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AI @ DePaul Peter Wiemer-Hastings [email protected] 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 WiemerHastings 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 (WiemerHastings) • 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/FOOSBA LL2.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?