UC Santa Barbara
Machine Learning Research Intern
Jun 2024 - Aug 2024 · Santa Barbara, CA
- Benchmarked CNNs, MLPs, and SNNs in PyTorch/SNNtorch to study accuracy-latency tradeoffs for audio classification.
- Built MFCC ML pipelines with model profiling and optimization, reducing inference latency by 37% on 10K+ audio samples.
- Evaluated model performance with accuracy, latency, and confusion-matrix analysis; presented findings at ICOPH Bangkok.
Summer research on the accuracy-latency tradeoff between CNNs, MLPs, and spiking neural networks for audio classification. Built MFCC pipelines and profiling tooling that cut inference latency 37% across 10K+ samples, and presented the findings at ICOPH Bangkok.
pytorchsnntorchaudioresearch