1. AI @ DePaul Peter Wiemer-Hastings peterwh@cs.depaul.edu 312-362-5736

2. Faculty Jacek Brzezinski Robin Burke Clark Elliott Steven Lytinen Craig Miller Bamshad Mobasher Ashley Morris Joseph Phillips Daniela Raicu Noriko Tomuro Peter Wiemer- Hastings

3. Research Areas/Projects (1) Intelligent Information Retrieval / Filtering –Web navigation (Miller) –WebACE (Mobasher) –ARCH (Mobasher, Lytinen, Miller) –FAQFinder (Tomuro, Lytinen) –Recommender systems (Burke) Intelligent Tutoring Systems, Cognitive Modeling –Miller –Wiemer-Hastings –Elliott

4. Research Areas/Projects (2) Natural Language Processing –Unification grammar and parsing (Tomuro, Lytinen) –WordNet (Tomuro) –Latent Semantic Analysis (Wiemer- Hastings) Fuzzy GIS –Morris AI in Games –Brzezinski

5. Robin Burke Recommender systems –Knowledge-based recommendation –Hybrid recommender systems –Interactive recommendation Applications in –Electronic Commerce: Intelligent product catalogs –Digital Libraries: Intelligent multi-dimensional browsing

6. Clark Elliott Emotion and Speech –Natural Language Generation –Natural Language Understanding

7. Steve Lytinen FAQFinder –with Noriko Tomuro –A natural language-based browser of Frequently Asked Questions (FAQ) files A Unification-based Natural Language Parser –with Noriko Tomuro –Efficient parsing algorithms for a very expressive grammar formalism called Unification Grammar ARCH –with Mobasher, Miller, Burke and Sieg –Document retrieval using concept hierearchy

8. Craig Miller User modeling to evaluate interfaces –in collaboration with NASA Ames research labs –Modeling of navigation patterns/behavior of web users –Evaluation of web site usability from a user's perspective Cognitive models of human learning –A rule-based category-learning system that produces behavior consistent with human behavior –Computational model of students interacting with an educational program (electrostatic physics)

9. Bamshad Mobasher Research Interests –Data mining and knowledge discovery on the Web (Web Mining) –Intelligent agents for information retrieval / filtering –Agents for electronic commerce and automated contracting Projects –Automatic Web Personalization based on Web Usage Mining –MAGNET: Multi-agent distributed environment for automated contracting and supply-chain management –WebACE: a client-side Web agent for document retrieval and categorization

10. Ashley Morris Using Fuzziness in Geographic Information Systems (GIS) –Able to better store and represent spatial objects Fuzziness in data modeling Fuzzy learning systems http://morris2k.cti.depaul.edu/gis/FOOSBA LL2.htmlhttp://morris2k.cti.depaul.edu/gis/FOOSBA LL2.html

11. Joseph Phillips Computational Scientific Discovery –The field borrows from Philosophy of Science, Machine Learning and Knowledge Discovery in Databases (KDD). –Representing scientific knowledge –Automating scientific reasoning –Updating scientific models given data in databases –Visualizing models –Developing model building and preferencing criteria, and defining heuristic functions over scientific models.

12. Daniela Raicu Content-based image retrieval Computer vision Data mining and knowledge discovery Machine learning Pattern recognition

13. Noriko Tomuro A Unification-based Natural Language Parser –with Steve Lytinen –Efficient parsing algorithms for a very expressive grammar formalism called Unification Grammar Computational Semantic Lexicon –WordNet as the broad-coverage lexical resource FAQFinder –with Steve Lytinen –A natural language-based browser of Frequently Asked Questions (FAQ) files

14. Peter Wiemer-Hastings Research Interests –Natural Language Understanding –Cognitive Modeling –Artificial Intelligence in Education Projects (more info at http://reed.cs.depaul.edu/peterwh) –SLSA: Hybrid symbolic and vector-based natural language understanding –StoryStation: Helps children write better by giving feedback from multiple agents –RMT: Research Methods Tutor, currently used by DePaul Psychology students

15. Classes (CSC) 3/457 (F) Expert Systems –Learn how to make a rule-based system, and some theory 3/458 (Sp) Symbolic Programming –Learn Lisp and Prolog, basic AI langs 3/480 (W) Foundations of AI –Search, logic, inference, agents 578 (F) Machine Learning –ML and Neural Networks 587 (W) Cognitive Science –Computer models of cognitive tasks

16. Other Classes DS/IS 575 (W) Intelligent Information Retrieval –How to pull important info out of the web or some other large collection CSC 594 (Spr) Topics in AI –This term: Topics in Knowledge Management (Burke) ITS 427 (Spr) Information Processing Models of Learning –Learn about how people learn ITS 580 (?) Artificial Intelligence in Learning Environments –Intelligence in Education

17. Questions?