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.

**CS**

- 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.

**Info**

- 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

**ECE**

- 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. - Ravi Tandon: information and coding theory, wireless communications, distributed cloud storage systems, machine learning, cyber-physical systems, and wireless security (cybersecurity) and privacy

**AMath**

- Michael (Misha) Chertkov [
**CS, Stat**]: energy systems, graphical models, stat hydro, non-eq. stat mech, fiber optics, stochastic control.

**MIS**

- Sudha Ram: machine learning, AI interpretability and explainable AI, large scale network science and data mining, health care analytics, big data analytics
- Muhammad Taqi Raza: networked and systems security, internet of things, cloud computing
- Junming Yin [
**Stat**]: statistical machine learning, probabilistic modeling and inference, nonparametric and high-dimensional statistics

**Stat**

- Helen Zhang [
**AMath**]: nonparametrics, high dimensiaonal data analysis, feature selection, sparse methods, statisical machine learning, biomedical data analysis.

**SIE**

- Roberto Furfaro: guidance and control of space systems, intelligent algorithms for space exploration, remote sensing of planetary bodies, model-based systems engineering applied to space missions

- UA TRIPODS
- Data Science Institute (Data7)
- Machine Learning for Artificial Intelligence (ML4AI) Lab
- Interdisciplinary Visual Intelligence (IVI) Lab
- Computational Language Understanding (CLU) Lab
- University of Arizona NLP Cluster

- CSC 580 Principles of Machine Learning by Carlos Scheidegger
- CSC 535 Probabilistic Graphical Models by Kobus Barnard
- CSC 665 Advanced Topics in Probabilistic Graphical Models by Jason Pacheco
- CSC 588 Machine Learning Theory by Chicheng Zhang
- CSC 665 Topics in Online Learning and Bandits by Kwang-Sung Jun
- ECE 523 Engineering Applications of Machine Learning and Data Analytics by Gregory Ditzler
- ISTA 410/INFO 510 Bayesian Modeling and Inference by Clayton Morrison
- ISTA 421/INFO 521 Introduction to Machine Learning by Clayton Morrison (Fall 2018 course)
- ISTA 457/INFO 557 Neural Networks by Steven Bethard
- MIS 601 Statistical Foundations of Machine Learning by Junming Yin
- MATH 574M Statistical Machine Learning by Helen Zhang