Find Academic Supervisors

Giampaolo LiuzziView profile

Associate Professor

• Nonlinear Optimization
• Global Optimization
• Mixed Integer Nonlinear Programming
• +3 more

Giampaolo Liuzzi is an Associate Professor at the Department of Computer, Automation, and Management Engineering 'Antonio Ruberti' (DIAG) at Sapienza University of Rome since July 2023. Previously, he was a fixed-term researcher at the same department (July 2020-June 2023) and a Senior Researcher at the Institute of Systems Analysis and Computer Science 'A. Ruberti' of the CNR until July 2020. He teaches Mathematical Programming, Complements of Mathematics, and Mathematical Analysis 2 for various engineering programs at Sapienza University. Dr. Liuzzi's research focuses on Nonlinear Optimization , particularly derivative-free methods for constrained and unconstrained optimization, global optimization, mixed integer nonlinear programming, and applications in operations research and machine learning. His work spans theoretical developments in optimization algorithms as well as practical applications in engineering design, simulation-based optimization, and biomedical systems. He has made significant contributions to derivative-free optimization techniques that don't require gradient information, which is particularly valuable for black-box optimization problems where derivatives are unavailable or expensive to compute. His recent publications (2022-2025) demonstrate a strong focus on advancing derivative-free optimization methods, with particular attention to complexity analysis, convergence properties, and practical implementations for challenging problem classes including nonsmooth, constrained, multi-objective, and mixed-integer optimization problems. His work bridges theoretical computer science with practical engineering applications, with publications appearing in top optimization journals like Optimization Methods & Software, Computational Optimization and Applications, and Journal of Optimization Theory and Applications. Dr. Liuzzi has received significant professional recognition through National Scientific Habilitations for both Associate Professor (2014) and Full Professor (2018) positions in Italy. He is actively involved in the academic community as an administrator of the Derivative-Free Library (DFL), a collection of algorithms and methods for derivative-free optimization developed through collaboration among several prestigious Italian research institutions. As an educator, Dr. Liuzzi has developed comprehensive teaching materials for courses in Mathematical Programming, Complements of Mathematics, and Mathematical Analysis. He is also engaged in academic entrepreneurship as a co-founder of DEIX s.r.l., a Sapienza startup focused on algorithms and industrial software for planning and control of complex systems. Additionally, he organized the 2nd Derivative-Free Optimization Symposium (DFOS'24) in June 2024 in Padua, highlighting his leadership role in this specialized optimization community.

• Operational Research
• Optimization Methods
• Logistics and Supply Chain Management
• +7 more

Manuel Iori is a Full Professor at the Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia (UNIMORE), Italy. His primary research focuses on operational research, optimization methods, and logistics systems. He specializes in vehicle routing problems, scheduling algorithms, and decision support systems with applications in industrial automation, healthcare, and service industries. Iori is actively involved in teaching advanced optimization courses for engineering students, emphasizing practical applications in data-driven decision-making and simulation. Research Interests: His work addresses complex optimization challenges such as multi-trip vehicle routing with time windows, scheduling under resource constraints, and tool switching in manufacturing systems. He integrates machine learning and metaheuristics to develop innovative solutions for logistics, production planning, and healthcare operations. Collaborations with industry partners (e.g., pharmaceutical distributors, printing companies) ensure practical relevance of his research. Teaching: Iori teaches courses like Optimization Methods for Data-Driven Engineering Processes , Models for Logistics and Production Optimization , and Methods and Algorithms for Optimization in Digital Industries . These courses combine theoretical foundations with hands-on labs using tools like Python, Xpress, and Anylogic. Key Contributions: He developed decision support systems for multi-trip routing in pharmaceutical distribution and supplier selection in facility management. His research on satellite scheduling and attended home delivery systems advances both theoretical and applied domains. As a member of CIRRELT (Canada), he collaborates on logistics optimization projects. Professional Activities: Iori’s work is reflected in over 80 peer-reviewed publications and contributions to conferences. He advises graduate students on optimization challenges and serves as a reviewer for top journals in operations research.

