Alexander Amini

Alexander Amini email: amini@mit.edu
room: 32-376
resume, google scholar

Bio

I am a PhD student at the Massachusetts Institute of Technology (MIT), in the Computer Science and Artificial Intelligence Laboratory (CSAIL), with Prof. Daniela Rus. I am a NSF Fellow and completed my Bachelor of Science and Master of Science in Electrical Engineering and Computer Science at MIT, with a minor in Mathematics.

My research focuses on building machine learning algorithms for end-to-end control (i.e., perception to actuation) of autonomous systems and formulating guarantees for these algorithms. I have worked on control of autonomous vehicles, formulating confidence of deep neural networks, mathematical modeling of human mobility, as well as building complex inertial refinement systems.

In addition to research, I am also a lead organizer and lecturer for MIT 6.S191: Introduction to Deep Learning, MIT's official introductory course on deep learning. In high school, I was awarded the first place Grand Prize at the EU Content for Young Scientists and BTYSTE with my project entitled: Tennis Sensor Data Analysis: An Automated System for Macro Motion Refinement. I grew up in New York, and then moved to Dublin, Ireland, where I attended Castleknock College, and then returned to the US in 2012.

My professional resume can be found here.

Projects

Some of my recent projects

Publications

My latest published research

Teaching

Courses, students, & mentorship

News

Oct 2021   Paper: Two new papers accepted to NeurIPS 2022 on the topics of causual navigation models [paper] and sparse flows [paper]!
Apr 2021   Our paper on co-optimizing sensor placement and policy learning is accepted to RA-L and nominated for the Best Paper Award at RoboSoft 2021! [paper] [news]
Mar 2021   Two new papers using our evidential uncertainty algorithm have been accepted and published for improved robustness in molecular drug discovery [paper] and end-to-end autonomous driving [paper]!
Feb 2021   Awarded two grants with FinTech@CSAIL and MachineLearningApplications @CSAIL to mitigate algorithmic bias and uncertainty in financial time series modeling and clinical trial outcome prediction.
Jan 2021   Lead organizer and lecturer for MIT 6.S191: Introduction to Deep Learning, with over 700 registered MIT students (and over 10,000 registrations globally online). [link] [video]
Dec 2020   Awarded the JP Morgan Fellowship for the 2021-2022 academic year with a focus on robustness and uncertainty of learning-based systems!
Dec 2020   Our paper on liquid time-constant neural networks is accepted to AAAI with an Oral spotlight! [paper] [news]
Aug 2020   Two papers focused on uncertainty and robustness of ML! (1) published in Nature Machine Intelligence and (2) accepted at NeurIPS for presentation in Dec 2020.