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Ramin Hasani

Co-founder and CEO, Liquid AI

Ramin Hasani is a co-founder and the CEO of Liquid AI, a company spun out of MIT CSAIL, and a machine learning scientist at MIT CSAIL. Hasani's research centres on robust deep learning and decision-making in complex dynamical systems, and Hasani is first author of the Liquid Time-constant Networks and closed-form continuous-time model papers. Hasani is also a co-author of the LFM2 technical report, which describes Liquid AI's second generation of foundation models.12345

At a glance

Current roles
Co-founder and CEO +1 more
Companies
Liquid AI, MIT
Sector
Company
Education
MIT
Work listed
3
Sources cited
7
Profile reviewed

Career

Hasani earned a PhD in computer science at the Vienna University of Technology (TU Wien) in 2020, with a dissertation on liquid neural networks. Hasani then worked as a postdoctoral associate at MIT CSAIL with Daniela Rus, and later held a joint appointment as a principal AI and machine learning scientist at the Vanguard Group and a research affiliate at CSAIL.1

Hasani co-founded Liquid AI with Mathias Lechner, Alexander Amini and Daniela Rus as a spin-out of MIT CSAIL, and leads the company as CEO. The company's official pages give its founding year as 2023.21

Research

Hasani's 2020 paper on liquid time-constant networks describes recurrent models whose time constants vary with their hidden state, computed with numerical differential equation solvers. The same year, Hasani co-authored "Neural circuit policies enabling auditable autonomy" in Nature Machine Intelligence, with Lechner as first author.36

Follow-up work by Hasani on closed-form continuous-time models (2021) approximated these dynamics in closed form, and "Liquid Structural State-Space Models" (2022) combined liquid networks with structured state-space models. Hasani is among the authors of the LFM2 technical report released by Liquid AI in 2025.475

Work and contributions

  • LFM (Liquid Foundation Models) Open weights

    Listed among the authors of the LFM2 technical report, which documents Liquid AI's LFM2 family of foundation models.5

  • Liquid Time-constant Networks Published research

    First author of the paper introducing liquid time-constant networks, continuous-time recurrent models built from linear first-order dynamical systems modulated by nonlinear gates.3

  • Closed-form Continuous-time Neural Models Published research

    First author of the paper deriving a closed-form approximation of liquid time-constant network dynamics, relaxing the need for complex numerical differential equation solvers.4

Timeline

  1. Completed a PhD in computer science at TU Wien with a dissertation on liquid neural networks.1
  2. Posted "Liquid Time-constant Networks" as first author.3
  3. Co-authored "Neural circuit policies enabling auditable autonomy" in Nature Machine Intelligence.6
  4. Posted "Closed-form Continuous-time Neural Models" as first author.4
  5. Posted "Liquid Structural State-Space Models" as first author.7
  6. Co-founded Liquid AI as a spin-out of MIT CSAIL and became its CEO.21
  7. Co-authored the LFM2 technical report describing Liquid AI's LFM2 models.5

Other documented roles

Roles the sources document that are not confirmed as current.

  • Principal AI and Machine Learning Scientist (past), The Vanguard Group1

Where they work

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References

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

  1. Liquid AI — TeamLiquid AI · Official page · accessed Oct 22, 2026
  2. Liquid AI — CompanyLiquid AI · Official page · accessed Oct 22, 2026
  3. Liquid Time-constant Networks (arXiv 2006.04439)arXiv · Paper · published Jun 8, 2020 · accessed Oct 22, 2026
  4. Closed-form Continuous-time Neural Models (arXiv 2106.13898)arXiv · Paper · published Jun 25, 2021 · accessed Oct 22, 2026
  5. LFM2 Technical Report (arXiv 2511.23404)arXiv · Paper · published Nov 28, 2025 · accessed Oct 22, 2026
  6. Neural circuit policies enabling auditable autonomy (Nature Machine Intelligence)Nature Machine Intelligence · Paper · published Oct 13, 2020 · accessed Oct 22, 2026
  7. Liquid Structural State-Space Models (arXiv 2209.12951)arXiv · Paper · published Sep 26, 2022 · accessed Oct 22, 2026