Veronica PiccialliView profile

Professor

• Nonlinear Optimization
• Semidefinite Programming
• Game Theory
• +6 more

Veronica Piccialli is a Full Professor at the Department of Computer, Control and Management Engineering "Antonio Ruberti" (DIAG) at Sapienza University of Rome. She teaches Geometry I for Management Engineering and Optimization Methods for Machine Learning for Data Science. Previously, she was Associate Professor at the University of Rome Tor Vergata (2020-2021) and Researcher there from 2008 to 2020. She serves as Associate Editor for INFORMS Journal on Computing (since 2019) and EURO Journal on Computational Optimization (since 2021). Dr. Piccialli earned her degree in Computer Engineering (summa cum laude) and PhD in Operations Research from Sapienza University of Rome in 2000 and 2004 respectively. In 2006, she completed a postdoc at the Combinatorics & Optimization department of the University of Waterloo, Canada. She obtained Italian national scientific qualifications as Associate Professor in 2013 and as Full Professor in 2017. Her research focuses on the intersection of optimization and machine learning, with particular expertise in nonlinear optimization, semidefinite programming, and mixed integer nonlinear programming. She applies these methods to diverse areas including Brain Computer Interfaces, electric consumption disaggregation, and process engineering for membrane systems. Her work demonstrates how advanced optimization techniques can enhance machine learning algorithms and solve complex engineering problems. Her recent publications show a strong trend toward integrating optimization with machine learning, particularly in clustering algorithms, support vector machines, and neural networks. She has developed exact algorithms for semi-supervised learning problems and applied optimization techniques to real-world challenges in logistics, energy systems, and process engineering. Her interdisciplinary approach bridges theoretical advances in optimization with practical applications across multiple domains. Dr. Piccialli has authored or co-authored over 40 articles in prestigious journals including Mathematical Programming, SIAM Journal on Optimization, IEEE Transactions on Neural Networks and Learning Systems, and Computational Optimization and Applications. She has also contributed 3 refereed book chapters to international publications. As an educator, she supervises student research and teaches advanced courses in optimization methods and geometry. Her teaching materials demonstrate a commitment to connecting theoretical concepts with practical applications, particularly in data science and machine learning contexts. Her current research involves collaborations with Université de Lorraine on membrane systems for gas filtration and with RFI (Rete Ferroviaria Italiana) on optimizing last-mile facilities for freight trains, demonstrating the real-world impact of her optimization expertise.

Boris HouskaView profile

Associate Professor

• Numerical Optimization
• Optimal Control
• Robust and Global Optimization
• +5 more

Boris Houska is an Associate Professor at the School of Information Science and Technology at ShanghaiTech University. His research focuses on numerical optimization, optimal control, robust and global optimization, and fast model predictive control algorithms. He holds a Ph.D. in Electrical Engineering from KU Leuven (2011) and a diploma in Mathematics and Physics from the University of Heidelberg (2007). Prior to his current role, he held positions including Assistant Professor at ShanghaiTech (2014–2020), postdoctoral researcher at Imperial College London (2012–2013), and visiting roles at institutions like UC Berkeley and the Freiburg Institute for Advanced Studies. His work emphasizes algorithm development for control systems, with notable contributions to the ACADO Toolkit, a widely recognized open-source framework for automatic control and dynamic optimization. Key awards include the ICCOPT Best Paper Prize (Finalist), a Marie-Curie Fellowship, and the ShanghaiTech Excellent Professor Award. Research interests span distributed optimization, robust control, and applications in power systems and robotics. His publications often address challenges in MPC (Model Predictive Control), including tube-based methods, distributed algorithms, and stochastic systems. Collaborative work includes contributions to smart grid optimization and autonomous vehicle coordination at traffic intersections.

Laura PalagiView profile

Professor

• Nonlinear Optimization
• Mixed Integer Nonlinear Programming
• Semidefinite Programming
• +7 more

Laura Palagi is a Full Professor at Sapienza University of Rome, Department of Computer, Automation, and Management Engineering "Antonio Ruberti". She has been with the institution since 1996, progressing from Researcher to Associate Professor in 2005, and Full Professor since March 2020. She currently serves as Coordinator of the PhD program in Automatic Control, Bioengineering and Operations Research (ABRO) since February 2024 and is the GEP committee representative at the DIAG Department. Her research focuses on developing and analyzing methods for solving nonlinear optimization problems with continuous and discrete variables. Key research themes include decomposition methods for training Support Vector Machines and Deep Networks, methods for Mixed Integer Nonlinear Programming (MINLP), Semidefinite Programming (SDP), standard quadratic problems (StQP), and convex quadratic constrained problems. She also develops predictive machine learning models for healthcare applications and surrogate models for engineering. Her recent publications span optimization methods, machine learning applications in healthcare, sustainability assessment, and network design. Her work demonstrates a strong integration of theoretical optimization with practical applications across multiple domains. Continuous Optimization Awards 2020 Member Best Paper Award from Journal of Global Optimization Honorable Mention from Marguerite Frank Award jury for Best EJCO Paper 2024 EURO Doctoral Dissertation Award Professor Palagi teaches multiple courses including Optimization and Decision Science, Laboratory of Decision Science, Optimization Methods for Machine Learning, and Optimization for Medical programs. She serves as thesis advisor for numerous Bachelor's and Master's students in Management Engineering, Mechanical Engineering, and Data Science programs.

