University of Wisconsin - Madison
B.S. with Computer Science, Mathematics, and Data Science Majors
At UW Madison, I developed as a programmer by learning from exceptional professors across disciplines such as algorithms, databases, optimization, and machine learning. Most of my academic programming experience is based in C, Java, and Python—Java forming the foundation for most introductory classes, Python for data science and machine learning courses, and C from sophmore year on for systems programming and algorithms.
Outside of my Computer Science studies, I spent considerable time at the chalkboards in math classrooms. I was constantly humbled by the complexities presented in my math classes, but I found this experience foundational to my approach in solving difficult problems.
Favorite Classes
Operating Systems
Algorithms
Matrix Methods in Machine Learning
Linear Optimization
Machine Organization
Databases
Real Analysis
Discrete Optimization
School Projects
Parallelized Radix Sort
Optimized parallel sort for large 4-byte key and 96-byte value records.
Implemented a multithreaded sorting algorithm that divides work among n threads, ensuring efficient memory use and minimal synchronization using memory-mapped records.
MinneMUDAC March Madness
Predicted NCAA brackets using data aggregation.
Developed Python-based statistical & deep nn models aggregating team performance metrics to simulate tournament outcomes. Despite realistic predictions (Wisconsin Badgers being eliminated rather quickly...), results highlighted the unpredictability of March Madness.
Distributed File Server
Built a file server using custom UDP implementation in C.
Designed a robust distributed file server with support for caching, file operations, and fault-tolerant communication using custom networking protocols. Optimized metadata handling and concurrency.