Data Science

Duchi, Garcia-Molina, Olukotun, Prabhakar, Rajagopal, Widom

All aspects of data and information are part of this research, including how to collect, store, organize, search, and analyse information. Recently there has been energized interest in information management because huge volumes of data are now available from sources such as web query logs, Twitter posts, blogs, satellites, sensors, and medical devices. The interest is not solely due to the volume, but because there has been a paradigm shift in the way data is used. In the past, data was used to verify hypotheses; today, mining data for patterns and trends leads to new hypotheses. The more data available, the finer and more sophisticated these hypotheses can be. Examples include:

  • Managing uncertain/approximate data and models,
  • Tracking data lineage,
  • Automated data cleansing (e.g., entity resolution, graph alignment, etc.),
  • Next generation distributed large-scale computing and simulation environments,
  • Scalable self-tuning optimization, machine learning, and data mining systems,
  • Algorithms for analysis of large, dynamic networks,
  • Causality,
  • Data visualization.

John Duchi

John Duchi Assistant Professor

Sequoia 126 (4065)
Hector Garcia-Molina

Hector Garcia-Molina Professor

Gates 434 (9040)
Website

Siroker, Marianne Administrator

Gates 435 (9040)
723-0872
siroker@cs.stanford.edu

Kunle A. Olukotun

Kunle A. Olukotun Professor

Gates 302 (9040)
Website

Hadding, Darlene Administrator

Gates 408 (9040)
723-1430
darleneh@stanford.edu

Balaji Prabhakar

Balaji Prabhakar Professor

Pkd 269 (9510)
Website

Kuduk, Andrea Administrator

Pkd 267 (9510)
723-4731
kuduk@ee.stanford.edu

Ram Rajagopal

Ram Rajagopal Assistant Professor Civil and Environmental Engineering
EE by Courtesy

Jennifer Widom

Jennifer Widom Professor

Gates 435 (9040)
Website

Siroker, Marianne Administrator

Gates 435 (9040)
723-0872
siroker@cs.stanford.edu