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Larremore, Daniel Benjamin Assistant Professor

Positions

Research Areas research areas

Research

research overview

  • Dr. Larremore's research focuses on developing methods of networks, dynamical systems, and statistical inference, to solve problems in social and biological systems. In particular, his work focuses on (1) Networks and theory, (2) Malaria's antigenic variation and evolution, and (3) Academic labor market dynamics.

keywords

  • Network science, dynamical systems, statistical models and inference, computational social science

Publications

selected publications

Teaching

courses taught

  • CSCI 3022 - Introduction to Data Science with Probability and Statistics
    Primary Instructor - Spring 2018 / Fall 2018 / Fall 2019
    Introduces students to the tools methods and theory behind extracting insights from data. Covers algorithms of cleaning and munging data, probability theory and common distributions, statistical simulation, drawing inferences from data, and basic statistical modeling. Same as CSPB 3022.
  • CSCI 4802 - Data Science Team Companion Course
    Primary Instructor - Spring 2019
    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 5352 - Network Analysis and Modeling
    Primary Instructor - Fall 2018 / Fall 2019
    Examines modern techniques for analyzing and modeling the structure and dynamics of complex networks. Focuses on statistical algorithms and methods, and emphasizes model interpretability and understanding the processes that generate real data. Applications are drawn from computational biology and computational social science. No biological or social science training is required. Recommended prerequisites: CSCI 3104 and APPM 3570.
  • CSCI 5802 - Data Science Team Companion Course
    Primary Instructor - Spring 2019
    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.

Background

International Activities

Other Profiles

Github

  • dblarremore