Alexander Amini
Co-founder and Chief Scientific Officer of Liquid AI; lead organizer of MIT 6.S191 Intro to Deep Learning
- Co-founder and Chief Scientific Officer, Liquid AI125
- AI scientist; lead organizer and lecturer of MIT 6.S191 Introduction to Deep Learning, Massachusetts Institute of Technology (MIT CSAIL)145
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.13459
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.53
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 2021.52
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.3785
Since January 2018, Amini has led MIT 6.S191, Introduction to Deep Learning, developing the curriculum, teaching lectures and publishing the course material online.56
Work
LFM (Liquid Foundation Models)Open weights
First-listed author of the LFM2 technical report, which documents Liquid AI's LFM2 family of foundation models.95
MIT 6.S191 Introduction to Deep Learning (external site: introtodeeplearning.com)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.546
Deep Evidential Regression (external site: arxiv.org)Published research
First author of a paper on learning uncertainty estimates for regression networks through evidential distributions.7
Career timeline
Aug 2017
Began graduate research at MIT CSAIL advised by Daniela Rus.5
Jan 2018
First led MIT 6.S191 Introduction to Deep Learning as organizer and lecturer.5
Oct 2019
Posted "Deep Evidential Regression" as first author.7
Jun 2020
Co-authored "Liquid Time-constant Networks" with Ramin Hasani and Mathias Lechner.8
May 2022
Completed a PhD in computer science at MIT.5
Jun 2022
Became a research scientist at MIT CSAIL.5
Mar 2023
Co-founded Liquid AI as Chief Scientific Officer.5
Nov 2025
Appeared as first-listed author of the LFM2 technical report.9
Other documented roles
Roles the sources document but that are not confirmed as current.
Honors and recognition
Related people
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- Andrew NgAlso from MIT
- Kaiming HeAlso at Massachusetts Institute of Technology
- Alessio FanelliTeaching and writing
- Bill DallyAlso at NVIDIA
- Mathias LechnerAlso at Liquid AI
- Andrej KarpathyLanguage models
- Brian HieAlso from MIT
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