I'm interested in theory of how information is (algorithmically) gathered, aggregated, and used to make predictions or decisions. Much of my research situates AI, theoretical CS, or machine-learning problems in a societal context where information has privacy implications or is held by strategic agents who might misreport it.
keywords
algorithmic game theory, machine learning, theoretical computer science
APPM 4490 - Theory of Machine Learning
Primary Instructor
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Spring 2021
Presents the underlying theory behind machine learning in proofs-based format. Answers fundamental questions about what learning means and what can be learned via formal models of statistical learning theory. Analyzes some important classes of machine learning methods. Specific topics may include the PAC framework, VC-dimension and Rademacher complexity. Recommended prerequisite: CSCI 5622 (minimum grade C-).
APPM 5490 - Theory of Machine Learning
Primary Instructor
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Spring 2021
Presents the underlying theory behind machine learning in proofs-based format. Answers fundamental questions about what learning means and what can be learned via formal models of statistical learning theory. Analyzes some important classes of machine learning methods. Specific topics may include the PAC framework, VC-dimension and Rademacher complexity. Recommended prerequisites: APPM 4440 and CSCI 5622.
CSCI 4830 - Special Topics in Computer Science
Primary Instructor
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Spring 2021
Covers topics of interest in computer science at the senior undergraduate level. Content varies from semester to semester. Only 9 credit hours from CSCI 4830 and/or CSCI 4831 can count toward Computer Science BS or BA.
CSCI 4950 - Senior Thesis
Primary Instructor
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Fall 2020 / Spring 2021
Provides an opportunity for senior computer science majors to conduct exploratory research in computer science. Department enforced restriction, successful completion of a minimum of 36 credit hours of Computer Science coursework and approved WRTG. May be repeated up to 8 total credit hours.
CSCI 5454 - Design and Analysis of Algorithms
Primary Instructor
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Fall 2019 / Fall 2020
Techniques for algorithm design, analysis of correctness and efficiency; divide and conquer, dynamic programming, probabilistic methods, advanced data structures, graph algorithms, etc. Lower bounds, NP-completeness, intractability. Recommended prerequisite: CSCI 2270 or equivalent.
CSCI 7000 - Current Topics in Computer Science
Primary Instructor
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Spring 2020 / Spring 2021
Covers research topics of current interest in computer science that do not fall into a standard subarea. May be repeated up to 8 total credit hours.
