Team
Données et apprentissage automatique
Biography
Valentina Sessa is a researcher whose work focuses on analyzing the environmental impacts of digital infrastructure and energy systems, as well as on mathematical optimization applied to various industrial fields. Her research specifically addresses the life cycle assessment (LCA) of data centers and generative artificial intelligence models, incorporating systematic approaches to quantify their carbon footprint and energy efficiency. She also explores advanced methods for detecting refrigerant leaks in industrial refrigeration systems, combining statistical techniques and dynamic models to improve the reliability of diagnostics. At the same time, her contributions to mathematical optimization include the development of sequential algorithms for solving quadratic and complementarity problems, with applications in energy planning and the modeling of dynamic systems. His work is based on an interdisciplinary approach, linking numerical modeling, machine learning, and the analysis of complex systems to address contemporary technical and environmental challenges.
Publication(s)
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2026
Generative AI impact assessment through a life cycle analysis of multiple data center typologies DOI : 10.1016/j.apenergy.2025.127288
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2025
Carbon Footprint of AI Data Centers: A Life Cycle Approach DOI : 10.46855/energy-proceedings-11569
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2025
PREFACE DOI : 10.23952/asvao.7.2025.3.01
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2025
Evaluating fault detection techniques for refrigerant leak detection in industrial vapor compression refrigeration systems DOI : 10.1109/ICCAD64771.2025.11099443
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2024
A two-phase sequential algorithm for global optimization of the standard quadratic programming problem DOI : 10.1007/s10898-024-01423-y
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2024
Alternating Direction Method and Deep Learning for Discrete Control with Storage DOI : 10.1007/978-3-031-60924-4_7
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2024
Refrigerant leak detection in industrial vapor compression refrigeration systems using machine learning; [Détection de fuite de liquide frigorigène dans les installations frigorifiques industrielles à compresseurs de vapeur utilisant l'apprentissage automatique] DOI : 10.1016/j.ijrefrig.2024.02.016
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2024
Computing Critical Angles Between Two Convex Cones DOI : 10.1007/s10957-024-02424-3
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2024
Towards Improved Datacenter Assessment: Review and Framework Proposition DOI : 10.46855/energy-proceedings-11079
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2022
Solution of Fractional Quadratic Programs on the Simplex and Application to the Eigenvalue Complementarity Problem DOI : 10.1007/s10957-022-02019-w
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2022
How sensitive is a carbon-neutral power sector to climate change? The interplay between hydro, solar and wind for Portugal DOI : 10.1016/j.energy.2021.122106
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2021
Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation DOI : 10.3390/cleantechnol3040050
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2021
Climate proofing the renewable electricity deployment in Europe - Introducing climate variability in large energy systems models DOI : 10.1016/j.esr.2021.100657
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2021
An alternating direction method of multipliers for the eigenvalue complementarity problem DOI : 10.1080/10556788.2020.1734804
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2020
A sequential partial linearization algorithm for the symmetric eigenvalue complementarity problem DOI : 10.1007/s10589-020-00226-7
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2019
Splitting methods for the Eigenvalue Complementarity Problem DOI : 10.1080/10556788.2018.1479408
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2019
Complementarity Model for Steady-State Analysis of Resonant LLC Power Converters DOI : 10.1109/TCSII.2018.2875591
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2019
VARIABILTY OF LONG TERM ESTIMATES OF HYDRO POWER GENERATION ON A EUROPEAN SCALE
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2017
Computation of periodic solutions in maximal monotone dynamical systems with guaranteed consistency DOI : 10.1016/j.nahs.2016.10.006
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2016
A single-phase active filter with cascaded multilevel inverter modelled as a complementarity problem DOI : 10.1109/IECON.2016.7793311
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2016
A complementarity approach for the computation of periodic oscillations in piecewise linear systems DOI : 10.1007/s11071-016-2758-5
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2016
On the numerical solution of the quadratic eigenvalue complementarity problem DOI : 10.1007/s11075-015-0064-9
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2014
A complementarity model for closed-loop power converters DOI : 10.1109/TPEL.2014.2306975
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2014
Time-stepping methods for constructing periodic solutions in maximally monotone set-valued dynamical systems DOI : 10.1109/CDC.2014.7039866
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2013
Computing period and shape of oscillations in piecewise linear Lur'e systems: A complementarity approach DOI : 10.1109/CDC.2013.6760622
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2012
Mixed linear complementarity problems for the analysis of limit cycles in piecewise linear systems DOI : 10.1109/CDC.2012.6426544
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2011
Computation of limit cycles in Lur'e systems DOI : 10.1109/acc.2011.5991294
Teaching
Mathematical Optimization for the Transition
The course is divided into two thematic parts, each alternating between lectures, tutorials (modeling exercises, convergence proofs), and lab sessions (implementation of the Gurobi solver): A. Integer Optimization for Discrete Problems 1. Review of Linear Optimization (Modeling Exercises, Geometry and Algebra, Duality and Optimality, Simplex Algorithm) 2. Integer Programming Modeling (Discrete Decisions, Logical Conditions, Nonlinear Functions, Ideal Formulations, Case Studies for the Transition) 3. Linear Integer Programming Algorithms (Cut Generation, Branch-and-Bound, Reformulation, and Decompositions) 4. Implementation (Branch-and-Cut, Modern Solvers and Parameterization, Case Study: Hybrid Electricity Generation) B. Equilibrium Problems from an Optimization Perspective 1. Review of Nonlinear Optimization (Problem Definition, Definition of Local and Global Solutions, Constraint Qualifications, First-Order Optimality Conditions, Second-Order Optimality Conditions, Duality) 2. Introduction to Complementarity Problems (Linear Complementarity Problems, Mixed Linear Complementarity Problems, Connection Between Optimization Problems and Complementarity Problems, Numerical Algorithms for Solving Complementarity Problems) 3. Optimization Problems Constrained by Complementarity Problems (Definition of Equilibrium Problems, Connection to Nash Equilibrium Problems, Transformation of a Nonconvex Quadratic Simplex Problem into a Mathematical Programming Problem with Complementarity Constraints) 4. Examples (Energy markets, International agreements on climate change adaptation) For each thematic section, course notes in slide format and the code needed for the lab will be made available to students. Equipment: personal computer with, as desired: a web browser and a GitHub or Google Drive account (for the code and to save the project in Google Colab) or a recent installation of Gurobi (several APIs available, including Python, C/C++, and Java) and MATLAB.
PhD supervision
- 2026 Decarbonization of road freight transport: optimizing the composition and use of electric truck fleets AL KOSTIT Malak
- 2024 Mathematical programming with equilibrium constraints: models and algorithms for nonconvex optimization MARTINS SASAKI Antonio
- 2023 Building a Methodology for Decarbonizing Electronic Equipment Through Design (Design-to-Green) LE BARROIS D'ORGEVAL Alexandre
- 2022 Refrigerant leak detection in industrial vapor-compressor refrigeration systems MTIBAA Amal