Institute for Human-Centered Artificial Intelligence (HAI)
Showing 21-40 of 188 Results
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Stephen Boyd
Samsung Professor in the School of Engineering
BioStephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory at Stanford University, and a member of the Institute for Computational and Mathematical Engineering. His current research focus is on convex optimization applications in control, signal processing, machine learning, and finance.
Professor Boyd received an AB degree in Mathematics, summa cum laude, from Harvard University in 1980, and a PhD in EECS from U. C. Berkeley in 1985. In 1985 he joined Stanford's Electrical Engineering Department. He has held visiting Professor positions at Katholieke University (Leuven), McGill University (Montreal), Ecole Polytechnique Federale (Lausanne), Tsinghua University (Beijing), Universite Paul Sabatier (Toulouse), Royal Institute of Technology (Stockholm), Kyoto University, Harbin Institute of Technology, NYU, MIT, UC Berkeley, CUHK-Shenzhen, and IMT Lucca. He holds honorary doctorates from Royal Institute of Technology (KTH), Stockholm, and Catholic University of Louvain (UCL).
Professor Boyd is the author of many research articles and four books: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least-Squares (with Lieven Vandenberghe, 2018), Convex Optimization (with Lieven Vandenberghe, 2004), Linear Matrix Inequalities in System and Control Theory (with El Ghaoui, Feron, and Balakrishnan, 1994), and Linear Controller Design: Limits of Performance (with Craig Barratt, 1991). His group has produced many open source tools, including CVX (with Michael Grant), CVXPY (with Steven Diamond) and Convex.jl (with Madeleine Udell and others), widely used parser-solvers for convex optimization.
He has received many awards and honors for his research in control systems engineering and optimization, including an ONR Young Investigator Award, a Presidential Young Investigator Award, and the AACC Donald P. Eckman Award. In 2013, he received the IEEE Control Systems Award, given for outstanding contributions to control systems engineering, science, or technology. In 2012, Michael Grant and he were given the Mathematical Optimization Society's Beale-Orchard-Hays Award, for excellence in computational mathematical programming. In 2023, he was given the AACC Richard E. Bellman Control Heritage Award, the highest recognition of professional achievement for U.S. control systems engineers and scientists. He is a Fellow of the IEEE, SIAM, INFORMS, and IFAC, a Distinguished Lecturer of the IEEE Control Systems Society, a member of the US National Academy of Engineering, a foreign member of the Chinese Academy of Engineering, and a foreign member of the National Academy of Engineering of Korea. He has been invited to deliver more than 90 plenary and keynote lectures at major conferences in control, optimization, signal processing, and machine learning.
He has developed and taught many undergraduate and graduate courses, including Signals & Systems, Linear Dynamical Systems, Convex Optimization, and a recent undergraduate course on Matrix Methods. His graduate convex optimization course attracts around 300 students from more than 20 departments. In 1991 he received an ASSU Graduate Teaching Award, and in 1994 he received the Perrin Award for Outstanding Undergraduate Teaching in the School of Engineering. In 2003, he received the AACC Ragazzini Education award, for contributions to control education. In 2016 he received the Walter J. Gores award, the highest award for teaching at Stanford University. In 2017 he received the IEEE James H. Mulligan, Jr. Education Medal, for a career of outstanding contributions to education in the fields of interest of IEEE, with citation "For inspirational education of students and researchers in the theory and application of optimization." -
Erik Brynjolfsson
Jerry Yang and Akiko Yamazaki Professor, Senior Fellow at Stanford Institute for Human-Centered Artificial Intelligence, 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.
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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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Danton Char
Associate 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
Assistant Professor (Research) of Radiology (Integrative Biomedical Imaging Informatics at Stanford) and, by courtesy, 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 range from developing novel data-efficient machine learning algorithms to clinical deployment and validation of patient outcomes. He is also exploring combining imaging with clinical, natural language, and time series data.
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Jonathan H. Chen, MD, PhD
Assistant Professor of Medicine (Biomedical Informatics)
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.
