Machine Learning at the University of Arizona
Machine learning at the U of Arizona consists of many labs from Computer Science (CS), School of Information (Info), Electrical and Computer Engineering (ECE), Applied Math (AMath), Statistics GIDP (Stat), Management Information Systems (MIS), and Cognitive Science GIDP (CogSci).
We indicate the joint affiliation next to each name, which means the ability to advise students from the other department.
- Kobus Barnard [Stat, ECE, AMath]: computer vision, machine learning, scientific applications, multimedia data.
- Kwang-Sung Jun [Stat, AMath]: interactive machine learning, multi-armed bandits, online learning.
- Jason Pacheco [Stat, AMath]: statistical machine learning, probabilistic graphical models, approximate inference algorithms, and information-theoretic decision making.
- Carlos Scheidegger: data visualization, data analysis, computer graphics, fairness in machine learning.
- Mihai Surdeanu: natural language processing, applied machine learning, artificial intelligence.
- Chicheng Zhang [Stat, AMath]: interactive machine learning, learning theory, contextual bandits, active learning.
- Steven Bethard: natural language processing, machine learning, information extraction methods.
- Peter Jansen: natural language processing, explanation-centered inference, inference over knowledge graphs.
- Clayton Morrison [CS, Stat]: machine learning, artificial intelligence, causal inference, knowledge representation, and automated planning
- Gregory Ditzler: data mining and applied machine learning.
- Ming Li [CS]: wireless and cyber security, wireless network modeling and optimization, wireless and spectrum security, privacy-preserving data analytics, and cyber-physical system security.
- Michael (Misha) Chertkov [CS]: energy systems, graphical models, stat hydro, non-eq. stat mech, fiber optics, stochastic control.
- Junming Yin [Stat]: statistical machine learning, probabilistic modeling and inference, nonparametric and high-dimensional statistics
- Helen Zhang [AMath]: nonparametrics, high dimensiaonal data analysis, feature selection, sparse methods, statisical machine learning, biomedical data analysis.