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Alexander Amini

Co-founder and Chief Scientific Officer, Liquid AI

Alexander Amini is a co-founder and the Chief Scientific Officer of Liquid AI and an AI scientist at MIT. Amini is the creator, lead organizer and lecturer of MIT 6.S191, Introduction to Deep Learning, and Amini's research covers autonomy, end-to-end control of autonomous systems and uncertainty estimation for neural networks. Amini is the first-listed author of the LFM2 technical report.12345

At a glance

Current roles
Co-founder and Chief Scientific Officer +1 more
Companies
Liquid AI, MIT
Sector
Company
Education
MIT
Work listed
3
Honors
2
Sources cited
9
Profile reviewed

Career

Amini studied computer science at MIT, completing a bachelor's degree in 2017, a master's degree in 2018 and a PhD in 2022 as a researcher at CSAIL advised by Daniela Rus. During that period Amini worked with NVIDIA's end-to-end driving team, working on confidence measures for deep networks.42

Amini became a research scientist at MIT CSAIL in June 2022 and co-founded Liquid AI in March 2023, serving as its Chief Scientific Officer. Amini's CV also lists a co-founder and advisor role at Themis AI from 2021.46

Research and teaching

Amini's research addresses learning end-to-end control for autonomous systems and estimating the confidence of neural networks, including the 2019 paper "Deep Evidential Regression". Amini co-authored the liquid time-constant network and closed-form continuous-time model papers with Ramin Hasani and Mathias Lechner.2784

Since January 2018, Amini has led MIT 6.S191, Introduction to Deep Learning, developing the curriculum, teaching lectures and publishing the course material online.49

Work and contributions

  • LFM (Liquid Foundation Models) Open weights

    First-listed author of the LFM2 technical report, which documents Liquid AI's LFM2 family of foundation models.54

  • MIT 6.S191 Introduction to Deep Learning Show, channel or publication

    Created the course and has served as its lead organizer and lecturer each January since 2018, including developing the curriculum and publishing the material online.439

  • Deep Evidential Regression Published research

    First author of a paper on learning uncertainty estimates for regression networks through evidential distributions.7

Timeline

  1. Began graduate research at MIT CSAIL advised by Daniela Rus.4
  2. First led MIT 6.S191 Introduction to Deep Learning as organizer and lecturer.4
  3. Posted "Deep Evidential Regression" as first author.7
  4. Co-authored "Liquid Time-constant Networks" with Ramin Hasani and Mathias Lechner.8
  5. Completed a PhD in computer science at MIT.4
  6. Became a research scientist at MIT CSAIL.4
  7. Co-founded Liquid AI as Chief Scientific Officer.4
  8. Appeared as first-listed author of the LFM2 technical report.5

Other documented roles

Roles the sources document that are not confirmed as current.

  • Deep learning researcher on the end-to-end driving team (June–August 2017), NVIDIA4

Honors and recognition

  • NSF Graduate Research Fellowship · National Science Foundation, 20174
  • Innovation Excellence Award · Hyperion Research, 20224

Where they work

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References

9 sources · profile reviewed Oct 22, 2026 · data updated Oct 22, 2026

  1. Liquid AI — TeamLiquid AI · Official page · accessed Oct 22, 2026
  2. Alexander Amini — homepageAlexander Amini · Official page · accessed Oct 22, 2026
  3. Alexander Amini — third-person bioAlexander Amini · Official page · accessed Oct 22, 2026
  4. Alexander Amini — Curriculum VitaeAlexander Amini · Official page · accessed Oct 22, 2026
  5. LFM2 Technical Report (arXiv 2511.23404)arXiv · Paper · published Nov 28, 2025 · accessed Oct 22, 2026
  6. Liquid AI — CompanyLiquid AI · Official page · accessed Oct 22, 2026
  7. Deep Evidential Regression (arXiv 1910.02600)arXiv · Paper · published Oct 7, 2019 · accessed Oct 22, 2026
  8. Liquid Time-constant Networks (arXiv 2006.04439)arXiv · Paper · published Jun 8, 2020 · accessed Oct 22, 2026
  9. MIT 6.S191 Introduction to Deep LearningMIT 6.S191 · Official page · accessed Oct 22, 2026