Ramin Hasani

Machine Learning Scientist

Computer Science and Artificial Intelligence Lab (CSAIL)

Massachusetts Institute of Technology (MIT)

Contact me at:


Quick Picks

[2 Accepted Papers] [NeurIPS 2021] 2 papers accepted to NeurIPS 2021! Causal Navigatin and Sparse Flows.

[Seminar talk] at MIT Center for Brain, Minds and Machines (CBMM), on Liquid neural networks, oct 5, 2021 [link]

[Keynote talk] on the 20th of August, 2021, I will give a keynote talk on “Liquid Neural Networks for Autonomous Driving” at IJCAI 2021 Artificial Intelligence for Autonomous Driving Workshop! [link]

[Accepted Paper] [ICML 2021] Our paper On-Off Center-Surround Receptive Fields for Robust Image Classification got accepted for publication at the 38th International Conference on Machine Learning (ICML), 2021. [link]

[Recent Invited Talks]

“Liquid Time-Constant Networks”,
Synthesis of Models and Systems Seminar at Simons Institute, UC Berkeley, CA, 3.22.21 [link]

“Understanding Liquid Time-Constant Networks”,
MIT Lincoln Laboratory Machine Learning Special Interest Group (LL-MLSIG) Seminar Series, 3.25.21

“Liquid Neural Networks”
MIT Open Learning, MIT Horizon, Cambridge, MA, 4.8.21

“What Is a Liquid Time-Constant Network?”,
Northeastern University, Boston, MA, 3.14.21 [link]

[New Preprint] A new preprint of our work on comparing model-based to model-free agents in autonomous racing environments is out! [link]

[Accepted Papers] [ICRA 2021] our works “Adversarial training is not ready for robot learning” have been accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA) 2021. [link]

[Press MIT News] article about our research: “Liquid” machine-learning system adapts to changing conditions.
The new type of neural network could aid decision-making in autonomous driving and medical diagnosis. (Jan 28th, 2021) [link]

[2 Accepted Papers] [AAAI 2021] our works “Liquid time-constant networks” and “On the verification of Neural ODEs” have been accepted for publication at the 35th AAAI Conference on Artificial Intelligence. [link]

[Cover of Nature MI] Our paper got featured on the cover of the October 2020 Issue of Nature Machine Intelligence Journal [link] [pdf]

[Position Update] I joined the Distributed Robotics Lab (DRL) of CSAIL MIT, as a postdoctoral associate.[link]

[New Paper Out] “Learning Long-term Dependencies in Irregularly-samples Time Series” [Paper][code]

[Accepted Paper] [Nature Machine Intelligence] “Neural Circuit Policies Enabling Auditable Autonomy” got accepted for publication in Nature Machine Intelligence. [link]

[PhD Thesis Award Nomination] My PhD Dissertation has been nominated for the TÜV Austria 2020 Science Award. [About the Award] [video]

[Accepted Paper] [ICML 2020] “A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits” got accepted to the 2020 International Conference on Machine Learning (ICML) [link]

[Accepted Paper] [Journal of Autonomous Robots 2020] “Plug-and-play supervisory control using muscle and brain signals for real-time gesture and error detection” got accepted to the journal of Autonomous Robots, August 2020. [link]

[PhD dissertation] Check out my PhD dissertation here: [link]

[PhD studies – Done!] Completed my PhD degree with honors on May 5th, 2020

[Medium Article] Curious about some mysterious facts on neural ODEs’? Read my Medium article: The Overlooked Side of Neural ODEs. [link]

[Accepted Paper] ICRA 2020 – We introduced a new regularization scheme to obtain state-stable recurrent neural networks, in control environments. The paper is gonna be presented during ICRA 2020 (May 29th -June 4th) in Paris, France.

[TED Talk] watch my latest TEDxCluj talk entitled “A journey inside a neural network” [link]

[MIT] I am currently a research scholar at MIT CSAIL Daniela Rus’s Robotics Lab. [My MIT CSAIL webpage]

[Accepted Paper] ICRA 2019 – We proposed a new brain-inspired neural network design methodology for interpretable and noise-robust robotic control [link]

[Accepted Paper] IJCNN 2019 – We proposed a new method to interpret LSTM networks [link]

[TED Talk] My TEDxVienna talk entitled “Simple Artificial Brains to Govern Complex Tasks” is officially released by TEDx. watch it [here]

[Accepted Paper] We proposed a new method to interpret LSTM networks. The paper will be presented at the NeurIPS (NIPS) 2018 Workshop on Interpretability and Robustness (IRASL). [Paper]

[Interview] Read my interview with Vera Steiner at TEDxVienna here

[TED Talk 2018] I gave my first TEDx talk at TEDxVienna on October 20th 2018. [link]

[Interview] Read my interview with TrendingTopics about my research and perspectives on AI [link]

[Press] reflections of our research work on “Neuronal Circuit Policies“: [TU Wien] [EurekAlert] [i-programmer] [techxplore] [NewsGuard] [Motherboard vice]

[AAAI-IAAI 2019] one accepted paper entitled “a machine learning suite for machine’s health monitoring”, for oral presentation. [link]

[Interview] Read my interview with Futurezone in German [link]

[AI Talk Sep2018] I gave a talk on “AI and Neuroscience” at the “BrainStorms” event. [link]

[Accepted papers] 2 journal papers got published at the Royal Society Philosophical Transactions: Biological Sciences. [Publications]

[RSS 2018] In a recently published paper at the Robotics Sicence and Systems (RSS) 2018 Conference, we showed “How to control robots with brainwaves and hand gestures” [MIT Press release][Paper]

At ICML & IJCAI 2018 in Stockholm, I presented two papers, one at the Explainable AI (XAI-18) workshop and one at DISE1 workshop.

[Press] Press releases on our recent NIPS Deep RL Symposium 2017 paper: [Motherboard Vice] [Phys.org] [TU Wien]

My student, Mathias Lechner, won the Best Master Thesis Award 2017 as the “Distinguished Young-Alumnus Award”, at the Faculty of Informatics at TU Wien. [link]

I presented a paper at the Deep Reinforcement Learning Symposium at NIPS 2017, and two papers at the workshop on Worm’s Neural Information Processing, at NIPS 2017.

I was a visiting research scholar at MIT CSAIL, working on developing interpretable machine learning algorithms for autonomous systems, with Daniela Rus.

I co-chaired a NIPS 2017 workshop on Worm’s Neural Information Processing (WNIP).

I attended ICML 2017 in Sydney and presented at WCB 2017. [slides]

I also attended IJCAI 2017 in Melbourne, and presented at BOOM 2017, [slides] [poster] Won the Best Poster Award! [link]

I took part in the Deep learning Indaba 2017 in Johannesburg, South Africa.

About me

I am a machine learning scientist at the Computer Science and Artificial Intelligence Lab (CSAIL), Massachusetts Institute of Technology (MIT). I have completed my PhD studies (with distinction) in Computer Science, at TU Wien, Austria (May 2020). My PhD dissertation was co-advised by Prof. Radu Grosu (TU Wien) and Prof. Daniela Rus (MIT).

My research focuses on developing interpretable deep learning and decision-making algorithms.