Main Office: 901 Cathedral of Learning
(412) 624-5755 (phone) (412) 624-6089 (fax)
http://www.isp.pitt.edu/index.html
E-mail: application@pogo.isp.pitt.edu
Director: Martha E. Pollack
Affiliated Faculty: Professors BUCHANAN (University Professor, Computer Science), CHANG (Computer Science), DALEY (Computer Science), FRIEDMAN (Medicine), KORFHAGE (Information Sciences), LESGOLD (Psychology), MAY (Business), VanLEHN (Computer Science); Associate Professors ASHLEY (Law), BANKS (Medicine), COOPER (Medicine), HIRTLE (Information Sciences), LOWE (Medicine), MOORE (Computer Science), MUNRO (Information Sciences), POLLACK (Computer Science); Assistant Professors DRUZDEL (Information Sciences), MAHLING (Information Sciences), WAGNER (Medicine); Instructor RAGHAVAN (Learning Research and Development Center); Lecturer OHLSSON (Learning Research and Development Center); Senior Research Professor RYMON (Adjunct)
The graduate program in Intelligent Systems serves as a center at the University of Pittsburgh for advanced education and research in artificial intelligence and related areas of cognitive science. Drawing on strengths from many sectors of the University, and on over thirty funded research projects, the program offers a strong, well-balanced core curriculum in the fundamentals of AI and many opportunities for advanced research and training. The scope of the program is broad, but offers concentration in specific areas such as automated diagnosis, knowledge representation, machine learning, intelligent tutoring, natural language processing and discourse, planning, case-based reasoning, and uncertain reasoning. There are especially strong connections to research groups in the Department of Computer Science, the Learning Research and Development Center, and the School of Medicine. The program also draws on associated faculty from other units, including the School of Law, the Joseph M. Katz Graduate School of Business, the Department of History and Philosophy of Science, the Department of Information Science and Telecommunications, the Department of Linguistics, the Department of Philosophy, and the Department of Psychology.
Students in the program may concentrate in such areas as:
- Formal studies of learning and reasoning, including default reasoning
and induction, reasoning with uncertain information, machine learning, planning,
machine diagnosis, human cognition, and applications of these theories.
- Experimental testing and validation of systems in applications such as diagnosis, inheritance reasoning, and planning.
- The nature of interactions between people and information processing
tools, including the interaction between a student and a machine or human
tutor, between a computer user and a computer, between a database user and
the database, between a professional and a diagnosis machine, and between
a programmer and a programming environment.
- Case-based reasoning, and applications of case-based reasoning, especially
in legal domains and in tutoring applications.
- Computational linguistics, and especially natural language generation
and discourse.
- Technologies related to the above foci, such as expert systems.
Admission to Graduate Studies
The program offers a Doctor of Philosophy degree. Guidance is also provided
for students who wish to pursue joint- or dual-degrees in the program and
another discipline. A Master of Science degree is available only to students
who are pursuing a doctoral program in Intelligent Systems or who are enrolled
simultaneously in another doctoral program elsewhere in the University. (The
only exception to this rule involves the Medical Informatics track.) Further
details about admission to such a joint- degree program are provided in the
program brochure.
Application Procedures
The program maintains updated information about program activities
and detailed application instructions. Applicants should obtain a current
copy of the program brochure before completing an application. Briefly, an
application consists of the standard Faculty of Arts and Sciences admission
forms, in addition to the following materials.
A concise statement of purpose, providing information on the following points:
- Objective in pursuing a PhD in Intelligent Systems.
- Theoretical background in relevant areas.
- Background in relevant tools and applications (list programming languages with which you are familiar, indicating level of familiarity).
- Relevant practical experience, including industrial or commercial experience.
Graduate Record Examinations are required of all applicants; applications are not complete until scores have been received. Applicants who are not native speakers of English must take the TOEFL examinations and submit scores. Applicants from abroad should take note of the special instructions that apply.
The program endeavors to provide financial support for all admitted students who require it. Support is currently available through a variety of sources, including externally supported research and training grants, University fellowships, and program funds. Further details are available in the program brochure.
