Alex Fogelson

Alex Fogelson

I'm a recovering software engineer turned researcher, working on how we measure progress in AI, algorithmic efficiency, and their downstream implications on math and science. I'm currently at MIT CSAIL (FutureTech group).

I've pledged to give away 10% of my income through the Giving What We Can Pledge.

  1. 2021 – 2022 M.S. in Data Science Carnegie Mellon University MS-DAS Program
  2. 2018 – 2021 B.S. in Mathematics
    Double Major in Computer Science
    Minor in Chinese Studies
    Carnegie Mellon University Completed in 3.5 years, concurrent with Master's program.
  3. Jun – Jul 2019 Study Abroad – Chinese Language and Culture Shanghai International Studies University

Career

  1. Apr 2024 – Present MIT FutureTech Research Assistant
  2. Jun – Jul 2025 Wolfram Institute Summer Research Program – Student
  3. Feb 2023 – Mar 2024 MFS Investment Management Software Engineer
  4. Aug – Dec 2022 Meta Software Engineer
  5. Jan – May 2022 PNC Financial Services Group Master's Capstone Project
  6. Jun – Aug 2021 Qualcomm Software Engineer, Intern
  7. 2020 – 2021 Carnegie Mellon University Teaching Assistant
    21-128 / 15-151, 21-241
  8. Jun – Aug 2020 Audition Technology Software Engineer, Intern
  9. Nov 2019 – Apr 2020 Expii, Inc. Content Developer, Intern

Publications

  1. Paper2026 Science Is Falling Behind the Frontier: Foundation Model Adoption Across Half a Million Papers NeurIPS 2026 — Workshop: AI4MetaScience Ana Trišović*, Alex Fogelson*, Janakan Sivaloganathan, Neil Thompson
  2. Paper2026 On the Origins of Algorithmic Progress in AI NeurIPS 2026 — Main Track ICML TAIGR 2026 — Best Paper Award Hans Gundlach*, Alex Fogelson, Jayson Lynch, Ana Trisovic, Jonathan Rosenfeld, Anmol Sandhu, Neil Thompson
  3. PaperApr 2026 Two AI Metrics Diverged: Will it make the difference? ICML TAIGR 2026 Alex Fogelson*, Zachary A. Brown*, Hans Gundlach, Jayson Lynch, Neil Thompson
  4. PaperDec 2025 Four Facts About U.S. China Competition in China SSRN — Pre-print Alex Fogelson

Writing

  1. Oct 2026 How Much of AI’s Mathematics Ability Is Selection Bias Under: Mixture of Experts
  2. May 2026 How Much Would It Have Cost Claude Shannon to Train Claude Opus Under: Mixture of Experts
  3. Mar 2026 Can Academic Science Keep Up with the AI Frontier Under: Mixture of Experts
  4. Jan 2026 On the Origins of Algorithmic Progress Under: Mixture of Experts
  5. Jul 2025 The Computational Limits of Language Models Under: Wolfram Summer Research Program

Talks

  1. 2026 The Rapid Growth of Foundation Model Usage in Science Wharton School of Business — AI and the Future of Work Conference
  2. 2025 The Computational Limits of Language Models Wolfram Research — Wolfram Technology Conference
  3. 2024 The AI Risk Repository Elisava School of Design and Engineering IAM — Guest Lecture