Dr. Hoenigman's research focuses on modeling underlying dynamics of relevant social issues such as hunger and food waste. A novel component of her work is the application of predictive modeling and learning algorithms to domains where these algorithms are not frequently used. Dr. Hoenigman also coordinates with local relief agencies to affect change in her community by involving the community in her research, and using the research findings to inform community practices.
keywords
predictive modeling, applying computer science modeling to social problems such as hunger and drought, modeling food waste in boulder, understanding underlying dynamics of social problems
Teaching
courses taught
COEN 1830 - Special Topics
Primary Instructor
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Spring 2021 / Fall 2023 / Fall 2024
Explores topics of interest in engineering. Content varies by instructor and semester. May be repeated up to 9 total credit hours.
CSCI 1300 - Computer Science 1: Starting Computing
Primary Instructor
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Fall 2023 / Fall 2024
Teaches techniques for writing computer programs in higher level programming languages to solve problems of interest in a range of application domains. Appropriate for students with little to no experience in computing or programming. Degree credit not granted for this course and CSCI 1310 and CSCI 1320 and ECEN 1310. Same as CSPB 1300.
CSCI 2270 - Computer Science 2: Data Structures
Primary Instructor
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Spring 2018
Studies data abstractions (e.g., stacks, queues, lists, trees, graphs, heaps, hash tables, priority queues) and their representation techniques (e.g., linking, arrays). Introduces concepts used in algorithm design and analysis including criteria for selecting data structures to fit their applications. Knowledge OF C++ is highly recommended. Degree credit not granted for this course and CSCI 2275. Same as CSPB 2270.
CSCI 2275 - Programming and Data Structures
Primary Instructor
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Fall 2018 / Fall 2020
Combines the content in CSCI 1300 and CSCI 2270 and is intended for students with experience with at least one object oriented programming language. Assumes knowledge of programming constructs- data types, conditionals, loops and classes. Students must pass a programming competency exam administered by the computer science department to take this class. The course includes an expedited instruction in the C++ programming language and then primarily focuses on the content in CSCI 2270: data abstractions (e.g., stacks, queues, lists, trees, graphs, heaps, hash tables, priority queues) and their representation techniques (e.g., linking, arrays). Introduces concepts used in algorithm design and analysis including criteria for selecting data structures to fit their applications. Degree credit not granted for this course and CSCI 2270 or CSPB 2270.
CSCI 3104 - Algorithms
Primary Instructor
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Fall 2018 / Fall 2019
Covers the fundamentals of algorithms and various algorithmic strategies, including time and space complexity, sorting algorithms, recurrence relations, divide and conquer algorithms, greedy algorithms, dynamic programming, linear programming, graph algorithms, problems in P and NP, and approximation algorithms. Same as CSPB 3104.
CSCI 3202 - Introduction to Artificial Intelligence
Primary Instructor
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Spring 2022 / Fall 2022
Surveys artificial intelligence techniques of search, knowledge representation and reasoning, probabilistic inference, machine learning, and natural language. Knowledge of Python strongly recommended. Same as CSPB 3202.
CSCI 4831 - Special Topics in Algorithms
Primary Instructor
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Spring 2018 / Spring 2019
Covers topics of interest in computer science at the upper-division 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 2018 / Spring 2019 / Fall 2021 / Spring 2022
Provides an opportunity for senior computer science majors to conduct exploratory research in computer science as an option for the capstone requirement. Department enforced prerequisites: 35 hours of Computer Science coursework including Foundation courses, Upper-Division writing, CS GPA 3.0. Department consent required, contact academic advisor for details. May be repeated up to 8 total credit hours.
