Daniel is a Financial Mathematics MSc graduate and an Applied Mathematics Ph.D. candidate. His research primarily focuses on fractional processes like the Ornstein-Uhlenbeck process and their relevance in fields such as stochastic correlation and the Heston model. His work ranges from the analysis of parameter estimation within these processes to the application of the Stochastic Correlation Process in complex financial models. Furthermore, he is interested in harnessing the power of neural networks to understand better and simulate these processes. In essence, his work combines advanced mathematics and AI to elucidate complex financial phenomena and advance predictive modeling capabilities. Daniel is also a member of the ELTE AI Research Group.