Giulia Pucci

Since September 2022, I am a doctoral student in Applied and Computational Mathematics at KTH under the supervision of Nacira Agram.

Publications

• Agram, N., Espen Benth, F., & Pucci, G. (2025) Installation of Renewable Capacities to Meet Energy Demand and Emission Constraints under Uncertainty, IMA Journal of Management Mathematics, dpaf023

• Agram, N., Pucci, G., & Øksendal, B. (2024) Impulse Control of Conditional McKean--Vlasov Jump Diffusions, Journal of Optimization Theory and Applications, 200(3), 1100–1130

Preprints

• Agram, N., & Pucci, G. (2025)Deep BSVIEs Parametrization and Learning-Based Applications [arXiv:2507.01948]

• Agram, N., Arharas, I., Pucci, G., & Rems, J. (2025)Deep Learning for Energy Market Contracts: Dynkin Game with Doubly RBSDEs [arXiv:2503.00880]

• Gozzi, F., Leocata, M., & Pucci, G. (2024)Network-Based Optimal Control of Pollution Growth [arXiv:2406.15338]

Invited Talks and Seminars

• Talk at Luiss Guido Carli University, Rome, Italy (Mar 26, 2025)
  Presentation: Deep Learning for Energy Market Contracts: Dynkin Game with Doubly RBSDEs

• Stochastics in Mathematical Finance and Physics Conference, Hammamet, Tunisia(Oct 21–25, 2024)
  Presentation: Installation of Renewable Capacities to Meet Emission Targets and Demand under Uncertainty

• Conference on Stochastic Analysis with Applications to Finance, Energy and Insurance, Algeria (Mar 6, 2024)
  Presentation: Stochastic Modeling in the Energy Sector

• Workshop on S(P)DEs: Numerics and Applications, Linnaeus University, Växjö, Sweden (Dec 6–8, 2023)
  Presentation: Impulse Control of Conditional McKean–Vlasov Jump Diffusions

Conference Participation

• 12th General AMaMeF Conference, Verona, Italy (Jun 23–27, 2025)
  Presentations:

  • Deep Learning for Energy Market Contracts: Dynkin Game with Doubly RBSDEs

  • Deep BSVIEs Parametrization and Learning-Based Applications (Replacement for Nacira's talk)

• Insurance Data Science Conference, Stockholm University, Sweden (Jun 17–18, 2024)
  Presentation: Network-Based Optimal Control of Pollution Growth