Title: AI at UC Irvine
Course: CS171: Introduction to Artificial Intelligence

Groups & Centers at UC Irvine

- UCI / ICS AI research group
- Center for Machine Learning and Intelligent Systems
- Data Science Initiative

AI faculty & suggested papers

These are some of the AI-related faculty here at UC Irvine, along with one or two papers (and comments about the paper or its authors) that can serve as an introduction to some of their work.

- Pierre Baldi
  - Baldi, et al., 2014, “Deep Learning in High-Energy Physics: Improving the Search for Exotic Particles”
  - Baldi & Sadowsk, 2016, “A Theory of Local Learning, the Learning Channel, and the Optimality of Backpropagation”
- Rina Dechter
  - Dechter & Pearl, 1985, “Generalized Best-First Search Strategies and the Optimality of A*”
  - Dechter, et al., 1991, “Temporal Constraint Networks”
- Charless Fowlkes
  - Yang, et al., 2011, “Layered Object Models for Image Segmentation”
  - Hallman & Fowlkes, 2015, “Oriented Edge Forests for Boundary Detection” (Sam Hallman was a UCI undergraduate when he presented this paper as a talk at the Computer Vision and Pattern Recognition conference. Sam completed his PhD with me in 2015. He is now working at Amazon. --- From Charless Fowlkes)
  - Diaz, et al., 2016, “Lifting GIS Maps into Strong Geometric Context” (This paper uses a dataset of photos taken on the UCI campus and a 3D model of our corner of campus. --- From Charless Fowlkes)
  - Ren, et al., 2008, “Learning Probabilistic Models for Contour Completion in Natural Images” (This is an older paper from my PhD but has a very nice connection to the discussion  of Huffman & Clowes line labeling as constraint satisfaction in Russell & Norvig, AIMA, Chap 24, 2nd ed. --- From Charless Fowlkes)
- Alexander Ihler
  - Sudderth, et al., 2010, “Nonparametric Belief Propagation”
  - Liu & Ihler, 2012, “Belief Propagation for Structured Decision Making”
- Richard Lathrop
  - Lathrop, et al., 1999, “Knowledge-based Avoidance of Drug-Resistant HIV Mutants” (Miriam Raphael and Sophia Deeds-Rubin were ICS undergraduate women when they shared the cover illustration of AI Magazine and an award from the Innovative Applications of Artificial Intelligence conference for this paper. --- From Richard Lathrop)
  - Danziger, et al., 2009, “Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning”
  - Wassman, et al., 2013, “Computational Identification of a Transiently Open L1/S3 Pocket for Reactivation of Mutant p53”
- Eric Mjolsness
  - Mjolsness, 2013, “Quantitative Symbolic Process Models: How a Fair Fraction of Intelligence Could be Abstracted in AI Research”
  - Mjolsness & Cunha, 2012, “Topological object types for morphodynamic modeling languages”
  - Mjolsness, 2010, “Towards Measurable Types for Dynamical Process Modeling Languages”
- Hernando Ombao
  - Wang, et al., 2016, “Modeling Effective Connectivity in High-Dimensional Cortical Source Signals”
- Donald Patterson
  - Patterson, et al., 2003, “Inferring High-Level Behavior from Low-Level Sensors” (This paper won a Ten-Year Impact Award. --- From Donald Patterson)
  - Patterson, et al., 2004, “Opportunity Knocks: A System to Provide Cognitive Assistance with Transportation Services”
- Babak Shahbaba
  - Shahbaba & Neal, 2009, “Nonlinear Models Using Dirichlet Process Mixtures”
  - Lan, et al., 2014, “Wormhole Hamiltonian Monte Carlo”
- Sameer Singh
  - Ribeiro, et al., 2016, “ ‘Why Should I Trust You?’ Explaining the Predictions of Any Classifier” (This paper won the Audience Appreciation Award at the ACM SIG Conference on Knowledge Discovery and Data Mining (KDD); see easy to read article by us on O'Reilly; view Summary Video. --- From Sameer Singh)
  - Gupta & Singh, 2016, “Collective Factorization for Relational Data: An Evaluation on the Yelp Datasets” (Nitish Gupta was an undergraduate who shared in the publication of this work, which was a grand prize winner in Round 4 of the Yelp dataset challenge. --- From Sameer Singh)
- Padraic Smyth
  - Gaffney, et al., 2007, “Probabilistic clustering of extratropical cyclones using regression mixture models” (The key student involved in this work started out as an ICS undergraduate. Scott Gaffney took ICS 171 and 175, got interested in AI, started to work in my group, decided to stay in ICS for his PhD, did a terrific job in writing a thesis on curve-clustering and working with collaborators in climate science to apply it to important scientific problems, and is now one of the leaders of Yahoo! Labs reporting directly to the CEO there. Scott grew up locally in Orange County and is someone I like to point as a great success story for ICS. --- From Padhraic Smyth)
- Hal Stern
  - Heins & Stern, 2014, “A Statistical Model for Event Sequence Data”
- Xiaohui Xie
  - Kim & Xie, 2014, “Handwritten Hangul recognition using deep convolutional neural networks”