Fabrizio Lillo, Full Professor of Mathematical Methods for Economics and Finance at the University of Bologna and at the Scuola Normale Superiore in Pisa (Italy), joins the advisory board of aisot to provide scientific insights into microstructures of financial markets and high frequency finance.
Fabrizio Lillo is one of the leading scientists in the field of mathematical finance and market microstructures. His perspective will add significant scientific value to the aisot advisory board. Fabrizio comments: “Having worked with the co-founders over the last years, I gained a deeper insight in their abilities and the innovative machine learning approach they take. So I am fully convinced that aisot has the potential to define the future of data analytics in finance and beyond.“
Fabrizio Lillo is Full Professor of Mathematical Methods for Economics and Finance at the University of Bologna (Italy). Since July 2021 he also holds the chair (part time) of Mathematical Finance on Market Microstructure, Networks and Systemic Risk at the Scuola Normale Superiore in Pisa (Italy). Formerly he has been Associate Professor of Mathematical Finance at the Scuola Normale Superiore, where he led the Quantitative Finance group. He has also been Postdoc, External Faculty, and Professor at the Santa Fe Institute (USA).
He is also a member of the editorial board of several journals (including Journal of Statistical Mechanics (JSTAT) and Market Microstructure and Liquidity). As a researcher and consultant, Fabrizio has worked in areas including market microstructure, data science, econometrics, forecasting models, high frequency trading, machine learning, portfolio optimization, risk management, trading strategies, transaction cost analysis.
“The aisot advisory board brings together an impressive mix of industry and research leaders from the finance and trading fields,” said Stefan Klauser, CEO, aisot. “We look forward to working with Fabrizio. Our advisors play a critical role in shaping our go to market strategy and reflecting on aisot’s directly actionable signals so we can refine our approach and introduce aisot signals at scale.”
