Artificial Intelligence (Natural Language Processing, Machine Learning, Vision)

Research in artificial intelligence (AI), which includes machine learning (ML), computer vision (CV), and natural language processing (NLP), aims to develop and analyze computational approaches to automated reasoning in the presence of uncertainties. Such automated reasoning systems will ultimately enhance human decision making capabilities in complex tasks, through the ability to process large amounts of data efficiently. In some cases automated reasoning can even reliably replace human decision making entirely.

Within the Department of Computer Science our AI/ML research interests span multiple areas: Foundational methods in ML and probabilistic methods (Kwang-Sung JunJason PachecoChicheng Zhang); Natural language processing (Mihai Surdeanu / CLU lab); Inferring statistical models from data with applications in computer vision and scientific data (Kobus Barnard / IVILAB); Enhancing visual representations of complex data (Carlos Scheidegger).

Our group is highly collaborative, both within CS and across the university. The vibrant NSF-funded program for Transdisciplinary Research in Principles of Data Science (TRIPODS) fosters collaboration between Mathematics, Statistics and Computer Science. Large-scale collaborative projects are common, such as the recent DARPA-funded efforts for Theory of Mind-based Cognitive Architecture for Teams (ToMCAT) and the World Modelers project, which aims to build models of global-scale events.

We encourage you to visit to learn more about AI/ML at the University of Arizona.

Artificial Intelligence Faculty

Jason Pacheco

Assistant Professor
Office: GS 724
Interests: Statistical machine learning, probabilistic graphical models, approximate inference algorithms, and information-theoretic decision making
(Ph.D., Brown University, 2016)

PhD Students

Sushma Anand Akoju

PhD Student
Office: GS 710
Interests: Knowledge representation and reasoning, Neuro-symbolic approaches, Information extraction, Question & Answer System, Rule based systems, Automated theorem proving, ML-based Theorem Proving, Logic based systems, Bayesian learning, Uncertainity
Advisor: Dr. Mihai Surdeanu

Manujinda Wathugala

PhD Student
Office: N/A
Interests: Machine Learning, Computer Vision, Natural Language Processing and Distributed Systems
Advisor: Dr. Kobus Barnard

Yao Zhao

PhD Student
Office: GS 725
Interests: Machine Learning, Bandit Problem and Mathematical Optimization
Advisor: Dr. Kwang-Sung Jun