The University of St. Thomas

Graduate Programs in Software

Faculty & Staff

Faculty & Staff

Chih Lai Ph.D.
Chih Lai portrait photo with robot

Associate Professor

clai@stthomas.edu
Phone: (651) 962-5573
Fax: (651) 962-5543

Office Location: OSS 308

Courses taught in Spring 2014
SEIS 610-01
20052
Software Engineering 1745-2100 M OSS 333

3 Credit Hours

This is a survey course covering software engineering concepts, techniques, and methodologies. Topics covered include software engineering; software process and its difficulties; software life-cycle models; software metrics; project planning including cost estimation; design methodologies including structured design, and object-oriented design; software testing; and software maintenance. A brief review of data structures is included. Prerequisite: SEIS601 (waived for programming experience)

SEIS 610-02
21064
Software Engineering 0830-1500 S OSS 333

3 Credit Hours

This is a survey course covering software engineering concepts, techniques, and methodologies. Topics covered include software engineering; software process and its difficulties; software life-cycle models; software metrics; project planning including cost estimation; design methodologies including structured design, and object-oriented design; software testing; and software maintenance. A brief review of data structures is included. Prerequisite: SEIS601 (waived for programming experience)

SEIS 734-01
21062
Data Mining 1745-2100 W OSS 333

3 Credit Hours

Modern hardware can easily collect megabytes of data from various sources within a short period of time. This explosive growth in data has overwhelmed analysts for years. To overcome the problem of information overloading, data mining has emerged as a major frontier. Data mining is the automated extraction of regularities and patterns representing previously unknown knowledge implicitly stored in large databases, data warehouses, and other massive information repositories. In this course, we will discuss suitable data models, data preparation, and finally, different methods and algorithms to discover new knowledge from raw data. Major topics include: (1) Data warehousing and data cleansing, (2) Decision tree classification and customer behavior prediction, (3) Data clustering, (4) Association rule and market basket analysis, (5) Temporal sequence and spatial trend analysis, (6) Data mining tools and frameworks, (7)Inductive and analytical learning, and (8) Genetic algorithms and programming. This course is ideal for anyone who needs to learn how to analyze raw data to maximize strategic planning, marketing power, and bottom-line success. Prerequisite: SEIS630 and programming experience

Courses taught in Fall 2014
SEIS 610-02
40592
Software Engineering 0830-1500 S OSS 333

3 Credit Hours

This is a survey course covering software engineering concepts, techniques, and methodologies. Topics covered include software engineering; software process and its difficulties; software life-cycle models; software metrics; project planning including cost estimation; design methodologies including structured design, and object-oriented design; software testing; and software maintenance. A brief review of data structures is included. Prerequisite: SEIS601 (waived for programming experience)

SEIS 735-01
41986
Healthcare Informatics 1745-2100 M OSS 325

3 Credit Hours

Healthcare is broadly defined as any care (prevention, treatment) and service management related to the health of an individual. Providing high quality care that is safe and effective to patients is increasingly difficult due to rapid growth of medical knowledge and escalating cost of new treatments. This course will discuss topics in informatics that are used for acquisition, storage, retrieval, management, and integration of heterogeneous healthcare data. This course will examine (1) various medical terminology / data standards, (2) numeric data from CDC, FDA, and WHO, (3) formal text from National Library of Medicine, (4) free text and charts from sample patient records and clinic reports, (5) different types of medical images. (6) We will also discuss clinic decision support systems that utilize data / text mining approaches to discover patterns & derive new hypotheses from datasets. Prerequisite: SEIS 630

SEIS 772-01
42880
Multimedia Informatn Retrieval 1745-2100 T OSS 333

3 Credit Hours

Modern hardware can easily collect megabytes of multimedia (audio, images, and video) data in areas like security, medicine, entertainment, and engineering. Many multimedia information systems have been developed to efficiently manage and retrieve useful multimedia data based on its contents, not key words. To acheive content-based multimedia information retrieval, this course will focus on three major areas: First, we will study methods in analyzing multimedia data and extracting useful features (i.e. colors, shapes, motions, fractal dimensionality, etc.) from such data. Next, we will discuss special index structures that enable us to organize and retrieve multimedia data from databases that has content similar to multimedia data in query. Finally, we will also cover multimedia data mining techniques to dectect repeated or unusual patterns from huge multimedia data. Prerequisites: SEIS 630 and some programming experience

Interests:

Data Mining, Multimedia Information Retrieval, Healthcare Informatics, Real-Time Systems, and Image Processing.

Teaching Experience:

Dr. Lai is an associate professor with GPS. He has taught courses in Data Mining, Multimedia Information Retrieval, Healthcare Informatics, Real-Time Systems, and Software Engineering. He was also a visiting professor of the Informatics Department at Trier University of Applied Science in Germany in 2010. Dr. Lai also taught an Operating System course at the Computer Science Department of Oregon State University.

Research and Publications:

Dr. Lai’s research interests include Data Mining, Multimedia Databases and Mining, Real-Time Systems, Real-Time Data Mining. Dr. Lai has published many technical papers on international conferences sponsored by IEEE and other organizations. Dr. Lai is the 2004 University MAXI Grant recipent.

Industry Experience:

Before joining University of St. Thomas, Dr. Lai was a principal software engineer, working on a next generation aircraft collision avoidance system (ADS-B) approved by FAA. Dr. Lai received three U.S. patents and three European patents, all related to aircraft collision avoidance algorithms. Dr. Lai also works with Medtronic and has pending patents on monitoring and evaluating Parkinson patients. Other industry experience includes building a network gateway between IBM / Novell networks.

GPS Courses:

Software Engineering -- SEIS 610
Multimedia Databases -- SEIS 772
Real-Time Systems and Applications -- SEIS 740
Data Mining -- SEIS 734
Healthcare Informatics -- SEIS 735


Academic History:

Ph.D., Oregon State University, 1999
MSCS, Oregon State University, 1992
BA, Fu-Jen Catholic University (Taiwan), 1987