Master’s Degree Requirements
The Intelligent
Systems Program offers a Doctor of Philosophy degree. A Master of Science
degree is available only to students who are pursuing a doctoral program in
Intelligent Systems, or simultaneously elsewhere in the University, or who
already hold the MD degree or an equivalent and are specifically admitted
to the Medical Informatics track.
Applicants with the BA degree who are not enrolled in graduate programs at the University of Pittsburgh:These applicants should follow the normal application procedures for the PhD degree. The admissions committee is looking for students with strong research potential. Recognizing that people can come to this field in many ways, the program invites applicants from a wide variety of educational settings and disciplinary backgrounds. The committee does look for evidence of advanced standing and outstanding performance in some of the core areas relevant to the subject, including theoretical and applied computer science, cognitive psychology and other areas of cognitive science, linguistics, and symbolic programming and software engineering.
Applicants for the Medical Informatics track: Such applicants must specifically indicate their interest in this track. For additional information, please contact Dr. Gregory Cooper at gfc@cbmi.upmc.edu.
Applicants for the MS degree only who are enrolled in other graduate programs at the University of Pittsburgh: Applicants who are already in residence at the University should contact the program directly before filling out an application. Application procedures are simplified in this case. To be admitted, such applicants must demonstrate, for at least one term, satisfactory progress in their primary department. Full admission to the ISP MS program requires the completion of the equivalent of a master's degree in the parent department and approval to continue for the doctorate in that department.
Applicants who wish to apply for simultaneous admission to the ISP MS and another PhD program:Such applicants will be taken into consideration. To be admitted provisionally to the master's program, a student must be admitted to an appropriate home department by that department's admissions process. The admission will be provisional; provisional status will be removed when the applicant demonstrates satisfactory progress, for at least one term, in the primary department.
To earn the Doctor of Philosophy degree in Intelligent Systems, a student must complete a program of study approved by an advisory committee of faculty. This program must include (a) the required courses shown below; (b) at least four advanced courses in the field of intelligent systems; (c) an MS-level project, approved by the faculty after an oral prospectus presentation, involving significant research, design, or development work and a written report; (d) successful completion of a comprehensive examination composed by an advisory committee; and (e) an acceptable dissertation. Successful completion of (c) satisfies the preliminary evaluation requirement of FAS. The advisory committee may waive requirements that have been satisfied through prior university-level study. The advisory committee normally consists of three faculty. At most one of these may be from outside the program, and no more than one can be non-tenure stream. At the time a dissertation committee has been formed, the advisory committee will be replaced by a larger dissertation committee, which will supervise and approve work on the dissertation.
Non-Medical Informatics Track Students
This document describes the steps required for normal progress through
the Intelligent Systems graduate degree programs. It has been approved by
the ISP Faculty, but the rules may be changed. Before using this document
for planning purposes, be sure it is the most recent applicable version.
| Prerequisites (or equivalents): | |
| CS 1510 | Design and Analysis of Algorithms |
| CS 1511 | Introduction to the Theory of Computation |
| CS 1571 | Introduction to Artificial Intelligence |
| CS 1573 | Artificial Intelligence Programming |
| PHIL 1500 | Symbolic Logic |
| Core (required for MS and PhD): | |
| ISSP 2050 | Intelligent Systems Research Seminar: The purpose of this seminar is to provide an orientation and overview of research activities in the program. This is normally taken during the first term of study. |
| ISSP 2160 | Foundations of Artificial Intelligence |
| ISSP 2170 | Machine Learning and Communication |
| ISSP 3712 | Knowledge Representation |
| Theory (A and B for the MS; A, B, and C for the PhD): | |
| (A) One course in applied or mathematical statistics; acceptable courses are: | |
| STAT 1631 | Intermediate Probability |
| STAT 1632 | Intermediate Mathematical Statistics |
(B) One course in theory of computation or algorithms; acceptable courses are: |
|
| CS 2110 | Theory of Computation |
| CS 2150 | Design and Analysis of Algorithms |
| ISSP 3520 | Theory of learning algorithms |
(C) One additional course; any of the courses listed above are acceptable. |
Equivalents of any these courses may be accepted by petition to the MS or PhD advisory committee.
