Ashutosh Trivedi

Associate Professor of Computer Science

Email: ashutosh.trivedi @ colorado.edu
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.

News.

[Jan '25] Job Opportunity: Undergraduate Research Assistant in Neurosymbolic AI
- Job Title: Undergraduate Summer Research Assistant
- Key Responsibilities:
  - Training systems using Reinforcement Learning and Hebbian Learning
  - Deploying SMT solvers to verify system properties
  - Writing research papers
- Qualifications:
  - Strong background in discrete mathematics
  - Proficiency in machine learning, particularly Reinforcement Learning and Hebbian Learning
  - Aptitude for theoretical computer science
- Compensation: $18–$28 per hour (based on prior experience)
- Application Process: Interested candidates should email their application to ashutosh.trivedi@colorado.edu.
[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 .