PeopleofSuperintelligence

Researcher

Ashish Khisti

At the University of Toronto, Ashish Khisti applies information theory to deep learning, distributed learning and foundation models. He earned a BASc in Engineering Science at Toronto and MASc and PhD degrees in electrical engineering and computer science at MIT, completed a postdoctoral year at Deutsche Telekom Laboratories, and joined Toronto's ECE department in 2009. From 2012 to 2022, he held a Tier II Canada Research Chair, and he served as associate editor of two IEEE journals from 2012 to 2018. In 2025, he coauthored an ICLR Spotlight paper on multi-draft speculative sampling and a NeurIPS paper on distribution coupling for speculative decoding and lossy compression. As of October 2026, he is a professor at Toronto and a visiting researcher at Qualcomm; he is scheduled to teach One-Shot Information Theory in Winter 2026.

Timeline

  1. By 2009

    Assistant professor at Toronto

    Khisti earns a BASc in Engineering Science at the University of Toronto and MASc and PhD degrees in electrical engineering and computer science at MIT. After a postdoctoral year at Deutsche Telekom Laboratories in Palo Alto, he joins the University of Toronto's Department of Electrical and Computer Engineering as an assistant professor in 2009.

  2. 2012–2022

    A Canada Research Chair

    He holds a Tier II Canada Research Chair at the University of Toronto from 2012 to 2022, and renews the appointment in 2017.

  3. 2012–2018

    IEEE editor and Cisco consultant

    He serves as associate editor of IEEE Transactions on Communications from 2012 to 2015 and IEEE Transactions on Information Theory from 2015 to 2018. He consults for Cisco Systems from 2015 to 2017.

  4. 2023–2025

    IEEE distinguished lecturer

    From 2023 to 2025, he serves as a Distinguished Lecturer for the IEEE Information Theory Society.

  5. 2025

    Multi-draft speculative sampling

    At ICLR 2025, Khisti coauthors a Spotlight paper on multi-draft speculative sampling. It decomposes the optimal scheme into two steps, beginning with importance sampling to select an intermediate token.

  6. 2025

    Distribution coupling at NeurIPS

    With Joseph Rowan and Truong Buu Phan, he coauthors the 2025 NeurIPS paper “List-Level Distribution Coupling with Applications to Speculative Decoding and Lossy Compression.” The paper develops a multi-draft speculative-sampling method and applies it to distributed lossy compression.

  7. 2026

    Information theory for AI

    His research applies information theory to deep learning, distributed learning and foundation models. In 2026, his lab's ICML paper covers detection of LLM-generated and human-written text.

  8. As of October 2026

    Professor at Toronto

    As of October 2026, he is a professor in the University of Toronto's Department of Electrical and Computer Engineering and a visiting researcher at Qualcomm. In Winter 2026, he is scheduled to teach a graduate course in One-Shot Information Theory.

Career

  1. NowUniversity of TorontoTier II Canada Research Chair
  2. NowQualcommVisiting researcher
  3. Deutsche TelekomPostdoctoral researcher

Education

  1. University of TorontoBASc
  2. Massachusetts Institute of TechnologyMASc and PhD

From the biography.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of Toronto. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  2. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes University of Toronto. 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.
  3. Among employers with 30+ full-time cases, OpenAI leads primary technical role H-1B pay at $310,000, and Amazon, Google and Microsoft file 57% of all casesIncludes Qualcomm. Across 440,669 certified US H-1B filings from 44 employers (FY2020–FY2026), OpenAI leads primary technical role base pay at $310,000 and xAI leads overall base pay at $290,000.
  4. 84% of people shaping AI with recorded undergraduate and doctoral schools changed institutions for their doctorateIncludes University of Toronto. 84% of people shaping AI with recorded undergraduate and doctoral schools changed institutions for their doctorate. Across 2,116 people with both an undergraduate degree and a doctorate recorded, we trace which undergraduate schools feed which doctoral programs, how often students stay at the same school, and how paths differ by birth-country region.

Sources and credits

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  1. Ashish Khisti - Vector Institute for Artificial Intelligence vectorinstitute.ai. Checked 21 October 2026.
  2. Signals, Information, and Algorithms Laboratory - News sia.mit.edu. Checked 21 October 2026.
  3. Seminar - Professor Ashish Khisti | Electrical and Computer Engineering | University of Waterloo uwaterloo.ca. Checked 21 October 2026.
  4. Khisti, Ashish Professor - Electrical & Computer Engineering ece.utoronto.ca. Checked 21 October 2026.
  5. Signals Multimedia and Security Laboratory comm.utoronto.ca. Checked 21 October 2026.
  6. Multi-Draft Speculative Sampling: Canonical Architectures and Theoretical Limits arxiv.org. Checked 21 October 2026.
  7. List-Level Distribution Coupling with Applications to Speculative Decoding and Lossy Compression proceedings.neurips.cc. Checked 21 October 2026.
  8. List-Level Distribution Coupling with Applications to Speculative Decoding and Lossy Compression arxiv.org. Checked 21 October 2026.