We seek to advance state-of-the-art artificial intelligence (AI) technology to develop next generation solutions in numerous high-impact areas. As individuals, companies, and governments produce ever-more "big data" with increasing volume, variety, and velocity, data science and machine learning have both become increasingly important in harnessing this data to deliver new insights and valuable capabilities. We are pioneering new intelligent algorithms and dynamic systems, leveraging the latest pattern recognition and statistical methods to innovate, transform, and disrupt. We develop the technologies of tomorrow, while evaluating the state of the art algorithms in new domains.
Cutting Edge Collaborators
Computing research at UT Austin is world-class. In 2019, UT launched Good Systems, an eight-year, university-wide Grand Challenge Initiative to design AI technologies that maximally benefit society. Our research group regularly collaborates with other Machine Learning faculty, students, and research staff across UT, including Computer Science , Electrical and Computer Engineering , Information, Risk & Operations Management , Computational Linguistics , Mathematics , Statistics & Data Science, and the Texas Advanced Computing Center.
Interested in Working With Us?
Our research assistants typically have strong computing and math backgrounds. We also work with and mentor students from other backgrounds who bring other diverse skills to bear on these problem areas. The primary qualities a student needs to succeed is the passion, drive, and imagination to do good work which will change the world. We are not standing by the sidelines to wait to see what tomorrow's world will look like. Instead, we are the ones leading the charge to build technology and make discoveries that will impact the world we live in today. This is what it means to be at a world-class research university and lead the charge at the forefront of science. There's no better place to be!
Dr. Ying Ding is Bill & Lewis Suit Professor at School of Information, University of Texas at Austin. She has been involved in various NIH, NSF and European-Union funded projects. She has published 240+ papers in journals, conferences, and workshops, and served as the program committee member for 200+ international conferences. She is the co-editor of book series called Semantic Web Synthesis by Morgan & Claypool publisher, the co-editor-in-chief for Data Intelligence published by MIT Press and Chinese Academy of Sciences, and serves as the editorial board member for several top journals in Information Science and Semantic Web. She is the co-founder of Data2Discovery company advancing cutting edge AI technologies in drug discovery and healthcare. Her current research interests include data-driven science of science, AI in healthcare, Semantic Web, knowledge graph, data science, scholarly communication, and the application of Web technologies.
Matthew Lease received his Ph.D. in Computer Science from Brown University and his B.Sc. in Computer Science from the University of Washington. He has received early career awards from the NSF, IMLS, and DARPA. Recent honors include Best Student Paper at the 2019 European Conference for Information Retrieval (ECIR) and Best Paper at the 2016 Association for the Advancement of Artificial Intelligence (AAAI) Human Computation and Crowdsourcing conference (HCOMP). Lease is currently helping lead Good Systems, an eight-year, university-wide Grand Challenge Initiative at UT Austin to design AI technologies that maximally benefit society.
Edison Thomaz is an Assistant Professor in the Electrical and Computer Engineering department of The University of Texas at Austin, with an appointment in the School of Information. He holds a Ph.D. in Human-Centered Computing from the School of Interactive Computing of the Georgia Institute of Technology, and a S.M. in Media Arts and Sciences from the MIT Media Lab. Prior to his academic appointments, Dr. Thomaz held industry positions at leading technology companies such as Microsoft and France Telecom.
Edison's research is in Activiomics, a new discipline that applies computational methods towards sensing, recognizing and modeling the entire span of people's everyday life activities and context, from individual gestures to life patterns and routines. By enabling the measurement of human behaviors at a finely-grained level of detail and pairing them with bio-physiological signals obtained with environmental and wearable sensors, his work aims to provide a new technical foundation for applications and discoveries at the frontier of human health and behavior such as gene-environment interactions, predictive health from behavior markers, and personal health informatics.