Other Computer Engineering Commons^™

The Engineering Admissions Partnership Program: A Navigation Strategy For Community College Students Seeking A Pathway Into Engineering, Marcia R. Laugerman, Mack C. Shelley, Steven K. Mickelson, Diane T. Rover

Diane Rover

This paper presents the evaluation of a program designed to improve transfer outcomes for community college students pursuing an engineering degree. The program, the Engineering Admissions Partnership Program (E-APP), was designed to improve the navigational success of community college transfer students through connections to the university. These connections include coordinated academic advising, peer-mentoring, campus visits, and online social and professional networks. The objective of the study is to determine the efficacy of the E-APP and its interventions, which will be measured by increased participation rates and increased university retention rates for E-APP participants. Outcome data for the students are analyzed …

Gridiron-Gurus Final Report: Fantasy Football Performance Prediction, Kyle Tanemura, Michael Li, Erica Dorn, Ryan Mckinney

Computer Science and Software Engineering

Gridiron Gurus is a desktop application that allows for the creation of custom AI profiles to help advise and compete against in a Fantasy Football setting. Our AI are capable of performing statistical prediction of players on both a season long and week to week basis giving them the ability to both draft and manage a fantasy football team throughout a season.

Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan

Masters Theses

Recent advances in cloud-based big-data technologies now makes data driven solutions feasible for increasing numbers of scientific computing applications. One such data driven solution approach is machine learning where patterns in large data sets are brought to the surface by finding complex mathematical relationships within the data. Nowcasting or short-term prediction of rainfall in a given region is an important problem in meteorology. In this thesis we explore the nowcasting problem through a data driven approach by formulating it as a machine learning problem.

State-of-the-art nowcasting systems today are based on numerical models which describe the physical processes leading to …