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Angele Christin
Associate Professor of Communication and, by courtesy, of Sociology
Current Research and Scholarly InterestsAngèle Christin studies how algorithms and analytics transform professional values, expertise, and work practices.
Her book, Metrics at Work: Journalism and the Contested Meaning of Algorithms (Princeton University Press, 2020) focuses on the case of web journalism, analyzing the growing importance of audience data in web newsrooms in the U.S. and France. Drawing on ethnographic methods, Angèle shows how American and French journalists make sense of traffic numbers in different ways, which in turn has distinct effects on the production of news in the two countries. She discussed it on the New Books Network podcast.
In a related study, she analyzed the construction, institutionalization, and reception of predictive algorithms in the U.S. criminal justice system, building on her previous work on the determinants of criminal sentencing in French courts.
Her new project examines the paradoxes of algorithmic labor through a study of influencers and influencer marketing on YouTube, Instagram, and TikTok. -
Geoffrey Cohen
James G. March Professor of Organizational Studies in Education and Business, Professor of Psychology and, by courtesy, of Organizational Behavior at the Graduate School of Business
Current Research and Scholarly InterestsMuch of my research examines processes related to identity maintenance and their implications for social problems. One primary aim of my research is the development of theory-driven, rigorously tested intervention strategies that further our understanding of the processes underpinning social problems and that offer solutions to alleviate them. Two key questions lie at the core of my research: “Given that a problem exists, what are its underlying processes?” And, “Once identified, how can these processes be overcome?” One reason for this interest in intervention is my belief that a useful way to understand psychological processes and social systems is to try to change them. We also are interested in how and when seemingly brief interventions, attuned to underlying psychological processes, produce large and long-lasting psychological and behavioral change.
The methods that my lab uses include laboratory experiments, longitudinal studies, content analyses, and randomized field experiments. One specific area of research addresses the effects of group identity on achievement, with a focus on under-performance and racial and gender achievement gaps. Additional research programs address hiring discrimination, the psychology of closed-mindedness and inter-group conflict, and psychological processes underlying anti-social and health-risk behavior. -
Nicholas Alvaro Coles
Research Scientist
BioI am a Research Scientist at Stanford University and the co-Director of the Stanford Big Team Science Lab. I conduct research on emotions, big team science, and AI.
In affective science, I seek to understand the social, cognitive, and physiological processes that underlie emotion. Much of my research here has focused on examining the extent to which sensorimotor feedback from the peripheral nervous system (e.g., changes in heart rate and muscle tension) impact the conscious experience of emotion.
In big team science, I seek to build infrastructure that allows researchers to collaboratively tackle ultra-complex questions in science. In this domain, I co-direct the Stanford Big Team Science Lab, where I support various big team science initiatives (e.g., the Virtual Experience Research Accelerator, Psychological Science Accelerator, and ManyBabies Consortium).
In artificial intelligence, I am interested in ways that these new technologies can be used to monitor, predict, and change humans' emotions. For example, I recently founded the Emotion Physiology and Experience Collaboration, which seeks to improve the algorithmic recognition of emotion by (a) documenting cultural and contextual sources of model bias, and (b) building foundational datasets that can improve these models. -
Steven Hartley Collins
Associate Professor of Mechanical Engineering and, by courtesy, of Bioengineering
BioSteve Collins is an Associate Professor of Mechanical Engineering at Stanford University, where he teaches courses on design and robotics and directs the Stanford Biomechatronics Laboratory. His primary focus is to speed and systematize the design and prescription of prostheses and exoskeletons using versatile device emulator hardware and human-in-the-loop optimization algorithms (Zhang et al. 2017, Science). Another interest is efficient autonomous devices, such as highly energy-efficient walking robots (Collins et al. 2005, Science) and exoskeletons that use no energy yet reduce the metabolic energy cost of human walking (Collins et al. 2015, Nature).
