Dr David Šiška is contributing to mathematical understanding and theoretical guarantees for machine learning and reinforcement learning algorithms and its applications in engineering, economics and finance. David is a Reader in mathematics with active research in the mathematical foundations of AI, reinforcement learning, stochastic control, game theory and financial mathematics. He works with industry leaders and startups in various consulting roles, solving real-world problems related to financial risk using mathematical modelling and agent-based modelling.Current AI projectsDavid is mostly focused on the following aspects of reinforcement learning (RL):Theoretical guarantees that an RL algorithm will learn.Efficient algorithms for near-continuous-time RL.Efficient function approximation in RL with theoretical guarantees in actor-critic settings. Awards and fellowshipsDavid is the Co-Investigator on an EPSRC Prosperity Partnership project, AI2 Assurance and Insurance for Artificial Intelligence, which will enable insurers to accurately price and underwrite AI-related risks in areas such as transport and healthcare.Learn more about AI2 Assurance and Insurance for Artificial Intelligence:New Partnership Aims To Understand Risks Associated With AI | Insurance EdgeEdinburgh Uni Launches £2M Project to Build Insurance for AI Risks | DIGITHe is the Principal Investigator on DeFi RiskMetrics via multi-agent simulations.Get in touchPlease visit David's research website:David Siska | School of MathematicsRecent publications using AI techniques (open access)A Fisher-Rao gradient flow for entropy-regularised Markov decision processes in Polish spaces | Foundations of Computational MathematicsRobust pricing and hedging via neural SDEs | Journal of Computational FinanceMean-Field Langevin Dynamics and Energy Landscape of Neural Networks | Annales de l’Institut Henri Poincaré This article was published on 2025-08-26
