Machine Learning for Safety-Critical Systems

Navid Azizan

Esther & Harold E. Edgerton Career Development Assistant Professor, Massachusetts Institute of Technology

Oct. 27, 2023 (Friday), 12:00 – 1:00 pm

Room 220, Building C

Abstract: The integration of machine learning, particularly deep neural networks (DNNs) into autonomous systems has revolutionized their capabilities, enabling sophisticated interpretation of high-dimensional sensory data and informed decision-making. However, the deployment of these systems in safety-critical applications is hindered by the opaque nature of DNNs, especially their unpredictable behavior under out-of-distribution (OoD) or anomalous conditions. This talk presents recent results on enhancing the safety and reliability of machine learning models for autonomous systems. Specifically, we will discuss (1) run-time monitors for learning-enabled components, namely uncertainty estimation and anomaly detection mechanisms for pre-trained models as well as latent representations, mitigating risks associated with unforeseen operational deviations; (2) model adaptation techniques and continual learning algorithms, to ensure consistent integration of new data without the setback of “catastrophic forgetting,” thereby sustaining the model’s adaptiveness and relevance in dynamic environments; and (3) safety-assured, learning-based control and decision-making systems, focusing on controllers intrinsically designed with safety and stability guarantees.

Bio: Navid Azizan is the Esther & Harold E. Edgerton (1927) Assistant Professor at MIT, where he is a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS) and holds dual appointments in the Department of Mechanical Engineering (Control, Instrumentation, & Robotics) and the Schwarzman College of Computing’s Institute for Data, Systems, & Society (IDSS). His research interests broadly lie in machine learning, systems and control, mathematical optimization, and network science. His research lab focuses on various aspects of enabling large-scale intelligent systems, with an emphasis on principled learning and optimization algorithms for autonomous systems and societal networks. He obtained his PhD in Computing and Mathematical Sciences (CMS) from the California Institute of Technology (Caltech), co-advised by Babak Hassibi and Adam Wierman, in 2020, his MSc in electrical engineering from the University of Southern California in 2015, and his BSc in electrical engineering with a minor in physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc in the Autonomous Systems Laboratory (ASL) at Stanford University in 2021. Additionally, he was a research scientist intern at Google DeepMind in 2019. His work has been recognized by several awards, including the 2020 Information Theory and Applications (ITA) Gold Graduation Award and the 2016 ACM GREENMETRICS Best Student Paper Award. He was named in the list of Leading Academic Data Leaders from the CDO Magazine in 2023, named an Amazon Fellow in Artificial Intelligence in 2017, and a PIMCO Fellow in Data Science in 2018. He was also the first-place winner and a gold medalist at the 2008 National Physics Olympiad in Iran.