Prof. Collins received his B.S. in Mechanical Engineering in 2002 from Cornell University, where he performed undergraduate research on passive dynamic walking robots. He received his Ph.D. in Mechanical Engineering in 2008 from the University of Michigan, where he performed research on the dynamics and control of human walking. He performed postdoctoral research on humanoid robots at T. U. Delft in the Netherlands. He was a professor of Mechanical Engineering and Robotics at Carnegie Mellon University for seven years. In 2017, he joined the faculty of Mechanical Engineering at Stanford University.
Prof. Collins is a member of the Scientific Board of Dynamic Walking and the Editorial Board of Science Robotics. He has received the Young Scientist Award from the American Society of Biomechanics, the Best Medical Devices Paper from the International Conference on Robotics and Automation, and the student-voted Professor of the Year in his department. -
Gary Darmstadt
Professor (Teaching) of Pediatrics (Neonatology) and, by courtesy, of Obstetrics and Gynecology
Current Research and Scholarly InterestsI have extensive experience in the development of global health innovations and in working to test and scale-up health interventions. At Stanford University, I am playing a leading role in developing global women and children’s health research and educational programs, including the establishment of a Global Center for Gender Equality at Stanford University. My research focuses on advancing child health and development in low resource settings and advancing gender equality and health globally, and includes several applications of artificial intelligence. Before joining Stanford, I was Senior Fellow at the Bill & Melinda Gates Foundation (BMGF), where I led the development of initiatives to address gender inequalities and empower women and girls. Prior to this role, I served as the BMGF Director of Family Health, leading strategy development and implementation across maternal, newborn and child health, nutrition, and family planning. In this role, I was responsible for investments ranging from scientific discovery to intervention development and delivery of interventions at scale. I worked closely with the Discovery team to shape discovery and development investments and was a co-founder of the Saving Lives at Birth Development Grand Challenge, the Putting Women and Girls at the Center of Development Grand Challenge, and the Healthy Birth, Growth and Development initiative. Based on these experiences, I understand how to identify knowledge gaps and generate evidence of impact for new interventions, and how to utilize evidence to influence the policy dialogue leading to programmatic adoption and scale-up of interventions in low income settings. As Director of Family Health, I also co-led the development and implementation of the BMGF global health strategy for India, which cuts across multiple health and development sectors. Before joining BMGF, I was Associate Professor and Founding Director of the International Center for Advancing Neonatal Health in the Department of International Health at the Johns Hopkins Bloomberg School of Public Health. I led the development of newborn health research, including numerous facility- and community-based maternal and child health research trials. Before joining Johns Hopkins, I was Senior Research Advisor for the $50M Saving Newborn Lives program of Save the Children-US, where I led the development and implementation of the global research strategy for newborn health and survival.
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Giulio De Leo
Professor of Oceans, of Earth System Science, Senior Fellow at the Woods Institute for the Environment and Professor, by courtesy of Biology
Current Research and Scholarly InterestsI am a theoretical ecologist mostly interested in investigating factors and processes driving the dynamics of natural and harvested populations and on how to use this knowledge to inform practical management. I have worked broadly on life histories analysis, fishery management, dynamics and control of infectious diseases and environmental impact assessment.
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Larry Diamond
Mosbacher Senior Fellow of Global Democracy at the Freeman Spogli Institute for International Studies, Senior Fellow at the Hoover Institution and Professor, by courtesy, of Sociology and of Political Science
Current Research and Scholarly Interestsdemocratic development and regime change; U.S. foreign policy affecting democracy abroad; comparative trends in the quality and stability of democracy in developing countries and postcommunist states; and public opinion in new democracies, especially in East Asia
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Ron Dror
Cheriton Family Professor and Professor, by courtesy, of Structural Biology and of Molecular & Cellular Physiology
Current Research and Scholarly InterestsMy lab’s research focuses on computational biology, with an emphasis on 3D molecular structure. We combine two approaches: (1) Bottom-up: given the basic physics governing atomic interactions, use simulations to predict molecular behavior; (2) Top-down: given experimental data, use machine learning to predict molecular structures and properties. We collaborate closely with experimentalists and apply our methods to the discovery of safer, more effective drugs.
