AI @ DePaul

Peter Wiemer-Hastings

peterwh@cs.depaul.edu

312-362-5736

Faculty

• Jacek Brzezinski• Robin Burke• Clark Elliott• Steven Lytinen• Craig Miller• Bamshad Mobasher

• Ashley Morris • Joseph Phillips• Daniela Raicu• Noriko Tomuro• Peter Wiemer-

Hastings

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

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

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

                                                                                

Clark Elliott

• Emotion and Speech

– Natural Language Generation

– Natural Language Understanding                                                                                 

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

                                                                                

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)

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

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/FOOSBALL2.html

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.

 

Daniela Raicu

• Content-based image retrieval

• Computer vision

• Data mining and knowledge discovery

• Machine learning

• Pattern recognition

                                                                                

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

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

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

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

Questions?