Summer 2024 projects
Topic: Speedier Simulations
Advisers: Fred Hickernell
Description: Monte Carlo methods are used to solve problems involving uncertainty, such as financial risk and geophysical problems whose parameters are not known precisely. The speedy simulations research group develops and implements algorithms in an open source Python package, called QMCPy that speeds up Monte Carlo simulations. Students will contribute to QMCPy by exploring new use cases, by implementing new algorithms, and/or by improving performance through parallel processing. By joining the speedy simulations research group, students will experience teamwork, learn to identify and solve research problems, follow good practices in technical software development, and hone their communication skills. A background in statistics and Python (or other language) programming will be an advantage.
Topic: Baseball Game Simulation
This is a collaborative project with the Chicago White Sox.
Advisers: Matt Koeing and Yuhan Ding
Descriptions: Take in two rosters, starting lineups, statistical data, and the rules of a baseball game to create a game simulator to predict the final score and statistics of a game. This simulator will simulate each at bat of a game using the present situation, players, and rules to predict the outcome. This tool could be used to test out the performance of different lineups, predict a season, or to predict a certain outcome.
Topic: Machine Learning for Traffic Accident Prediction
Advisers: Huiling Liao
Descriptions: Controlling traffic accidents is an important public safety challenge, therefore, accident prediction has been a topic of much research. With a large-scale but sparse publicly available dataset including a variety of data attributes such as traffic events and weather data, we try to tackle the traffic accident prediction through modeling the nonlinear evolution of spatio-temporal patterns. In this study, students will work together, starting from identifying research problems to coming up with a solution, to explore different spatio-temporal models and also learn to model the patterns and predict the occurrence and severity of accidents with the proper machine learning tools.
Summer 2023
