Skip to content

Latest commit

 

History

History
107 lines (78 loc) · 8.42 KB

index.md

File metadata and controls

107 lines (78 loc) · 8.42 KB
Raw
layout title permalink
bootstrap
Machine Learning at the University of Arizona
/index.html

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), Cognitive Science GIDP (CogSci), Aerospace and Mechanical Engineering (AME).

Faculty

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.
  • Eduardo Blanco [AMath]: natural language processing, computational semantics, relation extraction and inference, negation and uncertainty
  • Lei Cao: Systems for AI and AI for System, data management and analytics tools with SAUL properties (Scalable, Automatic, Human-in-the-loop).
  • Kwang-Sung Jun [Stat, AMath]: interactive machine learning, learning theory, multi-armed bandits, online learning, confidence bounds
  • Mihai Surdeanu: natural language processing, applied machine learning, artificial intelligence.
  • Jason Pacheco [Stat, AMath]: statistical machine learning, probabilistic graphical models, approximate inference algorithms, and Bayesian methods
  • Ellen Riloff: natural language processing, bootstrapped learning, artificial intelligence
  • Chicheng Zhang [Stat, AMath]: interactive machine learning, learning theory, contextual bandits, active learning.

Info

  • Steven Bethard [CS, AMath, CogSci]: natural language processing, machine learning, information extraction methods.
  • Peter Jansen: natural language processing, explanation-centered inference, inference over knowledge graphs.
  • Xuan Lu: Human-centered Data Science; Human-AI Collaboration; Causal Inference; Future of Work; Emoji
  • Clayton Morrison [CS, Stat]: machine learning, artificial intelligence, causal inference, knowledge representation, and automated planning
  • Adarsh Pyarelal [CogSci]: artificial intelligence, machine learning, applied natural language processing and dialog understanding, and scientific applications.

ECE

  • Eung-Joo Lee: computer vision, medical image analysis, and deep 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

  • Gondy Leroy: natural language processing and machine learning with a practical and positive impact
  • Sudha Ram: machine learning, AI interpretability and explainable AI, large scale network science and data mining, health care analytics, big data analytics

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

AME

  • Jekan Thangavelautham: Machine Learning applied to concurrent design and control of space systems, Artificial Neural Networks applied to multirobot systems and swarms for planetary reconnaissance, extreme environment exploration, site clearing, open-pit mining, excavation, and construction tasks.

Links

Courses