Department of Computer Science - University of Texas at Austin

Panel: Is AI Really Coming for Your Job? Agentic AI and the Future of Work

One panel, “Reimaging Work in the Age of AI Agents,” focused on the ongoing rise of agentic AI in the workplace. Right off ...
Department of Computer Science - University of Texas at Austin

UT Austin Computer Science Ranked 10th Best in the Country

UT Computer Science ranks 10th nationally with four “specialties,” or areas of research; also ranked in the top ten at UTCS: Artificial Intelligence moving up to 7th, Programming Languages ranked 7th, ...
Department of Computer Science - University of Texas at Austin

Top 4 Harmful Traits of AI Companions

As outlined in the paper: Harmful Traits of AI Companions, a cross-disciplinary team of researchers from UT Austin, the ...
www.cs.utexas.edu

CS373: Software Engineering

Spring 2026: 53375 (58), 53380 (58) 12 Jan - 27 Apr 2026 MWF 10 am & 11 am: RLP 1.106 https://gitlab.com/gpdowning-ut/cs373/ https://www.cs.utexas.edu/~utpc/ ...
www.cs.utexas.edu

Anna Gál

circuit complexity, lower bound methods, algorithms, and combinatorics. Jeff Ford, ``Lower Bound Methods for Multiparty Communication Complexity'', Ph. D. May 2006. Vladimir Trifonov, ``Techniques for ...
www.cs.utexas.edu

Transfer Learning for Reinforcement Learning Domains: A Survey

Transfer Learning for Reinforcement Learning Domains: A Survey. Matthew E. Taylor and Peter Stone. Journal of Machine Learning Research, 10(1):1633–1685, 2009.
www.cs.utexas.edu

Artificial Intelligence and Life in 2030

Artificial Intelligence and Life in 2030. Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram ...
www.cs.utexas.edu

A collective AI via lifelong learning and sharing at the edge

One vision of a future artificial intelligence (AI) is where many separate unitscan learn independently over a lifetime and share their knowledge with eachother. The synergy between lifelong learning ...
www.cs.utexas.edu

Multiagent Systems: A survey from a machine learning perspective

Multiagent Systems: A survey from a machine learning perspective. Peter Stone and Manuela Veloso. Autonomous Robots, 8(3):345–383, July 2000. @Article(MASsurvey, Author="Peter Stone and Manuela Veloso ...
www.cs.utexas.edu

Marginal Cost Pricing for System Optimal Traffic Assignment with Recourse under Supply-Side Uncertainty

Transportation networks are often subject to fluctuations in supply-side parameters such as capacity and free-flow travel time due to factors such as incidents, poor weather, and bottlenecks. In such ...
www.cs.utexas.edu

Learning to Interpret Natural Language Commands through Human-Robot Dialog

Learning to Interpret Natural Language Commands through Human-Robot Dialog. Jesse Thomason, Shiqi Zhang, Raymond Mooney, and Peter Stone. In Proceedings of the 2015 International Joint Conference on ...
www.cs.utexas.edu

Deep Reinforcement Learning in Parameterized Action Space

Recent work has shown that deep neural networks are capable ofapproximating both value functions and policies in reinforcementlearning domains featuring continuous state and actionspaces. However, to ...