Research | ML | Benchmarking
Research built for real engineering use.
I run reproducible experiments in ML and optimization, then translate findings into practical systems.
Reinforcement Learning Music ML Algorithm Analysis
Method
Reproducible runs
Metrics
Tracked and comparable
Validation
Baseline + ablations
Output
Code + report ready
Each study favors repeatability over one-off results.
Focus Areas
Music ML
SVM key classification with chroma, MFCC, and spectral features.
SVMFeature engineeringAudio analysis
Deep RL
DQN and PPO on LunarLander with replay and curriculum strategies.
DQNPPOReward shaping
Optimization
Hyperparameter tuning and benchmark-driven model comparison.
TuningBenchmarkingComparative analysis