Vice Provost and Dean of Research
Showing 1-20 of 22 Results
Affiliate, Stanford Digital Economy Lab (S-DEL)
BioSagit Bar-Gill is an assistant professor of Technology Management and Information Systems at Tel Aviv University's Coller School of Management, and a Fellow at Stanford's Digital Economy Lab and MIT’s Initiative on the Digital Economy.
Sagit's research is centered around the economics of online markets, and the impacts of digitization on firms and consumers. Her work combines economic modeling, experiments and data analytics to understand the online economy.
Michele Barry, MD, FACP
Drs. Ben & A. Jess Shenson Professor, Senior Associate Dean, Global Health, Director, Center for Innovation in Global Health, Professor of Medicine and Senior Fellow at the Woods Institute
Current Research and Scholarly InterestsAreas of research
Ethical Aspects of research conducted overseas
Clinical Tropical Diseases
Globalization's Impact upon Health Disparities
Associate Professor of Operations, Information and Technology at the Graduate School of Business and, by courtesy, of Electrical EngineeringOn Leave from 03/15/2021 To 12/31/2021
Current Research and Scholarly Interests1) Healthcare management: I am interested in improving healthcare delivery using data-driven modeling and decision-making.
2) Network models and message-passing algorithms: I work on graphical modeling ideas motivated from statistical physics and their applications in statistical inference.
3) Personalized decision-making: I work on machine learning and statistical challenges of personalized decision-making. The problems that I have worked on are primarily motivated by healthcare applications.
Professor of Developmental Biology, of Computer Science, of Pediatrics (Genetics) and of Biomedical Data Science
Current Research and Scholarly Interests1. Automating monogenic patient diagnosis.
2. The genomic signatures of independent divergent and convergent trait evolution in mammals.
3. The logic of human gene regulation.
4. The reasons for sequence ultraconservation.
5. Cryptogenomics to bridge medical silos.
6. Cryptogenetics to debate social injustice.
7. Managing patient risk using machine learning.
8. Understanding the flow of money in the US healthcare system.
Associate Professor of Medicine (Primary Care and Population Health) and Senior Fellow at the Woods Institute for the Environment
Current Research and Scholarly InterestsEffect of global health policies on health of individuals in developing countries, global health, HIV and TB.
Professor of Pediatrics (Genetics) and, by courtesy, of Genetics
Current Research and Scholarly InterestsMy research is focused on the diagnosis, discovery and delineation of rare genetic conditions with a focus of neurodevelopmental disorders. This work includes the application of novel computational methods and multi-omics profiling (whole genome sequencing, RNA sequencing, metabolomics). I additionally participate in an interdisciplinary project to develop induced pluripotent stem cell (iPSC) models of genetic neurodevelopmental disorders..
Associate Professor of Computer ScienceOn Partial Leave from 10/01/2021 To 06/30/2022
BioMichael Bernstein is an Associate Professor of Computer Science and STMicroelectronics Faculty Scholar at Stanford University, where he is a member of the Human-Computer Interaction group. His research focuses on the design of social computing and crowdsourcing systems. Michael has received eight best paper awards at premier computing venues, and he has been recognized with an NSF CAREER award and an Alfred P. Sloan Fellowship. His Ph.D. students have gone on both to industry (e.g., Adobe Research, Facebook Data Science, entrepreneurship) and faculty careers (e.g., Carnegie Mellon, UC Berkeley). Michael holds a bachelor's degree in Symbolic Systems from Stanford University, as well as a master's degree and a Ph.D. in Computer Science from MIT.
Nomellini and Olivier Professor in the Graduate School of Education
Current Research and Scholarly InterestsStudying the Impact of a Mathematical Mindset Summer Intervention, HapCaps: Design and Validation of Haptic Devices for improving Finger Perception (with engineering & neuroscience) The effectiveness of a student online class (https://lagunita.stanford.edu/courses/Education/EDUC115-S/Spring2014/about) (NSF). Studies on mathematics and mindset with Carol Dweck and Greg Walton (various funders). Studying an online network and it's impact on teaching and learning (Gates foundation)
Assistant Professor of Computer Science
BioJeannette Bohg is an Assistant Professor of Computer Science at Stanford University. She was a group leader at the Autonomous Motion Department (AMD) of the MPI for Intelligent Systems until September 2017. Before joining AMD in January 2012, Jeannette Bohg was a PhD student at the Division of Robotics, Perception and Learning (RPL) at KTH in Stockholm. In her thesis, she proposed novel methods towards multi-modal scene understanding for robotic grasping. She also studied at Chalmers in Gothenburg and at the Technical University in Dresden where she received her Master in Art and Technology and her Diploma in Computer Science, respectively. Her research focuses on perception and learning for autonomous robotic manipulation and grasping. She is specifically interesting in developing methods that are goal-directed, real-time and multi-modal such that they can provide meaningful feedback for execution and learning. Jeannette Bohg has received several awards, most notably the 2019 IEEE International Conference on Robotics and Automation (ICRA) Best Paper Award, the 2019 IEEE Robotics and Automation Society Early Career Award and the 2017 IEEE Robotics and Automation Letters (RA-L) Best Paper Award.
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. He has courtesy appointments in the Department of Management Science and Engineering and the Department of Computer Science, and is 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.
Professor Boyd 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. He is a Fellow of the IEEE, SIAM, and INFORMS, 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, with citation: “For excellence in classroom teaching, textbook and monograph preparation, and undergraduate and graduate mentoring of students in the area of systems, control, and optimization.” 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."