Advanced Courses: Four advanced courses, for both MS and PhD students. For PhD students, these courses need not be taken before receiving the MS degree. In fact, one purpose of the requirement is to encourage advanced students to participate in some courses.
Medical Informatics Requirements
This document contains the requirements for obtaining a graduate degree in the Medical Informatics track of the Intelligent Systems Program (ISP/MI).
The following curriculum assumes that a student already has training in a health-care field; if this is not so, then the faculty will select a set of courses that teach the student basic medical knowledge, and the student may take these courses as electives. For a sample ISP/MI curriculum that might be taken by a doctoral student, see http://www.cbmi.upmc.edu.
Prerequisites: It is assumed that students enter the program with the following or equivalent knowledge. If not, then these courses or their equivalent should be taken, or the student should be prepared to learn this material as self study.
- Ability to program in C, C++, or Pascal.
- Basic computer science concepts, as for example the material covered in J.G. Brookshear, Computer Science: An Overview (Benjamin/Cummings, Redwood City, CA, 1991).
- CS 1541, or equivalent course about computer organization.
- CS 1573 Artificial Intelligence Programming.
| Required Course Work: The following courses are all required for the MS and PhD degrees. |
|
| ISSP 2015 | Introduction to medical informatics (offered in Fall Term) |
| ISSP 2050 | Intelligent systems Research seminar (required first term only) |
| ISSP 2060 | Evaluation methods for medical informatics (offered in Spring Term) |
| ISSP 2070 | Probabilistic methods for computer-based decision support (offered in Fall Term) |
| ISSP 2240 | Decision Analysis and Decision Support Systems (offered in Spring Term) |
Two out of any three of the following courses are required for an MS degree. All three are required for a PhD degree. |
|
| ISSP 2160 | Foundations of Artificial Intelligence (suggested time: first year) |
| ISSP 2170 | Machine Learning and Communication (suggested time: second year) |
| ISSP 3712 | Knowledge representation (suggested time: second year) |
Choose one for MS degree and PhD degree: |
|
| CS 1501 | Data Structures and Algorithms |
| CS 1510 | Design and Analysis of Algorithms (undergraduate-level) |
| CS 2150 | Design and Analysis of Algorithms (graduate-level) |
| CS 3150 | Advanced Topics in Design and Analysis of Algorithms |
Choose one for MS degree and take both for the PhD degree: |
|
| ISSP 2040 | Introduction to Clinical Multimedia and the Internet |
| ISSP 2045 | Clinical Information Systems |
The following is only required for PhD students: |
|
| TA ISSP 2015 or an equivalent course in consultation with ISSP faculty. | |
Additional Degree Requirements
Beyond the course work listed above, students must meet the academic requirements
that are described in Regulations Governing Graduate Study at the University
of Pittsburgh.
Master’s Students:
ISSP 2000 Research and Thesis for the Master’s Degree (used for MS project)
| Doctoral Students: PhD Comprehensive Examination |
|
| ISSP 3000 | Research and Dissertation for the PhD Degree |
Facilities
Computational Resources:Faculty and students
in the Intelligent Systems Program are key personnel in many well-established
research projects and centers. There are well-equipped laboratories associated
with research groups in Computer Science, Information Science, the Learning
Research and Development Center, and the Center for Biomedical Informatics.
The program maintains a laboratory for student use, which is equipped with
PCs and Unix workstations. Optical-fiber based Ethernet links program machines
to the campus backbone, PITTNET, which provides access to all University computing
facilities, the Pittsburgh Supercomputing Center, and the Internet.
Martha E. Pollack: Plan generation and plan recognition, Resource-limited reasoning, Computational models of rationality.
Kevin Ashley: Case-based Reasoning, Intelligent Tutoring, Legal Information Management and Retrieval.
Gordon Banks: Application of AI to Clinical Neurology, Expert Systems, Cognitive Modeling of Neurologic Diseases Using Computers.
