Daniele Cappelletti

Daniele Cappelletti is an Associate Professor in the Department of Mathematical Sciences (DISMA) at the Politecnico di Torino, where he conducts research in stochastic modeling of biochemical reaction networks. He holds a PhD from the University of Copenhagen and has held postdoctoral positions at ETH Zurich, University of Wisconsin-Madison, and the University of Copenhagen. He is affiliated with the 'Probability and Applications' research group and leads the ConStRAINeD project funded by the Italian PRIN program.

• PhD in Probability, University of Copenhagen (2012–2015)
• M.S. in Mathematics, Pisa University (2009–2012)
• B.S. in Mathematics, Pisa University (2005–2009)

His research focuses on stochastic processes, probability theory, and mathematical biology, particularly in the modeling and analysis of chemical reaction networks. He investigates the long-term behavior, stability, and model reduction in both deterministic and stochastic frameworks, with applications in systems biology and synthetic biology. His work bridges theoretical mathematics with practical biological systems, especially those exhibiting absolute concentration robustness.

The trend in his recent publications shows a strong emphasis on the mathematical foundations of stochastic reaction networks, including tier structures, non-explosivity, and approximation techniques. He has also extended his work into computational applications such as using chemical networks to approximate probability distributions and analyzing security in blockchain protocols, demonstrating interdisciplinary reach across mathematics, biology, and computer science.

Daniele Cappelletti currently supervises PhD student Giulio Cuniberti in the Mathematical Sciences program at Politecnico di Torino. He has been involved in competitive research grants, most notably as Scientific Manager of the ConStRAINeD project (2023–2026), which aims to advance the convergence and stability theory of reaction network dynamics. He has not received any explicitly mentioned scientific awards in the provided text.

He is actively involved in teaching across multiple programs, including Mathematical Engineering, Data Science and Engineering, and Management Engineering. He teaches courses on stochastic processes, statistics, and time series analysis, both as course instructor and collaborator. He is a member of the Doctoral College in Mathematical Sciences and participates in the College of Management and Production Engineering and the College of Mathematical Engineering.