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Pramod Viswanath

Pramod Viswanath

· Associated Faculty

Princeton University · Computer Science

Active 1997–2026

h-index54
Citations26.4k
Papers35468 last 5y
Funding$4.2M1 active

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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About

Pramod Viswanath is the Forrest G. Hamrick Professor in Engineering at Princeton University's Department of Electrical and Computer Engineering. His research interests include the principle understanding and design of blockchains, as well as inventing communication algorithms via deep learning and full stack design of blockchain technologies. He has contributed to the development of various blockchain protocols and algorithms, including consensus algorithms, cryptographic hash aggregators, off-chain networking stacks, and incentive structures for proof of stake blockchains. Additionally, he has extensive experience in wireless communications, having worked actively in the field for over a decade, focusing on modeling, algorithm design, and system solutions, including early work on the first OFDM-based cellular system and coauthoring a popular book on wireless communication. His work emphasizes theoretical and conceptual aspects of communication and network systems.

Research topics

  • Computer science
  • Computer network
  • Mathematics
  • Algorithm
  • Theoretical computer science

Selected publications

  • Coded Merkle Tree: Solving Data Availability Attacks in Blockchains

    Lecture notes in computer science · 2020-01-01 · 69 citations

    book-chapterSenior author
    PublisherDOI

  • Correct Answers from Sound Reasoning: Verifiable Process Supervision for Language Models

    ArXiv.org · 2026-04-03

    articleOpen access

    Training language models to produce both correct answers and sound reasoning remains an open challenge. Reinforcement learning with verifiable rewards typically optimizes only final outcomes, which can lead to a failure mode where task accuracy improves while reasoning becomes less accurate, less complete, or even internally inconsistent. We propose verifiable process supervision (VPS), a post-training framework for verifiable domains that jointly optimizes prediction accuracy and reasoning qual…

    PublisherOA PDF

  • TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks

    2026-04-24

    preprintOpen accessSenior author

    Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces, edge specialized accelerators) for both training and inference. Yet ML-as-a-Service reveals little about what actually ran or whether returned outputs faithfully reflect the intended inputs and models. Users lack recourse against service downgrades such as model swaps, quantization, graph rewrites, or discrepancies like altered advertisement embeddings. Verifying outputs is especially dif…

    PublisherDOI

  • TabularMath: Evaluating Computational Extrapolation in Tabular Learning via Program-Verified Synthesis

    Open MIND · 2026-01-25

    preprint

    Standard tabular benchmarks mainly focus on the evaluation of a model's capability to interpolate values inside a data manifold, where models good at performing local statistical smoothing are rewarded. However, there exists a very large category of high-value tabular data, including financial modeling and physical simulations, which are generated based upon deterministic computational processes, as opposed to stochastic and noisy relationships. Therefore, we investigate if tabular models can pr…

    DOI

  • TabularMath: Evaluating Computational Extrapolation in Tabular Learning via Program-Verified Synthesis

    arXiv (Cornell University) · 2026-01-25

    articleOpen access

    Standard tabular benchmarks mainly focus on the evaluation of a model's capability to interpolate values inside a data manifold, where models good at performing local statistical smoothing are rewarded. However, there exists a very large category of high-value tabular data, including financial modeling and physical simulations, which are generated based upon deterministic computational processes, as opposed to stochastic and noisy relationships. Therefore, we investigate if tabular models can pr…

    PublisherOA PDF

Recent grants

Frequent coauthors

  • Sreeram Kannan

    101 shared

  • Sewoong Oh

    Google (United States)

    88 shared

  • David Tse

    45 shared

  • Giulia Fanti

    29 shared

  • Shaileshh Bojja Venkatakrishnan

    29 shared

  • Yihan Jiang

    University of Florida

    24 shared

  • Peter Kairouz

    24 shared

  • Adnan Raja

    24 shared

Labs

  • Pramod Viswanath LabPI

Education

  • Doctor of Philosophy, Electrical Engineering and Computer Science

    University of California, Berkeley

    2000

  • Master of Engineering

    Indian Institute of Science Bangalore

    1995

  • Bachelor of Engineering

    National Institute of Technology Karnataka

    1993

Awards & honors

  • Best Paper Award, Sigmetrics conference, 2015
  • Xerox Faculty Research Award, College of Engineering, UIUC,…
  • NSF CAREER Award, 2002
  • Eliahu Jury Award, UC Berkeley, EECS, 2000
  • Bernard Friedman Prize, UC Berkeley, Mathematics, 2000

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