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Ruchir Puri

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Ruchir Puri is chief scientist of IBM Research, an IBM Fellow and a vice president for IBM’s technology and technical community. His work spans automated chip design and AI systems for understanding and generating software.[1]

Start with Project CodeNet →
Ruchir Puri

Education

National Institute of Technology Kurukshetra

Bachelor’s degree, Electronics and Communication Engineering

Completed 1988

[3]
Indian Institute of Technology Kanpur

Master’s degree, Electrical Engineering

Completed 1990

[2]
University of Calgary

PhD, Computer Engineering

Completed 1994

[2]

Links

IBM Research LinkedIn

Chief scientist, IBM Research; IBM Fellow; vice president, technology and technical community · IBM

Career

Puri’s early work concerned methods for designing high-performance electronic circuits and microprocessors. His institutional biographies describe later leadership across IBM’s research and AI businesses, including serving as Watson’s chief technology officer and chief architect from 2016 to 2019. He earned his master’s degree at IIT Kanpur and doctorate at the University of Calgary.[1][2][3]

Contributions

Puri is the first listed author of CodeNet, a dataset of programming-problem submissions in many languages. It pairs code with annotations and, for most samples, input-output tests. Researchers can use this material to compare programs, translate between languages or study execution costs. The test cases offer a way to check behavior on supplied inputs; they do not prove correctness for every possible input.[4]

Ideas

Puri’s career connects two forms of automation: tools that help design computer hardware and models that help work with software. CodeNet makes part of that second task concrete by preserving unsuccessful submissions and execution information alongside working programs, supporting research on errors as well as successful examples.[4][1]

Tools, writing & talks

Tools

  • Start here

    Project CodeNetgithub.com

    A multilingual code dataset with problem metadata and test cases for AI-for-code research.

    Begin with the dataset organization and one programming problem’s submissions and tests. Basic programming is enough to inspect examples; model training requires a separate experimental setup.

    First author of the accompanying paper[4][5]
References

Latest source check 2026-11-04

  1. [1] Official IBM Research biography

    research.ibm.com · Checked 2026-11-04

  2. [2] IIT Kanpur alumnus biography

    www.iitk.ac.in · Checked 2026-11-04

  3. [3] Original engineering-award biography

    2014.aaeoy.org · Checked 2026-11-04

  4. [4] Original CodeNet dataset paper

    arxiv.org · Checked 2026-11-04

  5. [5] Official CodeNet repository

    github.com · Checked 2026-11-04

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