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Ashutosh Trivedi

Associate Professor of Computer Science

Email: ashutosh.trivedi @
Web: Home , Twitter, Google Scholar, DBLP, ResearchID, ORCID , and ResearchGate.
Research Interests : Safety in AI · Reinforcement Learning · Formal Methods · Software Fairnes · Software Accountability
Group: Programming Languages and Verification (CUPLV)

Artificial Intelligence (AI) assisted software solutions have made substantial inroads in critical aspects of modern existence where they routinely make safety-, socio-, and legal- critical decision with certainty and swift. Instances of such AI-assisted decisions include: self-driving cars deciding to stop, implantable pacemakers deciding to pace, or the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) software deciding if individuals are prone to reoffend. These AI-assisted software are data-driven: they adapt their behavior based experiences in the form of data: be it the expertly curated data in supervised learning, surprising patterns hidden in raw data in unsupervised learning, or the self-generated data guided by expertly designed reward signals in reinforcement learning. The focus of my research is on enabling rigorous system engineering of data-driven system towards improved safety, privacy, fairness, and accountability.

While formal methods for rigorous system engineering provide principles, processes, and practices for traditional systems development, data-driven systems---due to their statistical, inductive, and adaptive nature---demand a paradigm shift. My research seeks to understand and to redefine the role of formal methods in data-driven system development. While I continue to leverage my expertise in analyzing functional requirements including safety and privacy, I am actively exploring the applications of formal methods in analyzing legal and societal implications of data-driven software systems. Some notable examples from my current research include:

  • the role of formal requirements in reinforcement learning ,
  • the use of automatic testing and debugging in fairness-aware configuration of machine learning libraries, and
  • the role of formal specifications in expressing correctness requirements for the tax-preparation software.


  • [June'24] Job Opportunity: Undergraduate Summer Research Assistant in Neurosymbolic AI: We, along with Prof. Fabio Somenzi, are seeking a motivated undergraduate summer research assistant to join our project in Neurosymbolic AI.
    • Job Title: Undergraduate Summer Research Assistant

    • Key Responsibilities: 1) Training neural networks and 2) Deploying SMT solvers to verify their properties.
    • Qualifications: 1) Strong background in discrete mathematics, 2) Proficiency in programming, and 3) Aptitude for theorem proving.
    • Compensation: $18 per hour, Up to 25 hours per week
    • Application Process: Interested candidates should send an email to
  • [May'22] The call for papers for the 20th ACM/IEEE International Symposium on Formal Methods and Models for System Design (MEMCODE) is out. Consider submitting your latest work on formal methods and models.
  • Our article in the Annual Reviews in Control on Secure-by-construction synthesis of cyber-physical systems is available online.
  • [Apr'22] Congratulations to Mateo Perez on receiving Bell Foundation Outstanding Research Award 2022.
  • [Apr'22] Congratulations to Vishnu Murali on receiving Departmental Outstanding Research Award 2022.
  • [Feb'22] I look forward to organizing the 8th workshop on Symbolic-Numeric Methods for Reasoning about CPS and IoT
  • [Jan'22] Paper by Murali et al. “k-Inductive Barrier Certificates for Stochastic Systems” accepted to HSCC’22.
  • [Jan'22] Received the NSF 2022 CAREER Award to investigate Reinforcement Learning for Recursive Markov Decision Processes and Beyond.
  • [Jan'22] I am teaching CSCI 2270 (Data Structures) this spring (Details on Canvas).
  • [Dec'21] Paper by Velasquez et al. on "Controller Synthesis for Omega-Regular and Steady-State Specifications" accepted to AAMAS’22.
  • [Dec'21] Paper by Perez et al. on "Translating Omega-Regular Specifications to Average Objectives for Model-Free Reinforcement Learning” accepted to AAMAS’22.
  • [Dec'21] Paper by Tizpaz-Niari et al. on "Fairness-aware Configuration of Machine Learning Libraries" accepted to ICSE'21.
  • [Nov'21] Congratulations to Taylor Dohmen on delivering an excellent presentation on "Regularity in Transducers and Applications in Reinforcement Learning" for his area exam.
  • [Oct'21] Tianhan Lu wins the Radhia Cousot Young Researcher Best Paper Award at SAS 2021.
  • [Aug'21] Congratulations to Tianhan Lu on successfully defending his thesis proposal.
  • [Aug'21] Paper by Komp et al. on “Event-Triggered and Time-Triggered Duration Calculus for Model-Free Reinforcement Learning” accepted to RTSS'21.
  • [July'21] Paper by Perez et al. “Model-Free Reinforcement Learning for Lexicographic Omega-Regular Objectives” accepted to Formal Methods (FM'21).
  • [July'21] Paper by Murali et al. on “Safety Verification of Dynamical Systems via k-Inductive Barrier Certificates” accepted to CDC'21.
  • [July'21] Paper by Dohmen et al. on “Regular Model Checking with Regular Relations” accepted to Fundamentals of Computation Theory (FCT'21).
  • [July'21] Paper by Lu et al. accepted to SAS 2021.
  • [June'21] Mungojerrie (beta release) is available for download.
  • [April'21] Paper by Perez et al. on Model-Free Reinforcement Learning for Branching Markov Decision Processes accepted to CAV'21 .

Current PhD Students.

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Taylor Dohmen

Interests. Regular Relations and Reinforcement Learning

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John Komp

Interests. Duration Calculus and Reinforcement Learning

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Tianhan Lu

Interests. Static Analysis for Resource and Cost Bounding             

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Vishnu Murali

Interests. Foundations of Cyber-Physical Systems Verification and Synthesis

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Mateo Perez

Interests. Automata-Theoretic Reinforcement Learning

Graduated PhD Students

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Saeid Tizpaz-Niari

Thesis. Differential Performance Debugging and its application to side-channel analysis (2020)

First Employment. Assistant Professor at UT El Paso

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Devendra Bhave

Thesis. Perfect Subclasses of Real-timed Recursive Systems (2020)

First Employment. Senior Software Engineer, Mathworks