Valentina Sessa
Présentation
I was born in Avellino, Italy, in 1983. I received the Master degree in automatic control engineering and the PhD degree in information engineering from University of Sannio, Benevento, Italy, in 2010 and 2013, respectively. In 2012, I was a visiting student at INRIA Rhône-Alpes, France. After one year of Postdoctoral Fellowship at the Department of Engineering, University of Sannio, I had a postdoctoral position at IMPA (Instituto Nacional de Matemática Pura e Aplicada), Rio de Janeiro, Brazil. From August 2015 to June 2017, I was Assistant Professor at the Department of Electronic and Telecommunications Engineering at State University of Rio de Janeiro (UERJ), Brazil, where I taught two courses: Automatic control and Mathematical model for electrical engineering. From October 2018 to December 2020, I was Ingénieur de recherche at Mines ParisTech, Sophia Antipolis, where I collaborate on the European Project, called Clim2Power. My current research interests include analysis of nonsmooth dynamical systems, in particular, piecewise linear and complementarity systems; modelling and control of power electronic converters; numerical algorithms for complementarity problems; global optimization. Lately, I got interest also for machine learning applied to energy problems.
Publications
Publications
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Investigating a Hybrid Approach for Global Optimization of Pump Scheduling Problem3IA Côte d'Azur Scientific Days 2022, Nov 2022, Sophia Antipolis, France |
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VARIABILTY OF LONG TERM ESTIMATES OF HYDRO POWER GENERATION ON A EUROPEAN SCALEInternation Conference on Applied Energy, Aug 2019, Vasteras, Switzerland |
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Alternating direction method and deep learning for discrete control with storageCombinatorial Optimization, 14594, Springer Nature Switzerland, pp.85-96, 2024, Lecture Notes in Computer Science, ⟨10.1007/978-3-031-60924-4_7⟩ |
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A two-phase sequential algorithm for global optimization of the standard quadratic programming problem2024 |
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Modeling the climate dependency of the run-of-river based hydro power generation using machine learning techniques: an application to French, Portuguese and Spanish cases2020 |
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Machine learning for assessing variability of the long-term projections of the hydropower generation on a European scale2020 |