• Specialization: High Performance Computing

• Relevant coursework: Data Structures & Algorithms; Operating Systems; Computer Networks; Artificial Neural Networks & Deep Learning; Linear Algebra; Statistics for Engineers; Building User Interfaces

• Designed and implemented a multithreaded log-analysis tool (4 threads, 3.6× speedup) for 10,000+ distributed processing tasks.
• Extracted per-task CPU/memory utilization, I/O throughput, and runtime; designed an algorithm to capture idle periods in IBM DP logs.
• Built a dynamic profiling tool using strace, top, and custom logic to trace CPU, memory, and I/O usage of executing code in real time.
• Applied the tool to Synopsys Proteus, analyzed multiple test cases, and identified heavy I/O bottlenecks affecting runtime performance.

• Developed a Python script to parse and transform batches of internal job logs (~2,000+ lines per run); extracted error patterns and file-type data into a structured table for PowerBI ingestion.
• Created a KPI dashboard to visualize trends in system log errors (frequency by file-type and error code); used weekly by the team to analyze issues and spot recurring failures.
• Researched and evaluated data confidence platforms (e.g., Collibra, Snowflake) to handle terabyte-scale EHR and claims data; summarized findings to inform future pipeline structure.

• Investigated integration of 3D motion-capture APIs (Google MediaPipe Pose Landmarker) for STEM educational games with real-life body tracking.
• Debugged frontend JavaScript logic to enhance gameplay responsiveness and player feedback.

• Built a multithreaded European option pricing engine in C++ using OpenMP and CUDA, accelerating Monte Carlo simulations on real-time option chains (Tradier & FRED APIs).
• Achieved 5.1× CPU (8 threads) and 180× GPU (CUDA) over a serial baseline (100M paths/contract); accuracy validated against Black-Scholes.
• Extended the engine to support American options via Longstaff–Schwartz.

Code: github.com/brianshen03/MonteCarloOptionPricer

• Collaborated with a 3-person team to build a neural network with TensorFlow and scikit-learn to predict NCAA tournament outcomes using KenPom data; applied regularization and dropout to prevent overfitting.

Code: github.com/415yoni/ANN_FinalProject