I am a PhD candidate at Colorado State University in the Artificial Intelligence Program. My research interests include:
- Operations Research
- Combinatorial Optimization
- Evolutionary Computation
- Machine Learning
- High Performance Computing
Currently, I am working with the SCHED group on combinatorial optimization problems such as the Traveling Salesman Problem and Satisfiability. State-of-the art heuristic solvers for these problems are quite good at solving uniform randomly generated problems. However, many of these same heuristics suffer from a significant decrease in performance when presented with real-world problems such as those arising in industrial applications. My research focuses on understanding the difficulty of these problems and how we can design better algorithms to solve them.
Previously, I have worked with the CSU Brain-Computer Interface Labratory on the design of a P300-speller, a user-interface that allows subjects to interact with a computer using only their thoughts. Many previous P300-spellers relied on the use of expensive hardware, such as the Biosemi EEG. These costs could be prohibitive to many potential users. One goal of my research was to achieve a low-cost alternative without sacrificing performance. The result of our work is a P300 speller that can achieve similar results as $60,000+ units for approximately $6,000.
I have also worked with members of the CSU High Performance Computing Group on a parallel version of the Smith-Waterman algorithm for CUDA-enabled GPGPUs. Previous parallelizations of this algorithm for GPUs were inefficient due to excessive global memory usage. By adapting the algorithm to a tiled wavefront strategy, we were able to significantly reduce the number of global memory read and writes, thus improving the overall performance.