I am a postdoctoral researcher in the Department of Computer Science at the University of Liverpool working with Professor Rahul Savani. I am interested in designing reinforcement learning (RL) agents which can learn how to act optimally to make markets. Since the agent's interaction with the financial market in turn causes the future dynamics to change, it requires a market model that is adaptive. One way of achieving this is to create a world model of the financial market which generates order flow according to the history of the limit order book up to that point.
A popular choice for the world model is to use an analytical model of the orderbook dynamics with a concrete mathematical representation. However, this suffers from a variety of issues that arise due to the inability of the chosen model to reproduce "empirical facts" observed in real-life limit order books due to its simplicity. An exciting alternative is to "learn" the orderbook dynamics using a deep generative model such as a conditional GAN or a conditional VAE. This enables much more complex dynamics to by captured and the realism of the simulated orderbook to vastly improve. One can then train a reinforcement learning agent to optimally make markets in this environment.
Before joining Liverpool, I did my PhD in Statistics at the University of Warwick under the supervision of Professor David Hobson and Dr Martin Herdegen, where I worked on problems on financial stochastic optimal control. In particular, I researched a variant of the Merton optimal investment-consumption problem where the agent's preferences are given by stochastic differential utility.