Information, Data, Network, and Communication Sciences (IDNCS)


Research covers theory, simulation, and implementation. We study the fundamental problems in information and coding theory, communication, data science, network science, optimization, statistics, machine learning, distributed systems, economics, statistical signal processing, and stochastic control. The motivations are guided by societally important applications such as data centers, distributed storage and content delivery, peer-to-peer computing, social networks, control over wireless networks, cognitive radio, spectrum sharing, scheduling, privacy and security, incentive and mechanism design with resource constraints, sensor networks, transportation systems, hybrid systems, systems biology, DNA and RNA sequencing and storage, and MRI. We develop and apply tools in probability theory, information and coding theory, functional analysis, convex geometry, queuing theory, stochastic processes, optimization theory, statistical physics, statistics, and game theory. We also research fundamental building blocks of large-scale communication and computing infrastructures.


  • Information and coding theory

    Shannon theory, lossless and lossy data compression, algebraic codes, polar codes, LDPC codes, Turbo codes, distributed compression, network coding.

  • Machine learning and data science

    Graphical models, high-dimensional statistics, approximation theory, adaptive inference, nonparametric inference, statistical learning theory, individual sequence learning theory, reinforcement learning, distributed estimation, robust statistics.

  • Optimization

    Convex and non-convex optimization, stochastic optimization, stochastic approximation, computationally efficient statistical algorithms, statistical and computational tradeoff.

  • Probability theory and functional analysis

    High-dimensional probability, measure concentration, matrix concentration, limit theorems, entropy power inequality, interplay between information theory and probability theory, convex geometry, stochastic processes, queuing theory.

  • Network design and analysis

    Peer-to-peer networks, quality of service, communication for control, cross-layer optimization, simulation tools, privacy and security, social networks, information networks.

  • Wireless and sensor networks

    Architectures and protocols for ad-hoc, mobile and vehicular networks, multiple antennas, opportunistic communication, cognitive radio, and spectrum sharing, control over wireless networks.

  • Systems biology

    DNA and RNA sequencing and compression.

  • Economics and game theory

    Mechanism and market design, urban economics, market-based architectures, incentive compatibility, auction design.

  • Distributed systems

    Byzantine fault tolerance, permissioned and permissionless distributed consensus, distributed content delivery.

  • Implementations

    Energy-efficient transceivers, ultra low-energy wireless sensors, communication system/circuit co-design, magnetic resonance imaging.

Research Centers




Faculty Awards

  • SIAM John von Neumann Lecture Prize: Jennifer Chayes, 2015.
  • National Academy of Sciences (NAS) Member: Jennifer Chayes, 2019.
  • National Academy of Engineering (NAE) Member: Pravin Varaiya, 1999. David G. Messerschmitt, 1990. Eugene Wong, 1987.
  • American Academy of Arts and Sciences Member: Jennifer Chayes, 2014. Pravin Varaiya, 1985. Eugene Wong, 1980.
  • Berkeley Citation: Edward A. Lee, 2018. Pravin Varaiya, 2006. David G. Messerschmitt, 2005. Eugene Wong, 1994.
  • Sloan Research Fellow: Michael Lustig, 2013. Martin Wainwright, 2005. Jennifer Chayes, 1989.

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