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ESE Spring Seminar – “End-to-end Learning for Robust Decision Making”
April 8 at 11:00 AM - 12:00 PM
Because the physical world is complex, ambiguous, and unpredictable, autonomous agents must be engineered to exhibit a human-level degree of flexibility and generality — far beyond what we are capable of explicitly programming. Achieving such rich and intricate decision making requires rethinking the foundations of intelligence across all stages of the autonomous learning lifecycle.
In this talk, I will share new learning-based approaches towards dynamic, resilient, and robust decision making of autonomous systems. Such solutions are capable of not only reliably solving a particular problem, but also anticipating what could go wrong in order to strategize, adapt, and continuously learn. We advance robust decision making by (1) computationally designing rich synthetic environments of hard to collect, out-of-distribution edge-cases; (2) creating efficient, expressive, and interpretable learning models; and (3) developing adaptive, robust, and grounded learning algorithms, and exploiting their interdependence to realize generalizable decision making.
Ph.D. Candidate in the Computer Science and Artificial Intelligence Lab (CSAIL), MIT
Alexander Amini is a PhD student at the Massachusetts Institute of Technology (MIT), in the Computer Science and Artificial Intelligence Laboratory (CSAIL), with Prof. Daniela Rus. His research focuses on developing the science and engineering of autonomy and its applications to safe decision making for autonomous agents. His work has spanned learning end-to-end control (i.e., perception-to-actuation) of autonomous systems, formulating confidence of neural networks, mathematical modeling of human mobility, as well as building complex inertial refinement systems. In addition to research, Amini is the lead organizer and lecturer for MIT 6.S191: Introduction to Deep Learning, MIT’s official introductory course on deep learning. Amini is a recipient of the NSF Graduate Research Fellowship and completed his Bachelor of Science (B.S.) and Master of Science (M.S.) in Electrical Engineering and Computer Science at MIT, with a minor in Mathematics.