the incumbent players in the database industry. The company was quietly acquired by SAP in late 2005. As a founding chief architect of SAP HANA, he led the development of the revolutionary in-memory database platform that helped transform SAP into a global innovator in enterprise software. In 2014, after witnessing SAP run its own business on SAP HANA, Dr. Cha returned to SNU, where he founded the Big Data Institute (now the AI Institute) and later established the Graduate School of Data Science, serving as its founding dean.
In recognition of his significant contributions across academia and industry, Dr. Cha received the prestigious Samsung Ho-Am Prize in Engineering in 2022. Beyond academia, he has played a vital role in fostering innovation and entrepreneurship through active involvement with both corporate and government sectors. His current research and leadership focus is on Enterprise AI, aiming to help organizations redesign their structures and operations for the AI era. As the agentic AI emerges as the new software paradigm for building proactive, self-learning, lightweight, modular enterprise operating systems, Dr. Cha founded a new startup to serve the agentification needs of large manufacturing companies. -
Zach Chandler
Director of Open Source 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.
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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.
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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
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Jonathan H. Chen, MD, PhD
Associate Professor of Medicine (Computational Medicine) and of Biomedical Data Science
Current Research and Scholarly InterestsInformatics solutions ares the only credible approach to systematically address challenges of escalating complexity in healthcare. Tapping into real-world clinical data streams like electronic medical records will reveal the community's latent knowledge in a reproducible form. Delivering this back as clinical decision support will uniquely close the loop on a continuously learning health system.
