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Frongillo, Rafael M Assistant Professor and Roubos Engineering Endowed Faculty Fellow

Positions

Research

research overview

  • Dr. Frongillo's research interests lie in the interface of theoretical machine learning and economics (encompassing topics such as information elicitation, crowdsourcing, learning, and markets), as well as dynamical systems. Below are broad questions describing his current focus. What is the expressive power of empirical risk minimization? How can we design better incentives for crowdsourcing? Can we understand markets in terms of optimization algorithms? How can we prove rigorous results in dynamics using computational topology?

keywords

  • algorithmic economics, theoretical machine learning, information elicitation, dynamical systems

Publications

selected publications

Teaching

courses taught

  • APPM 8500 - Statistics, Optimization and Machine Learning Seminar
    Primary Instructor - Fall 2018 / Fall 2019
    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 - Fall 2018 / Fall 2019
    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 - Spring 2018 / Fall 2018
    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 5802 - Data Science Team Companion Course
    Primary Instructor - Spring 2018 / Fall 2018
    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 7000 - Current Topics in Computer Science
    Primary Instructor - Fall 2018 / Fall 2019
    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.

Background

International Activities

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