Stanford Data Science

Showing 1-10 of 10 Results

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  Emmanuel Candes

  Barnum-Simons Chair of Math and Statistics, and Professor of Statistics and, 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.

  • Contact Info

  • Web page:

    http://www-stat.stanford.edu/~candes
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  Laura Gwilliams

  Assistant Professor of Psychology and, by courtesy, of Linguistics
  

  BioLaura Gwilliams is jointly appointed between Stanford Psychology, Wu Tsai Neurosciences Institute and Stanford Data Science. Her work is focused on understanding the neural representations and operations that give rise to speech comprehension in the human brain. To do so, she brings together insight from neuroscience, linguistics and machine learning, and takes advantage of recording techniques that operate at distinct spatial scales (MEG, ECoG and Neuropixels).
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  Brian Hie

  Assistant Professor of Chemical Engineering
  

  BioI am an Assistant Professor of Chemical Engineering at Stanford University, the Dieter Schwarz Foundation Stanford Data Science Faculty Fellow, and an Innovation Investigator at Arc Institute. I supervise the Laboratory of Evolutionary Design, where we conduct research at the intersection of biology and machine learning.
  
  I was previously a Stanford Science Fellow in the Stanford University School of Medicine and a Visiting Researcher at Meta AI. I completed my Ph.D. at MIT CSAIL and was an undergraduate at Stanford University.
• Guido Imbens

  Applied Econometrics Professor, Professor of Economics and Senior Fellow at the Stanford Institute for Economic Policy Research
  
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  Ramesh Johari

  Professor of Management Science and Engineering and, by courtesy, of Electrical Engineering
  

  BioJohari is broadly interested in the design, economic analysis, and operation of online platforms, as well as statistical and machine learning techniques used by these platforms (such as search, recommendation, matching, and pricing algorithms).

  • Contact Info

  • Web page:

    http://web.stanford.edu/people/rjohari
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  David Lobell

  Benjamin M. Page Professor, William Wrigley Senior Fellow at the Freeman Spogli Institute, at the Woods Institute for the Environment and at the Stanford Institute for Economic Policy Research
  

  Current Research and Scholarly InterestsWe study the interactions between food production, food security, and the environment using a range of modern tools.
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  Russell Poldrack

  Albert Ray Lang Professor of Psychology and, by courtesy, of Psychiatry and Behavioral Science
  

  Current Research and Scholarly InterestsOur lab uses the tools of cognitive neuroscience to understand how decision making, executive control, and learning and memory are implemented in the human brain. We also develop neuroinformatics tools and resources to help researchers make better sense of data.
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  Chiara Sabatti

  Professor of Biomedical Data Science and of Statistics
  

  Current Research and Scholarly InterestsStatistical models and reasoning are key to our understanding of the genetic basis of human traits. Modern high-throughput technology presents us with new opportunities and challenges. We develop statistical approaches for high dimensional data in the attempt of improving our understanding of the molecular basis of health related traits.
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  Ludwig Schmidt

  Assistant Professor of Computer Science
  

  BioLudwig Schmidt is an assistant professor at Stanford University in the Computer Science Department and Stanford Data Science. Ludwig’s research interests revolve around the empirical foundations of machine learning, often with a focus on datasets, reliable generalization, multimodality, and language models. Recently, Ludwig’s research group contributed to open source machine learning by creating OpenCLIP, DCLM, and the LAION-5B dataset. Ludwig completed his PhD at MIT and was a postdoc at UC Berkeley. Ludwig’s research received a new horizons award at EAAMO, best paper awards at ICML & NeurIPS, a best paper finalist at CVPR, and the Sprowls dissertation award from MIT.
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  Brian Trippe

  Assistant Professor of Statistics and, by courtesy, of Computer Science
  

  BioDr. Brian Trippe is an assistant professor at Stanford in the Department of Statistics, with an affiliation in Stanford Data Science.
  
  In his research, Dr. Trippe develops probabilistic machine learning methods to address challenges in biotechnology and medicine. Recently, his focus has been on generative modeling and inference algorithms for protein engineering.
  
  Before joining Stanford, Dr. Trippe was a postdoctoral fellow at Columbia University in the Department of Statistics, and a visiting researcher at the Institute for Protein Design at the University of Washington.