Dr. Frongillo's research interests lie in the interface of theoretical machine learning and algorithmic economics, encompassing topics such as the design of loss functions in machine learning, and incentive-compatible mechanisms to elicit information from individuals or crowds. He is also active in dynamical systems research. Broad questions describing his current focus include the following: How can we systematically design loss functions for challenging machine learning problems like structured prediction? How can we design better incentives in machine learning and forecasting? What is the computational complexity of fundamental problems in dynamical systems?
keywords
algorithmic economics, theoretical machine learning, information elicitation, dynamical systems
Elicitation for Aggregation.
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence.
900-906.
2015
APPM 8500 - Statistics, Optimization and Machine Learning Seminar
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Fall 2018 / Fall 2019 / Fall 2021
Research-level seminar that explores the mathematical foundations of machine learning, in particular how statistics and optimization give rise to well-founded and efficient algorithms.
CSCI 3434 - Theory of Computation
Primary Instructor
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Fall 2018 / Fall 2019 / Fall 2020 / Fall 2021 / Fall 2022
Introduces the foundations of formal language theory, computability, and complexity. Shows relationship between automata and various classes of languages. Addresses the issue of which problems can be solved by computational means, and studies complexity of solutions.
CSCI 4802 - Data Science Team Companion Course
Primary Instructor
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Spring 2018 / Fall 2018 / Spring 2020 / Spring 2022 / Fall 2022
Gives students hands-on experience applying data science techniques and machine learning algorithms to real-world problems. Students work in small teams on internal challenges, many of which will be sponsored by local companies and organizations and will represent the university in larger teams for external challenges at the national and global level, such as those hosted by Kaggle. Students will be expected to participate in both internal and external challenges, attend meetings and present short presentations to the group when appropriate. Same as CSCI 5802.
CSCI 4830 - Special Topics in Computer Science
Primary Instructor
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Fall 2021 / Fall 2023
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 2019 / Spring 2020 / Fall 2020 / Spring 2021 / Fall 2021 / Spring 2022 / Fall 2022 / Spring 2023
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 5100 - Computer Science Colloquium
Primary Instructor
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Spring 2020
Learn about innovative research and teaching in computer science by attending talks and discussions by leading researchers and educators. Learn professional presentation skills and etiquette of participating in scientific research presentations. Students may attend during any term but they need to be signed up for this course during the term they wish to earn that credit.
CSCI 5802 - Data Science Team Companion Course
Primary Instructor
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Spring 2018 / Fall 2018 / Spring 2020 / Spring 2022 / Fall 2022
Gives students hands-on experience applying data science techniques and machine learning algorithms to real-world problems. Students work in small teams on internal challenges, many of which will be sponsored by local companies and organizations and will represent the university in larger teams for external challenges at the national and global level, such as those hosted by Kaggle. Students will be expected to participate in both internal and external challenges, attend meetings and present short presentations to the group when appropriate. Instructor consent required. Same as CSCI 4802.
CSCI 6100 - Computer Science Colloquium
Primary Instructor
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Spring 2020
Learn about innovative research and teaching in computer science by attending talks and discussions by leading researchers and educators. Learn professional presentation skills and etiquette of participating in scientific research presentations. Students may attend during any term but they need to be signed up for this course during the term they wish to earn that credit.
CSCI 6314 - Algorithmic Economics
Primary Instructor
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Spring 2022
This course will survey the frontier of algorithmic economics: the study of incentives and strategic behavior through a computational lens. It will show how microeconomic theory applies to the design of algorithms, and conversely, how algorithmic thinking applies to economics. Other topics may include game theory, mechanism design / auction theory, forecasting mechanisms, and voting / social choice theory. Recommended prerequisite: CSCI 5454.
CSCI 7000 - Current Topics in Computer Science
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Fall 2018 / Fall 2019 / Fall 2021 / Fall 2023
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.
