PeopleofSuperintelligence

Lab leader

Edward Kim

Edward Kim is a machine-learning researcher and AI leader whose work links materials discovery with enterprise language models. He studied materials science and engineering at MIT, where he coauthored AI methods for extracting recipes from scientific papers and proposing inorganic synthesis parameters. His later work includes a 2020 study on machine-learned metrics for materials discovery, a 2023 guide to fine-tuning Cohere's Command models, and a 2025 essay on AI reasoning for business decisions. As of October 2026, he is Cohere's Director of Sovereign AI and an adjunct professor in the University of Toronto's Department of Materials Science and Engineering.

Links
Google Scholar, LinkedIn and GitHub

Timeline

  1. 2017

    Extracting synthesis recipes

    In 2017, he is an MIT graduate student in materials science and engineering and coauthors a machine-learning system that extracts synthesis recipes from research papers. His PhD research uses natural-language processing and generative methods. A related 2017 study applies a variational autoencoder to suggest inorganic synthesis parameters.

  2. 2020

    Metrics for materials discovery

    In 2020, he coauthors a paper that introduces two machine-learned metrics to identify discovery-rich and discovery-poor materials design spaces.

  3. June 2023

    Fine-tuning Command models

    In June 2023, he is Cohere's Manager of Technical Staff and coauthors a guide to fine-tuning custom Command models.

  4. May 2025

    AI reasoning for business

    In May 2025, he coauthors a Cohere essay on the role of AI reasoning models in enterprise decision-making.

  5. As of October 2026

    Sovereign AI at Cohere

    As of October 2026, he is Cohere's Director of Sovereign AI and an adjunct professor in the University of Toronto's Department of Materials Science and Engineering. His previous work includes ML systems for accounting software at Xero, AI for chemicals and materials at Citrine Informatics, and natural-language processing for pharmaceuticals at Pfizer.

  1. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI from 1961 to 2025, with a year-by-year chart.
  2. The United States employs an estimated 53% of people in our data with a known employment countryOur estimates of where AI careers are concentrated now and how countries’ shares of dated positions changed over time.
  3. 46% of living people in our data with known birth and employment countries work outside their birth countryOur estimate of cross-border careers in AI, from country of birth and the country of each current organization.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionOur estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.

Sources and credits

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  1. Artificial intelligence aids materials fabrication news.mit.edu. Checked 19 October 2026.
  2. Virtual screening of inorganic materials synthesis parameters with deep learning nature.com. Checked 19 October 2026.
  3. Edward Kim mrs.digitellinc.com. Checked 19 October 2026.
  4. Machine-Learned Metrics for Predicting the Likelihood of Success in Materials Discovery citrine.io. Checked 19 October 2026.
  5. How to Create Custom Command Models cohere.com. Checked 19 October 2026.
  6. AI reasoning and the future of decision-making cohere.com. Checked 19 October 2026.
  7. Edward Kim eddotman.github.io. Checked 19 October 2026.