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 limit order book data with sequence models and reinforcement learning for quantitative trading. I’m a member of Oxford Engineering Department and British Open-ended Learning and Discovery Lab (BOLD), my supervisors are Stefan Zohren and Jakob Foerster.

My interests center on machine learning for high-frequency trading: market making, execution, and short-term prediction. More broadly, I work on systematic trading strategies and regime-switching models.

Previously at Samsung R&D, I built ML for sensor time-series (signal processing, dependency structure modeling, end-to-end feature delivery). In academic research (Skoltech–Sberbank), I worked on uncertainty quantification, anomaly detection, and deep learning. Earlier, at a top-ranked regional bank, I built ATM withdrawal prediction and a discrete-optimization system for cash logistics.