Bruce Buchanan: Expert Systems, Hypothesis Formation, Medical Decision-making, Machine Learning.
Gregory F. Cooper: Application of Probability Theory and Decision Theory to Medical Informatics Problems, Bayesian Belief Networks, Causal Discovery, Data Mining.
Robert Daley: Theory of Learning Algorithms, Genetic Algorithms, Mathematical and Philosophical Foundations in Computer Science.
Marek J. Druzdzel: Decision-theoretic Methods in Intelligent Systems, Decision Support Systems for Strategic Business Planning.
Charles Friedman: Development of information resources, Methods for evaluation in medical informatics, Design, evaluation, and implementation of computer-based tools for medical education and assessment.
Stephen C. Hirtle: Spacial Cognition, Classification, Mathematical Psychology.
Robert R. Korfhage: Information Retrieval, Visual Languages, Graph Theory.
Alan Lesgold: Formal Modeling and Intelligent Computer-based Instruction of Complex Skills.
Henry J. Lowe: Integrated Multimedia Medical Record Systems Design and Implementation, Telemedicine Applications of the Electronic Medical Record, Clinical, Research and Educational Applications of the Internet.
Dirk Mahling: Use of goal-based task representations in multimedia workflow/project-management, Supporting problem-based learning for medical teams.
Jerrold May: Decision Support Systems in Management Science and in Engineering.
Johanna D. Moore: Natural Language Processing, Discourse, Intelligent Tutoring Systems, Multimedia Interfaces.
Paul Munro: Neural Networks, Neurobiological Models, Data Compression and Error Correction.
Kalyani Raghavan: Intelligent Tutoring Systems.
Ron Rymon: Machine Learning, Financial Modeling, Knowledge Discovery in Databases, Automated Medical Diagnosis.
Steven Small: Functional magnetic resonance imaging, Computational modeling, Aphasia.
Kurt Van Lehn: Intelligent Tutoring Systems, Cognitive Simulations, Machine Learning.
Michael Wagner: Construction of decision-theoretic reminder systems, Computer-assisted medical decision-making, Data accuracy in computer-based medical records.
|
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| 2000 | RESEARCH & THESIS FOR THE MASTER'S DEGREE | 1-9 CR. |
| 2010 | AIM SEMINAR | 3 CR. |
| 2015 | INTRODUCTION TO MEDICAL INFORMATICS | 3 CR. |
| Cross-listed with INFSCI 2901 | ||
| 2020 | TOPICS IN INTELLIGENT SYSTEMS | 3 CR. |
| 2040 | INTRODUCTION TO CLINICAL MULTIMEDIA AND THE INTERNET | 3 CR. |
| Cross-listed with INFSCI 2902 | ||
| 2045 | CLINICAL INFORMATION SYSTEMS | 3 CR. |
| Cross-listed with INFSCI 2903 | ||
| 2050 | INTELLIGENT SYSTEMS RESEARCH SEMINAR | 1 CR. |
| 2060 | EVALUATION METHODS FOR MEDICAL INFORMATICS | 3 CR. |
| Cross-listed with INFSCI 2904 | ||
| 2070 | PROBABILISTIC METHODS FOR COMPUTER-BASED DECISION SUPPORT | 3 CR. |
| Cross-listed with INFSCI 2905 | ||
| 2080 | ADVANCED MEDICAL INFORMATICS SEMINAR | 3 CR. |
| 2140 | INTRODUCTION TO PARALLEL DISTRIBUTED PROCESSING | 3 CR. |
