Research

Machine learning spans a range of topical areas. The guide below can be used to identify labs, faculty, and scientists conducting research in each of these areas.

Active Learning
Active Learning

Prioritizing data labelling to optimize the training of supervised models

Bioinformatics
Bioinformatics

The science of collecting and analyzing complex biological data using computational methods

Computational Neuroscience
Computational Neuroscience

The development of mathematical tools and multi-scale models to investigate neural function

Deep Learning
Deep Learning

Algorithms inspired by the structure and function of the brain referred to as artificial neural networks

Evolutionary Computation
Evolutionary Computation

The development of algorithms inspired by biological evolution to solve computational problems

Explainable AI
Explainable AI

Frameworks to develop interpretable, high-confidence machine learning models

Graphical Models
Graphical Models

The use of graphs to represent domain problems

Graphics
Graphics & Visualization

Generating graphics with the aid of machine learning

Knowledge Representation
Knowledge Representation

Encoding human reasoning into a symbolic language that can be processed by information systems

Large Scale Machine Learning
Large Scale Machine Learning

Machine learning systems that are able to process high-volume, open-ended data sets

Machine Learning Theory
Machine Learning Theory

The study of the theoretical and mathematical frameworks that underpin machine learning

Natural Language
Natural Language

A field that gives machines the ability to read, understand and derive meaning from human language

Networks
Networks

Developing efficient algorithms and systems to response to different network scenarios

Optimization
Optimization

The study of methods to optimize the performance of machine learning models

Recommender Systems
Recommender Systems

The use of machine learning methods in a manner that seeks to predict user preferences

Reinforcement Learning
Reinforcement Learning

Enabling an agent to learn by trial and error using feedback from actions and experiences

Robotics
Robotics

The study of devices that can move and react to sensory input

Signal Processing
Signal Processing

Using computational models to make explicit information contained within a signal

Statistical Machine Learning
Statistical Machine Learning

A framework for machine learning that draws from the field of statistics

Variational Inference
Variational Inference

A machine learning method that enables computation of specific distributions through optimization

Virtual Reality
Virtual Reality

The simulation of the real world in a virtual environment

Vision
Vision

Training computers to interpret and understand digital images and videos

Wireless Communication
Wireless Communication

The transmission of information from one point to other without using a connecting physical medium

Artificial Intelligence Lab

The AI Lab, founded in 1983, investigates the central challenges of machine cognition, especially machine learning, knowledge representation and reasoning, and robotics.

Center for Big Data Analytics

The Center for Big Data Analytics (CBDA) at the University of Texas at Austin is an interdisciplinary research center focusing on large-scale data analysis.

Computational Visualization Center

The Computational Visualization Center develops and improves the core technologies for comprehensive computational modeling, simulation, analysis, and visualization of natural and synthetic phenomena, and then utilize them as an integrated tool for rapid discovery.

Computer Vision

Studies visual recognition and visual search. Recent and ongoing projects in the group consider large-scale image/video retrieval, unsupervised visual discovery, active learning, active recognition, first-person "egocentric" computer vision, interactive machine learning, image and video segmentation, activity recognition, vision and language, and video summarization.

Deep Learning

Research focus in computer vision, machine learning and computer graphics with an emphasis on deep learning, as well as image segmentation and understanding.

Graphics and AI

The intersections of Graphics and various fields in AI, including natural language processing, robotics, computer vision, and machine learning.

HuthLab

The HuthLab uses quantitative, computational methods to try to understand how the human brain processes the natural world. The lab is focused on understanding how the meaning of language is represented in the brain.

IDEAL Lab

IDEAL is primarily concerned with analyzing and mining 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.

Learning Agents Research Group

Research in LARG aims to understand how to best create complete intelligent agents, and focuses on a number of areas such as machine learning, multiagent systems, and robotics.

Machine Learning

Machine learning research group studies adaptive computational systems that improve their performance with experience with a current focus on natural language learning.

Neural Networks

Neural Networks’ research concentrates on cognitive science, computational neuroscience, and evolutionary computation, including natural language processing, episodic memory, concept and schema learning, the visual cortex, and evolving neural networks in sequential decision tasks such as robotics, game playing, and resource optimization.

Personal Autonomous Robotics Lab

The goal of PEARL is to enable personal robots to be deployed in the home and workplace with minimal intervention by robotics experts. In settings such as these, robots do not operate in isolation, but have continual interactions with people and objects in the world. PEARL focuses on developing algorithms to solve problems that robot learners encounter in real-world interactive settings.

Virtual Reality Lab

The virtual reality lab examines two scientific problems: human vision and motor control. The vision research attempts to understand the function of vision in the context of daily tasks. The second direction of research that we are pursuing involves studying the characteristics of human motion.

Wireless Systems Innovation Laboratory

Research focus on wireless communications, signal processing, matrix analysis, and information theory. This lab is interested in solving theoretical problems that find applications in real systems.

Wireless Networking and Communications Group

The mission of the WNCG is to create 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.