PeopleofSuperintelligence

Researcher

Kunal Talwar

Kunal Talwar is a theoretical computer scientist whose research connects differential privacy with machine learning and algorithms. He earned a B.Tech. from IIT Delhi in 2000 and a PhD from UC Berkeley in 2004, then worked at Microsoft Research in Silicon Valley and Google Brain before Apple. With Frank McSherry, he coauthored “Mechanism Design via Differential Privacy” for FOCS in 2007; his work received a Privacy Enhancing Technologies Award in 2009, and his PATE paper won an ICLR Best Paper Award in 2017. In 2019, he served as a visiting scientist in two Simons Institute programs, and his 2025 Apple paper studied privacy and computation trade-offs in private repetition and metaselection. As of October 2026, he is a Research Scientist at Apple; in Spring 2026, he served as a visiting scientist and program organizer for a Simons program and presented at Apple’s privacy-preserving machine-learning and AI workshop.

Now at
Apple
Works
236
Citations
29,237
h-index
51
Badges

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 2000–2004

    IIT Delhi and Berkeley degrees

    He earns a B.Tech. from IIT Delhi in 2000 and a PhD from UC Berkeley in 2004.

  2. 2007–2009

    A FOCS paper and PET award

    In 2007, he coauthors “Mechanism Design via Differential Privacy” with Frank McSherry for FOCS. In 2009, his work receives a Privacy Enhancing Technologies Award.

  3. 2017

    PATE and an ICLR award

    He coauthors “Semi-Supervised Knowledge Transfer for Deep Learning from Private Training Data,” which wins an ICLR Best Paper Award in 2017. The paper introduces PATE, which combines multiple models trained on disjoint datasets.

  4. 2019

    Visiting scientist at Simons

    In Spring and Summer 2019, he serves as a visiting scientist in the Simons Institute’s Data Privacy: Foundations and Applications and Foundations of Deep Learning programs.

  5. By 2021

    Research roles before Apple

    Before joining Apple, he works at Microsoft Research in Silicon Valley and Google Brain.

  6. January 2025

    Privacy and computation trade-offs

    In January 2025, he publishes “Privacy-Computation Trade-offs in Private Repetition and Metaselection.” The paper shows that an algorithm preserving privacy cost up to a constant factor can lower failure probability only polynomially as computational overhead grows.

  7. 2026

    Federated learning program organizer

    In Spring 2026, he serves as a visiting scientist and program organizer for the Simons Institute’s Federated and Collaborative Learning program.

  8. As of October 2026

    Research scientist at Apple

    By 2021 and as of October 2026, he is a Research Scientist at Apple. He is a theoretical computer scientist whose work spans differential privacy, machine learning, algorithms and data structures. In 2026, he presents “Crypto for DP and DP for Crypto” at Apple’s privacy-preserving machine learning and AI workshop.

Career

  1. Since 2020AppleResearch Scientist
  2. 2026University of California, BerkeleyVisiting Scientist and Program Organizer
  3. 2020–2023Tel Aviv University
  4. 2015–2020Google
  5. 2010Microsoft Research
  6. 2005–2016Microsoft

Education

  1. 2004University of California, BerkeleyPhD
  2. 2000IIT DelhiB.Tech.

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Most-cited works

  1. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems2016 · 9,405 citationsWith Andy Davis, Chris Olah, Derek G. Murray, Fernanda Viégas, Geoffrey Irving and Greg Corrado
  2. Deep Learning with Differential Privacy2016 · ACM SIGSAC Conference on Computer and Communications Security (CCS) · 6,448 citationsWith Ian Goodfellow and Martin Abadi
  3. Mechanism Design via Differential Privacy2007 · Annual Symposium on Foundations of Computer Science · 1,518 citationsWith Frank McSherry
  4. Quincy2009 · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · 857 citationsWith Andrew V. Goldberg and Michael Isard
  5. Learning Differentially Private Recurrent Language Models2017 · 848 citationsWith Daniel Ramage

From OpenAlex, as of 21 October 2026.

  1. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes Kunal Talwar. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.
  2. Google alumni have founded or led 133 other organizations, more than any other AI labIncludes Kunal Talwar. We estimate that alumni of 17 major AI labs went on to found or lead 350 other organizations, including 226 they founded, and we trace 472 career moves directly between 23 labs since 2010.
  3. 23% of people shaping AI who left a university for a company later returned to a universityIncludes Kunal Talwar. We estimate that 468 of the 2,011 people shaping AI who left a university for a company (23%) later returned to a university after a median of 5 years, and 163 of 829 (20%) made the reverse round trip.
  4. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Kunal Talwar. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Kunal Talwar yet. Send us a free-licensed photo.

  1. Kunal Talwar | ISL Colloquium isl.stanford.edu. Checked 21 October 2026.
  2. PET Award petsymposium.org. Checked 21 October 2026.
  3. Database Privacy - Microsoft Research: Publications microsoft.com. Checked 21 October 2026.
  4. Census Bureau Appoints New Leader and Members to Census Scientific Advisory Committee census.gov. Checked 21 October 2026.
  5. Research at Google and ICLR 2017 research.google. Checked 21 October 2026.
  6. Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data research.google. Checked 21 October 2026.
  7. Kunal Talwar simons.berkeley.edu. Checked 21 October 2026.
  8. Privacy-Computation Trade-offs in Private Repetition and Metaselection machinelearning.apple.com. Checked 21 October 2026.
  9. Privacy-Computation trade-offs in Private Repetition and Metaselection arxiv.org. Checked 21 October 2026.
  10. Apple Workshop on Privacy-Preserving Machine Learning & AI 2026 machinelearning.apple.com. Checked 21 October 2026.