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Trivedi, Ashutosh

Associate Professor

Positions

Research Areas research areas

Research

research overview

  • Artificial Intelligence (AI)-assisted software solutions have made substantial inroads into critical aspects of modern life, where they routinely make safety-critical, socio-critical, and legal-critical decisions with certainty and speed. Examples of such AI-assisted decisions include self-driving cars deciding to stop, implantable pacemakers determining when to pace, and the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) software assessing whether individuals are likely to reoffend. These AI-assisted systems are data-driven: they adapt their behavior based on experiences in the form of data—whether expertly curated datasets in supervised learning, hidden patterns in raw data uncovered through unsupervised learning, or self-generated data guided by expertly designed reward signals in reinforcement learning. Dr. Ashutosh Trivedi's research focuses on enabling rigorous system engineering, specifically through formal methods, to enhance the safety, security, fairness, and accountability of data-driven systems.

keywords

  • Safety in AI, Reinforcement Learning, Formal Methods, Software Fairness, Software Accountability

Publications

selected publications

Teaching

courses taught

  • CSCI 2270 - Computer Science 2: Data Structures
    Primary Instructor - Spring 2019 / Spring 2020 / Spring 2021 / Spring 2022 / Spring 2023
    Studies data abstractions (e.g., stacks, queues, lists, trees, graphs, heaps, hash tables, priority queues) 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. Knowledge OF C++ is highly recommended. Degree credit not granted for this course and CSCI 2275. Same as CSPB 2270.
  • CSCI 3434 - Theory of Computation
    Primary Instructor - Fall 2023
    Introduces the foundations of formal language theory, computability, and complexity. Shows relationship between automata and various classes of languages. Addresses the issue of which problems can be solved by computational means, and studies complexity of solutions.
  • CSCI 4831 - Special Topics in Algorithms
    Primary Instructor - Spring 2024
    Covers topics of interest in computer science at the upper-division undergraduate level. Content varies from semester to semester. Only 9 credit hours from CSCI 4830 and/or CSCI 4831 can count toward Computer Science BS or BA.
  • CSCI 5444 - Introduction to Theory of Computation
    Primary Instructor - Fall 2018 / Fall 2020 / Fall 2021 / Fall 2022 / Fall 2023
    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 of Autonomous Systems
    Primary Instructor - Spring 2018
    Covers techniques for modeling, design and verification of autonomous 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.
  • CSCI 6950 - Master's Thesis
    Primary Instructor - Spring 2020 / Summer 2022 / Fall 2022 / Spring 2023 / Fall 2023 / Spring 2024 / Summer 2024
  • CSCI 7000 - Current Topics in Computer Science
    Primary Instructor - Fall 2019 / Summer 2023 / Spring 2024
    Covers research topics of current interest in computer science that do not fall into a standard subarea. May be repeated up to 18 total credit hours.

Background

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Github

  • astrivedi