About

Oxford PhD researcher in Generative AI for market microstructure. I study the realism of GenAI-generated order flow through the lens of market impact, combining tokenized Limit-Order-Book message streams with sequence models (SSMs, transformers, diffusion), GPU-accelerated JAX simulations, and reinforcement learning for quantitative trading.

Member of Oxford Engineering Department, supervised by Stefan Zohren and Jakob Foerster.

Research Interests

  • State-of-the-art machine learning methods for trading strategies
  • Market making and execution
  • Predictive analytics
  • Causal inference
  • Graph neural networks for time-series
  • Regime-switching models
  • Online portfolio selection

Experience

Samsung R&D - Built ML for sensor time-series (signal processing, dependency structure modeling, end-to-end feature delivery)

Skoltech-Sberbank - Uncertainty quantification, anomaly detection, and deep learning research

Regional Bank - ATM withdrawal prediction and discrete-optimization system for cash logistics

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