James Caverlee
- Professor
- Lynn ’84 and Bill Crane ’83 Faculty Fellow
- Office: HRBB 425H
- Phone: 979-845-0537
- Email: caverlee@cse.tamu.edu
Richard Furuta
- Professor
- Undergraduate Advisor
- Office: HRBB 402C | Advising: EABA 100C
- Phone: 979-845-4087
- Email: ugrad-advisor@cse.tamu.edu
Xia (Ben) Hu
- Associate Professor
- Lynn ’84 and Bill Crane ’83 Faculty Fellow
- Office: HRBB 502C
- Phone: 979-845-8873
- Email: hu@cse.tamu.edu
Frank M. Shipman
- Professor
- Associate Department Head
- Office: HRBB 404
- Phone: 979-862-3216
- Email: shipman@cse.tamu.edu
Courtesy Appointments
Nick Duffield
- Professor
- Royce E. Wisenbaker Professor I
- Director, Texas A&M Institute of Data Science
- Office: WEB 332D
- Phone: 979-845-7328
- Email: duffieldng@tamu.edu
Daniel W. Goldberg
- Associate Professor
- Office: OMB 707
- Phone: 979-845-6395
- Email: daniel.goldberg@geog.tamu.edu
Courses Offered
CSCE 603. Database Systems and Applications. Credits 3. 3 Lecture HoursIntroduction to the concepts and design methodologies of database systems for non-computer science majors; emphasis on E. F. Codd's relational model with hands-on design application. No credit will be given for both CSCE 310 and CSCE 603. Prerequisite: CSCE 601; graduate classification. |
CSCE 608. Database Systems. Credits 3. 3 Lecture HoursDatabase modeling techniques; expressiveness in query languages including knowledge representation; manipulation languages data models; physical data organization; relational database design theory; query processing; transaction management and recovery; distributed data management. |
CSCE 666. Pattern Analysis. Credits 3. 3 Lecture HoursIntroduction to methods for the analysis, classification and clustering of high dimensional data in Computer Science applications. Course contents include density and parameter estimation, linear feature extraction, feature subset selection, clustering, Bayesian and geometric classifiers, non-linear dimensionality reduction methods from statistical learning theory and spectral graph theory, Hidden Markov models, and ensemble learning. Prerequisite: MATH 222, MATH 411 (or equivalent) and graduate classification. |
CSCE 670. Information Storage and Retrieval. Credits 3. 3 Lecture HoursRepresentation, storage, and access to very large multimedia document collections; fundamental data structures and algorithms of information storage and retrieval systems; techniques to design and evaluate complete retrieval systems, including cover of algorithms for indexing, compressing, and querying very large collections. Prerequisite: CSCE 310 or CSCE 603 or approval of instructor; graduate classification. |
CSCE 675. Digital Libraries. Credits 3. 3 Lecture HoursSurveys current research and practice in Digital Libraries, which seek to provide intellectual access to large-scale, distributed digital information repositories; current readings from the research literature which covers the breadth of this interdisciplinary area of study. Prerequisite: Graduate classification in computer science. |