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Morrison, Rebecca Elizabeth Assistant Professor

Positions

Research Areas research areas

Research

research overview

  • Rebecca Morrison’s research focuses on probabilistic models of different types, including probabilistic graphical models to interpret and simplify large data sets, and Bayesian inference and reasoning to calibrate and validate predictive computational models. These topics are motivated by the goal to make reliable predictions of physical systems using computational models, physical constraints, and available data sets. Rebecca is also interested in probabilistic dynamical systems and combinatorial game theory, and has worked on a number of applications at the intersection of physics-based and data-driven models, including combustion, epidemiology, satellites and reentry vehicles, and space weather.

keywords

  • probabilistic graphical models, calibration and validation, predictive modeling

Publications

selected publications

Teaching

courses taught

  • CSCI 2820 - Linear Algebra with Computer Science Applications
    Primary Instructor - Spring 2019 / Fall 2019
    Introduces the fundamentals of linear algebra in the context of computer science applications. Includes vector spaces, matrices, linear systems, and eigenvalues. Includes the basics of floating point computation and numerical linear algebra.
  • CSCI 4802 - Data Science Team Companion Course
    Primary Instructor - Fall 2020 / Spring 2021
    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 - Fall 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 5802 - Data Science Team Companion Course
    Primary Instructor - Fall 2020 / Spring 2021
    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 5822 - Probabilistic Models of Human and Machine Learning
    Primary Instructor - Spring 2021
    Introduces a set of modeling techniques that have become a mainstay of modern artificial intelligence, cognitive science and machine learning research. These models provide essential tools for interpreting the statistical structure of large data sets and for explaining how intelligent agents analyze the vast amount of experience that accumulates through interactions with an unfamiliar environment. Recommended prerequisite: undergraduate course in probability and statistics.
  • CSCI 7000 - Current Topics in Computer Science
    Primary Instructor - Fall 2018 / Spring 2020 / Fall 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.

Background

International Activities

global connections related to teaching and scholarly work (in recent years)

Other Profiles