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 named the European Union Young Scientist of 2011 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.

Teaching

Academic courses I teach.

Blog

Blog about my research.

News

Oct 2018   Invited Talk at IROS in Madrid, Spain: Variational Autoencoder for End-to-End Control of Autonomous Driving with Novelty Detection and Training De-biasing
Jun 2018   Paper: Variational Autoencoder for End-to-End Control of Autonomous Driving with Novelty Detection and Training De-biasing has been accepted to IROS 2018 [paper]
Jun 2018   Graduation: Master of Science (MS) from MIT in EECS. Thesis: Robust Learning for End-to-End Autonomous Driving [thesis]
Mar 2018   Invited Talk at NVIDIA's GTC in San Jose, California: Learning Steering Bounds for Parallel Autonomy [talk]
Jan 2018   Lecturer for MIT 6.S191: Introduction to Deep Learning, MIT's official course on deep learning applications and foundations. [link]