Ashutosh Trivedi's research interests lie at the intersection of computer science, control theory, and machine learning. His research focuses on applying rigorous mathematical reasoning techniques to design and analyze safe and secure cyber-physical systems (CPS) with guaranteed performance. Ashutosh investigates foundational issues (decidability, undecidability, computational complexity, and efficient algorithms) related to modeling and analysis of learning-enabled CPS as well as practically focused tools that can be used by practitioners to analyze large systems at scale. The interplay between deep fundamental problems and their applications to building safe and secure systems have produced solutions for many interesting theoretical challenges as well as tools that can analyze CPS at scale.
CSCI 2270 - Computer Science 2: Data Structures
Spring 2019 / Spring 2020
Studies data abstractions (e.g., stacks, queues, lists, trees, graphs) and their representation techniques (e.g., linking, arrays). Introduces concepts used in algorithm design and analysis including criteria for selecting data structures to fit their applications. Degree credit not granted for this course and CSCI 2275. Same as CSPB 2270.
CSCI 5444 - Introduction to Theory of Computation
Reviews regular expressions and finite automata. Studies Turing machines and equivalent models of computation, the Chomsky hierarchy, context-free grammars, push-down automata, and computability.
CSCI 5854 - Theoretical Foundations for Cyber-Physical Systems
Covers techniques for modeling, design and verification of Cyber-Physical Systems and application domains including automotive systems, robotics and medical devices. Modeling topics include timed systems, differential equations, switched systems, hybrid dynamical systems. Verification topics: reachability and stability verification. Temporal specifications. Synthesis of controllers. Applications: automotive systems, medical devices.