Serena Della Corte

(temporary) Assistant Professor at the Mathematical Institute of
Leiden University

Latest news:

New preprint! Curious to see what neural networks are actually using when calibrating Heston and rough Heston models? We explore this through SHAP and nuSHAP, looking at feature relevance, redundancy, and dimensionality reduction in implied volatility surfaces. Read it here
New prepint! Curious how unpunctuality in scheduled arrivals can lead to rare congestion? We develop a sample-path large deviations framework and identify the most likely path to workload overflow. Read it here
"Machine learning for option pricing: an empirical investigation of network architectures" is published! Read it here
"A comparison principle based on couplings of partial integro-differential operators" is published! Read it here

I am currently a (temporary) assistant professor at Leiden University.

Previously, I was a postdoctoral researcher at Delft Institute of Applied Mathematics, working on machine learning methods for finance, particularly for stochastic decision-making problems.

Previously, I was a PhD candidate at TUDelft in the group of Applied Probability, where I studied Hamilton–Jacobi equations and their role in large deviations for Markov processes.

Research interests:

Stochastic analysis and applied probability, with a focus on Hamilton–Jacobi equations and viscosity solutions, large deviations for Markov processes, and machine learning methods for stochastic decision problems in finance.

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