Past Events

In this seminar, we discuss various aspects of artificial intelligence (AI) planning. Our mission is to devise a plan for a robust, resilient, and provably correct autonomous system under real-world conditions. For example, reinforcement learning promises that autonomous systems can learn to operate in unfamiliar environments with minimal human intervention. However, why haven't most autonomous systems implemented reinforcement learning yet? The answer is simple: there are significant unsolved challenges. One of the most important ones is obvious: Autonomous systems operate in unfamiliar, unknown environments. This lack of knowledge is called uncertainty. To tackle these challenges, we combine the areas of AI and Formal Methods and employ neurosymbolic AI methods to achieve trustworthy, reliable, and safe artificial intelligence

Dr. Sze Zheng Yong is an Associate Professor with the Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA. Prior to that, he was an Assistant Professor in the School for Engineering of Matter, Transport and Energy at Arizona State University and a postdoctoral fellow in the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. He received a Dipl.-Ing. (FH) degree in Automotive Engineering with a specialization in mechatronics and control systems from the Esslingen University of Applied Sciences, Germany in 2008, and S.M. and Ph.D. degrees in Mechanical Engineering from Massachusetts Institute of Technology, Cambridge, MA, in 2010 and 2016, respectively. Dr. Yong was the recipient of the DARPA Young Faculty Award in 2018, the NSF CAREER and NASA Early Career Faculty awards in 2020, and the ONR Young Investigator Program Award in 2022. His research interests include the broad areas of control, estimation, planning, identification, and optimization of hybrid systems, with applications to autonomous, robotic, and cyber-physical dynamic systems and their safety, robustness, and resilience.

Daniel Fried is an assistant professor in the Language Technologies Institute at Carnegie Mellon University since Fall 2022. His research in natural language processing focuses on grounding, interaction, and applied pragmatics, with a particular focus on language interfaces such as grounded instruction following and code generation. Previously, he was a postdoc at Meta AI and the University of Washington and completed a PhD at UC Berkeley. His work has been supported by a Google PhD Fellowship and a Churchill Fellowship.

Jared Miller is a 5th year PhD Student at the Robust Systems Lab at Northeastern University, advised by Mario Sznaier. He received his B.S. and M.S. degrees in Electrical Engineering from Northeastern University in 2018. He is a recipient of the 2020 Chateaubriand Fellowship from the Office for Science Technology of the Embassy of France in the United States. He was given an Outstanding Student Paper award at the IEEE Conference on Decision and Control in 2021 and in 2022. His current research topics include safety verification and data-driven control. His interests include large-scale convex optimization, nonlinear systems, semi-algebraic geometry, and measure theory.

 Dr. Jared Culbertson is a research mathematician with the U.S. Air Force Research Laboratory's Autonomous Capabilities Team (ACT3), a research group focused on the development and deployment of flexible AI solutions across a diverse set of air and space mission areas. Jared's research primarily deals with fundamental aspects of representational structures, recently involving compositional approaches for hybrid dynamical systems and now focused on behavior acquisition, diversity, and composition in reinforcement learning problems. 

Abhishek Kulkarni is a Ph. D. candidate in Electrical and Computer Engineering at the University of Florida (UF), Gainesville. Before moving to UF, he was a Ph. D. candidate in Robotics Engineering at Worcester Polytechnic Institute, where he also earned his master’s degree in Robotics Engineering. He received his bachelor’s degree from Vishwakarma Institute of Technology (VIT), Pune, India in Electronics and Telecommunications Engineering. At VIT, he co-founded Cognitive Robotics and Intelligent Systems Lab (CRISTL), which was the first lab on campus focusing on the theoretical foundations of robot cognition. The challenges of designing reliable, robust and reasonable autonomous systems that Abhishek faced while leading CRISTL have shaped his current research interests that lie at the intersection of formal methods and game theory with applications to robotics and cyber-physical systems.