Current course:

Research Methods in Computer Science (CMPSCI 691DD, Spring 2007) — This course introduces graduate students to basic ideas about conducting a personal research program (see more extended description below).

Previous courses:

Artificial Intelligence (CMPSCI 383, Fall 2006) —This course explores key concepts of artificial intelligence, including knowledge representation, state-space search, and reasoning under uncertainty. We will examine how these concepts are applied in game playing and adaptive systems, two areas where AI concepts have proven to be extraordinarily successful. Prerequisites: CMPSCI 250, CMPSCI 287, and CMPSCI 311. 3 credits. (course website on WebCT)

Research Methods in Computer Science (CMPSCI 691DD, Fall 2005, Spring 2006) — This course introduces graduate students to basic ideas about conducting a personal research program. Students will learn basic methods for reading technical papers, selecting research topics, devising research questions, planning research, and synthesizing broader theories. The course will be structured around three activities: lectures on basic concepts of research strategy and techniques, discussions of technical papers, and preparation and review of written assignments. Significant reading, reviewing, and writing will be required, and students will be expected to participate actively in class discussions.

Knowledge Discovery and Data Mining (CMPSCI 591Y, Spring 2005) — Knowledge discovery is the process of discovering useful regularities in large and complex data sets. The field encompasses techniques from artificial intelligence (representation and search), statistics (inference), and databases (data storage and access). When integrated into useful systems, these techniques can help human analysts make sense of vast stores of digital information. This course presents the fundamental principles of the field, familiarizes students with the technical details of representative algorithms, and connects these concepts to applications in industry, science, and government, including fraud detection, marketing, scientific discovery, and web mining. The course assumes that students are familiar with basic concepts and algorithms from probability and statistics.

Until Fall 2004, I was a research assistant professor, I did not regularly teach required courses. Periodically, I chose to teach graduate seminars related to my research area:

Computational Social Network Analysis (CMPSCI 691T, Fall 2003, taught jointly with Andrew McCallum) — Social Network Analysis is the study of relationships among social entities, such as communications among members of a group, economic transactions between corporations, and treaties among nations. Interest is this field is blossoming as traditional practitioners in the social and behavioral sciences are being joined by researchers from statistics, graph theory, machine learning and data mining. In this course, we surveyed the field of Social Network Analysis from a computational point of view, with a focus on practical applications and open avenues for further research. We read and discussed 2-4 papers per week. Students wrote half-page responses to each paper and took turns summarizing the collected responses and presenting papers in class. There was a final project and paper. 3 credits.

Principles of Knowledge Discovery (CMPSCI 691M, Fall 2001) — This course introduced students to basic concepts in knowledge discovery, including knowledge representation, data representation, search, parameter estimation, and hypothesis testing. Students read and discussed 3-6 technical articles each week. Grading was based on short weekly response papers and class participation. 3 credits.

Information Mining (CMPSCI 691I, Fall 1999, taught jointly with James Allan) — Students in this seminar learned about information mining, an intersection of information retrieval, information extraction, and knowledge discovery (data mining). The field uses unstructured textual information in ways similar to IR, but leverages extraction technology to identify important objects and relationships within the text. Discovery tools such as link analysis and visualization enable extraction of "knowledge" from the text. The seminar required reading and presenting recent research papers in those and related fields. It also included a semester-long programming project, in which students built significant components of an information mining system. 3 credits.

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