I work where quantitative finance meets machine learning: building research infrastructure that turns raw market data into tested, explainable signals, and the systems that trade on them.
- Alpha research tooling — automated factor discovery: symbolic expression search, GPU-accelerated evaluation with PyTorch and Triton, IC-based fitness and genetic evolution.
- Trading systems — event-driven execution, market-data pipelines, and backtesting.
- Deep learning — experimental network architectures, time-series models, and Kaggle competitions.
- LLM tooling — local inference and agent runtimes.
