Brings computational efficiency to applications with high societal impact and develops holistic solutions spanning algorithmic, system, and architecture level.

Provides tools, methodologies, and knowledge for engineering the machines that support intelligent applications, from the smallest circuits to the largest systems. Machine learning, reasoning, and understanding from cloud to edge.

NSF AI Institute that advances technological leadership in future generation edge networks (6G and beyond) and distributed AI.

Advancing innovation in MRI analysis and developing cutting-edge generative AI methods for health-risk prediction.

Advances core AI research in machine learning, reasoning, and robotics, with leading faculty, top-ranked Ph.D. students, and a legacy of pioneering innovation.

Builds AI models and evaluates performance and end-user impact to create automated, human-safe practices to safeguard the information environment.

The Advanced Robotic Technologies for Surgery (ARTS) Lab develops high dexterity and situationally aware continuum manipulators, soft robots, and instruments especially designed for less/minimally invasive treatment of various medical applications.

Advances autonomous systems through research in perception, planning, control, and human interaction, developing robots that operate accurately and efficiently in complex, unstructured environments.

Develops theory and algorithms for the design and verification of autonomous systems in the intersection of computing, control theory, and learning theory.

Addressing fundamental challenges in autonomous system development via  machine learning, game theory, information theory, and formal methods.

Creating digital AI models that aid in the diagnosis and treatment of diseases.

Powered by one of the largest GPU computing clusters in academia, CGAI advances research across the biosciences, health care, computer vision and natural language processing.

Applies neurophysiological, anatomical, computational, and mathematical methods to advance our understanding of how the brain interprets sensory information to create our perception of the world around us.

Harnesses the power of data through the lens of physics-based modeling to embrace the opportunities and challenges of machine learning in complex applications across science, engineering and medicine.

Directs the use of human brain signals to control devices, interact with our environment, and eventually recover from insults to and deficits of our central nervous system.

NSF-Simons AI Institute building a new breed of astronomical tools by harnessing the power of AI to accelerate our understanding of the universe.

Applies deep learning to improve MRI reconstruction methods for diagnosis and monitoring that can be used across disparate system hardware and clinical needs.

Develops theoretically grounded algorithms for analyzing large-scale, high-dimensional data, with a focus on clustering, classification, and visualization in fields like text mining and bioinformatics.

Interdisciplinary research grand challenge that works to define, evaluate, and build ethical human + AI systems.

Established in 2011, the Human Centered Robotics lab designs humanoid robots and researches bipedal locomotion

Explores psychological understanding of AI and develops human-centered methods and systems for better AI-integrated workplaces, smart communities and cities, and online information.

This lab uses fMRI and computational models to study how the brain represents language. It maps brain responses to natural speech, decodes meaning from brain activity, and shares open datasets and tools.

Analyzes and mines complex data in various forms — including structured and semi-structured data, signal streams, images and videos — in order to characterize and understand the underlying phenomena and obtain actionable insights.

NSF AI Institute developing key foundational tools for the next decade of AI innovation.

LARG advances AI through research on autonomous agents that learn, plan, and collaborate in dynamic environments, with applications in robotics, traffic systems, gaming, and human interaction.

Pioneered the use of visual neuroscience to create picture and video quality measurement and monitoring tools that control the quality and bandwidths of a large percentage of all streaming video.

UT Austin’s headquarters for advancing core ML algorithms and applications. It unites researchers across campus to drive breakthroughs in deep learning, optimization, fairness, and scalability.

Explores a broad range of machine learning topics, with a current emphasis on natural language learning, multimodal reasoning, and human-centered AI systems.

Building better robot bodies through intentional design for optimized interaction with their environments.

Explores neuroevolution, cognitive modeling, and computational neuroscience to advance machine learning, understand brain function, and develop intelligent behavior in artificial agents.

Brings the power of computational modeling to grand challenge problems that require estimation, design and control.

Develops innovative robotic devices to improve the quality of life and rehabilitation of those surviving a disability.

Develops algorithms for robot learning, perception, control, and planning to enable intelligent behavior in real-world settings. We welcome motivated students to join through official channels.

Develops algorithms for general-purpose robot autonomy by integrating perception and action. The group advances robotics and embodied AI to enable adaptable, intelligent agents in unstructured environments.

Applies network science, machine learning and optimization, and hardware prototyping to explore new avenues in system design.

The UT Statistical Learning & AI Group develops advanced machine learning and statistical methods for reasoning with complex, structured models in massive datasets, with applications in inference, optimization, and model evaluation.

Consortium on Law and Ethics of A.I. and Robotics (CLEAR) brings together leaders in the academy, industry, and government to promote understanding of the legal, ethical, and policy challenges posed by emerging AI technologies.

Engages the best minds in academia, government and the private sector in developing practical solutions to the pressing problems of an increasingly globalized world.

Conducts research in natural language processing and machine learning, focusing on improving large language models for complex tasks in science, medicine, and law through enhanced reasoning, reliability, and evaluation.

Explores imbalances in AI technologies and provides insights that inform public policies and programs focused on digital inclusion for everyone.

World-class robotics education and innovative research teams that emphasizes long-term autonomy and human-robot interaction.

NSF AI Institute for Learning-Enabled Optimization at Scale is making impossible optimizations possible, at scale and in practice.

Advances foundational research in data science and machine learning by uniting computer science, engineering, math, and statistics. It fosters collaboration, supports fellowships, and develops next-generation theory and curriculum.

UT Austin Villa is the University of Texas at Austin's RoboCup soccer team, competing in both legged and simulation leagues. With numerous international championships, the team pioneers research in autonomous, multiagent robotic systems.

Builds cost-efficient and sustainable inference serving systems for the emerging massive neural networks, including LLMs, MoEs, and Multimodal ML models. 

Explores theoretical and algorithmic foundations of generative AI and neurosymbolic AI with the goal of enhancing efficiency, trustworthiness, and reasoning capabilities in large language models (LLMs), as well as driving innovation in 3D/4D computer vision.

Creates a collaborative environment that supports research, provides highly relevant education and opportunities, promotes technical innovation, imagination and entrepreneurship in wireless networking, communications and data sciences.