Ashutosh TrivediAssociate Professor of Computer Science
Email: ashutosh.trivedi @ colorado.edu
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:
Thesis. Differential Performance Debugging and its application to side-channel analysis (2020)
First Employment. Assistant Professor at UT El Paso
Thesis. Perfect Subclasses of Real-timed Recursive Systems (2020)
First Employment. Senior Software Engineer, Mathworks