Data & Information Science

Matrix of varied orange blocksHow does Netflix learn about your taste in movies? How can HD video be compressed and sent over an LTE network to your phone? How are medical images acquired and analyzed to diagnose diseases? How does the computer program like AlphaGo beat a human professional Go player?  These are some of the questions that drive the exciting research on data, information, networks, communication.

At Princeton, we derive answers to these questions using the intriguing language of mathematics and engineer our solutions into products we use every day. This work benefits from many decades of intellectual heritage in information and data science, and in turn guides the future evolution of information technology and data science. Graduate education in information sciences and systems emphasizes breadth and fundamentals in probability, systems, statistics, optimization, and machine learning.  Our research focuses on principles and underlying theories exemplified by theoretical machine learning, optimization, information theory, statistics, signal, image and video processing, control and reinforcement learning, coding theory, data transmission and compression, and network science.

Faculty

  • Emmanuel Abbe

  • Yuxin Chen

    • Yuxin Chen
      • Assistant Professor of Electrical Engineering
  • Niraj Jha

    • Niraj Jha
      • Professor of Electrical Engineering
  • Chi Jin

    • Chi Jin
      • Assistant Professor of Electrical Engineering
  • Sanjeev R. Kulkarni

    • Sanjeev R. Kulkarni
      • William R. Kenan Jr. Professor of Electrical Engineering
      • Dean of the Faculty
  • Sun-Yuan Kung

  • Jason D. Lee

    • Jason D. Lee
      • Assistant Professor of Electrical Engineering
  • Ruby Lee

    • Ruby Lee
      • Forrest G. Hamrick Professor in Engineering
  • Prateek Mittal

  • H. Vincent Poor

    • H. Vincent Poor
      • Michael Henry Strater University Professor of Electrical Engineering
      • Interim Dean, School of Engineering and Applied Science
  • Peter J. Ramadge

    • Peter J. Ramadge
      • Gordon Y.S. Wu Professor of Engineering
      • Director of the Center for Statistics and Machine Learning