Currently.
- Volatility Surface Forecasting: ConvLSTM neural network forecasting S&P 500 implied volatility surfaces 30 days ahead, achieving 23% lower MSE than GARCH baseline
- Deep Learning for Intraday Volatility: Training GRU + HAR + GARCH models on SPY/QQQ realized variance and order book imbalance to predict intraday volatility
- ML Enhanced Risk Parity: Portfolio construction using hierarchical risk parity with machine learning forecasted covariance matrices
- NASA SUITS: Architecting local offline AI assistant for EVA spacesuits using BitNet 1.58-bit quantized models with multiagent system
Previously.
- Low-Latency Trading Systems: High performance trading infrastructure in Rust and C++ for real-time market data processing with sub-microsecond latency
- Graph Neural Networks for Order Books: Building GNN transformers pretrained on 1M+ contact graphs for financial market prediction
- iOS Security Research: Developing Pointer Authentication Code bypass techniques for iOS jailbreak development and kernel exploitation