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 Jun, Jason Pacheco, Chicheng 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 ml.arizona.edu to learn more about AI/ML at the University of Arizona.