| Cross-listed with INFSCI 2410 | ||
| 2160 | FOUNDATIONS OF ARTIFICIAL INTELLIGENCE | 3 CR. |
| Cross-listed with CS 2710 | ||
| 2170 | MACHINE LEARNING AND COMMUNICATION | 3 CR. |
| Cross-listed with CS 2750 | ||
| 2210 | ARTIFICIAL INTELLIGENCE | 3 CR. |
| Cross-listed with INFSCI 2044 | ||
| 2221 | HUMAN INFORMATION PROCESSING | 3 CR. |
| Cross-listed with INFSCI 2300 | ||
| 2230 | NATURAL LANGUAGE PROCESSING | 3 CR. |
| Cross-listed with INFSCI 2420 | ||
| 2240 | DECISION ANALYSIS AND DECISION SUPPORT SYSTEMS | 3 CR. |
| Cross-listed with INFSCI 2130 | ||
| 2250 | RESEARCH DESIGN | 3 CR. |
| Cross-listed with INFSCI 2100 | ||
| 2260 | VISUAL LANGUAGES | 3 CR. |
| Cross-listed with INFSCI 2650 | ||
| 2400 | INTELLIGENT SYSTEMS LABORATORY | 3 CR. |
| 2510 | SEMINAR ON ARTIFICIAL INTELLIGENCE AND LEGAL REASONING | 3 CR. |
| 2630 | PERSPECTIVES IN COGNITIVE SCIENCE | 2 CR. |
| Cross-listed with PSY 2410 | ||
| 2710 | MEDICAL EXPERT SYSTEMS SEMINAR | 3 CR. |
| 2805 | ARTIFICIAL INTELLIGENCE AND THE LOGIC OF DISCOVERY | 3 CR. |
| Cross-listed with HPS 2674 | ||
| 2820 | PRAGMATICS | 3 CR. |
| Cross-listed with LING 2681 | ||
| 2850 | COMPUTATIONAL LINGUISTICS | 3 CR. |
| 2871 | ART OF LOGIC AND COMPUTATION | 3 CR. |
| Cross-listed with LING 2871 | ||
| 2875 | LOGIC PROGRAMMING AND COMPUTATIONAL MORPHOLOGY | 3 CR. |
| Cross-listed with LING 2350 | ||
| 2900 | GRADUATE INTERNSHIP | 3 CR. |
| 2902 | DIRECTED STUDY | 3 CR. |
| 2990 | INDEPENDENT STUDY | 3 CR. |
| 3000 | RESEARCH AND DISSERTATION FOR THE PHD DEGREE | 1-9 CR. |
| 3120 | NATURAL LANGUAGE PROCESSING | 3 CR. |
| Cross-listed with CS 3730 | ||
| 3180 | VISUAL LANGUAGES AND VISUAL PROGRAMMING | 3 CR. |
| Cross-listed with CS 3650 | ||
| 3300 | ADVANCED TOPICS IN EXPERT SYSTEMS | 3 CR. |
| Cross-listed with CS 3720 | ||
| 3360 | STRUCTURE AND INTERPRETATION OF COMPUTER PROGRAMS | 3 CR. |
| Cross-listed with BAIM 3011 | ||
| 3370 | ARTIFICIAL INTELLIGENCE IN BUSINESS | 3 CR. |
| Cross-listed with BAIM 3012 | ||
| 3380 | KNOWLEDGE-BASED EXPERT SYSTEMS | 3 CR. |
| 3390 | ADVANCED TOPICS IN ARTIFICIAL INTELLIGENCE | 3 CR. |
| 3520 | THEORY OF LEARNING ALGORITHMS | 3 CR. |
| Cross-listed with CS 3120 | ||
| 3565 | ADVANCED TOPICS IN ARTIFICIAL INTELLIGENCE | 3 CR. |
| Cross-listed with CS 3710 | ||
| 3570 | ADVANCED TOPICS IN COMPUTATIONAL RATIONALITY | 3 CR. |
| Cross-listed with CS 3760 | ||
| 3610 | SEMINAR IN LEARNING AND INSTRUCTIONAL PROCESSES | 3 CR. |
| Cross-listed with PSY 3485 | ||
| 3712 | KNOWLEDGE REPRESENTATION | 3 CR. |
| Cross-listed with CS 3740 | ||
Additional information about the program is available on request. Questions
and requests regarding the program may be sent by E-mail to application@pogo.isp.pitt.edu.
Telephone calls should be directed to the program offices at (412) 624 -5755.
The program's postal address is: Intelligent Systems Program; University
of Pittsburgh; Pittsburgh, PA 15260.