Leads the VITA Group, working on efficient and robust deep learning, sparse neural networks and large language models.
Trustworthy & safe ML
Large language models
Efficient training & inference
2026A Physics-Grounded Benchmark for Multi-Agent Dynamics in World ModelsECCV
2026Beyond Test-Time Memory: State-Space Optimal Control for LLM ReasoningICML
165 T1 · 197 papers · 15 members · 45 alumni
USUT AustinComputer Science
Peter StoneProfessor, Truchard Foundation Chair in Computer Science
Leads the Learning Agents Research Group, working on reinforcement learning, multiagent systems, robot soccer and autonomous agents.
Reinforcement learning
Embodied & active vision
2026Coadaptive Value AlignmentAAMAS
2026Factored Latent Action World ModelsICML
68 T1 · 117 papers · 10 members · 48 alumni
USUT AustinDepartment of Aerospace Engineering and Engineering Mechanics
Ufuk TopcuAssociate Professor Cockrell Family Dean's Chair for Engineering Excellence
Designs and verifies autonomous systems, combining formal methods, control and learning for safe decision-making.
Autonomous vehicles & drones
Planning, control & multi-robot systems
Reinforcement learning
2026Do LLMs Strategically Reveal, Conceal, and Infer Information? A Theoretical and Empirical Analysis in The Chameleon GameAAMAS
2026IG-MCTS: Human-in-the-Loop Cooperative Navigation under Incomplete InformationAAMAS
51 T1 · 125 papers · 17 members · 24 alumni
USUT AustinComputer Science
Kristen GraumanProfessor, Professorship in Computer Sciences #4
Leads the UT Computer Vision Group, working on visual recognition, video analysis, first-person (egocentric) vision and embodied multimodal perception.
Recognition, detection & segmentation
Video understanding
Multimodal & grounded language
2026Audio-Visual Camera Pose Estimation with Passive Scene Sounds and In-the-Wild VideoECCV
2026Don't Let the Video Speak: Audio-Contrastive Preference Optimization for Audio-visual Language ModelsECCV
Works on efficient machine learning and ML systems for generative models and robotics, such as fast diffusion and 3D perception.
Efficient training & inference
Generative & foundation models
Image & video generation
2026CaptionQA: Is Your Caption as Useful as the Image Itself?CVPR
2026QuantV2X: A Fully Quantized Multi-agent System for Cooperative PerceptionECCV
38 T1 · 45 papers · 5 members
USUT AustinComputer Science
Qiang LiuAssociate Professor, Faculty Fellowship #7 in Computer Science
Leads the Statistical Learning & AI Group, working on probabilistic inference, Stein variational methods, generative models such as rectified flow and deep…
Learning theory & optimization
Probabilistic & causal inference
Efficient training & inference
2026∇-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Latent SpaceICLR
2026Cautious Optimizers: Improving Training with One Line of CodeICLR
33 T1 · 34 papers · Group size not listed
USUT AustinComputer Science
Aditya AkellaProfessor, Regents Chair in Computer Sciences #1
Builds programmable networks, programmable NICs and hardware acceleration, and systems for big data and machine learning workloads such as serverless computing.
Accelerators, chips & architecture
Data management & big data
2026Canopy: Property-Driven Learning for Congestion ControlEuroSys
2026Express Lane to Efficiency and Reliability: Multi-Dimensional Control in Meta's Express Backbone NetworkNSDI
Develops scalable machine learning algorithms that exploit problem structure, including matrix and tensor methods, extreme classification and large-scale data…
Recognition, detection & segmentation
Visualization & visual analytics
2026PyraTok: Language-Aligned Pyramidal Tokenizer for Video Understanding and GenerationCVPR
2026The Art of Scaling Reinforcement Learning Compute for LLMsICLR