• Systems Engineering
• Control Engineering
• Discrete Event Systems
• +7 more

Mariagrazia Dotoli is a Full Professor in Systems and Control Engineering at the Polytechnic University of Bari, Department of Electrical and Information Engineering, where she has been serving since 1999. She previously held the position of Vice Rector for Research (2011-2013) and served as a member elect of the Academic Senate (2012-2015). Currently, she coordinates the interuniversity PhD course in Industry 4.0 between the Polytechnic University of Bari and the University of Bari Aldo Moro. Her research interests span across multiple domains of systems engineering, with particular focus on discrete event industrial systems, Petri nets, manufacturing systems, supply chains, logistics and transportation systems, traffic networks, and energy systems. Her work bridges theoretical control systems with practical industrial applications, especially in the context of Industry 4.0 and smart manufacturing. Her extensive publication record demonstrates consistent contributions to automation science and engineering, with recent work focusing on warehouse optimization, collaborative robotics, supply chain management, and smart energy systems. Her research shows a clear trend toward integrating classical control theory with modern computational approaches, including machine learning and optimization algorithms for industrial applications. Prof. Dotoli maintains significant editorial responsibilities as Senior Editor of the IEEE Transactions on Automation Science and Engineering and Associate Editor for multiple IEEE journals. She has organized and chaired numerous international conferences including CASE2024, MED2021, and CODIT2020, demonstrating leadership in the automation community. Her academic career shows continuous progression from Assistant Professor (1999) to Full Professor, with additional leadership roles in university administration and international professional organizations. She remains actively engaged in both theoretical research and practical industrial applications of control systems engineering.

Graziana CavoneView profile

Associate Professor

• Automatic Control
• Model Predictive Control
• Cyber-Physical Systems Security
• +5 more

Associate Professor Graziana Cavone is with the Department of Civil, Computer and Aeronautical Technologies Engineering at Roma Tre University, Rome, Italy. Her research integrates automatic control, optimisation and AI to guarantee safe, secure and efficient operations of cyber-physical systems ranging from industrial robots and drones to water networks and intermodal freight terminals. Education & affiliation: Department of Civil, Computer and Aeronautical Technologies Engineering, Roma Tre University Scientific disciplinary sector ING-INF/04 – Automatic Control Research interests revolve around four pillars: (i) model-predictive and data-driven control of complex dynamic systems, (ii) safe and ergonomic human-robot/human-drone interaction in logistics 4.0 environments, (iii) cyber-security and resilience of industrial control and water-supply networks, and (iv) stochastic optimisation and Petri-net modelling for railway traffic and intermodal freight terminals. Recent 2024-2025 publications exhibit a clear trend toward AI-enabled anomaly detection, digital-twin security frameworks, and socio-technical studies on robot acceptance, while earlier work concentrated on COVID-19 mitigation control and collaborative-robot motion planning. Scientific awards & editorial activity: Guest-editor for IEEE CASE 2018 special section, publicity chair for IEEE CASE 2023; specific prizes not listed in supplied text. Advising & grants: No explicit student lists or funded-project details are provided in the source. Labs & teams: No dedicated laboratory designation is mentioned; research is conducted within the departmental laboratories of Roma Tre University.

• Operations Research
• Logistics Optimization
• Mathematical Programming
• +3 more

Massimo Pappalardo is a Full Professor in the Department of Computer Science at the University of Pisa. His research focuses on Operations Research, particularly in logistics optimization, mathematical programming, and equilibrium problems. He is actively involved in teaching, including courses on Operations Research for Computer Engineering and logistics applications for Master's programs in Management and Control of Logistics Systems. His work bridges theoretical advancements and practical applications in optimization algorithms, variational inequalities, and stochastic modeling. His academic contributions span over three decades, emphasizing nonlinear programming, variational analysis, and equilibrium models. He has authored or co-authored numerous papers in top journals and conference proceedings, addressing topics like lexicographic multi-objective optimization, grossone methodology, and network equilibrium models. His research frequently intersects with computational methods and real-world problems in logistics and energy systems. Teaching materials and reception schedules are managed via the university's teaching teams portal. No specific awards are mentioned in the provided texts, but his extensive publication record reflects significant scholarly impact in optimization and operations research.

• Combinatorial Optimization
• Meta-heuristics
• Mixed Integer Programming
• +7 more

Luigi De Giovanni is an Associate Professor of Operations Research at the Department of Mathematics, University of Padua, where he has been faculty since 2007 (promoted to Associate Professor in 2018). He holds the Italian academic qualification as Full Professor since 2020. His research focuses on Combinatorial Optimization, Meta-heuristics, Mixed Integer Programming, and their applications in Transportation and Logistics, Air traffic and Airport Optimization, Telecommunication Network Design, and Production Scheduling. His work bridges theoretical optimization methods with practical industrial applications across various sectors including transportation, manufacturing, and telecommunications. Professor De Giovanni has led and participated in several significant research projects including: European Research Project 'OptiFrame - An Optimization Framework for Trajectory Based Operations' (H2020-SESAR-2015-1, 2016-2018) PRIN Project 'Nonlinear and Combinatorial Aspects of Complex Networks' (2017-2020) European Research Project 'AAS - Integrated Airport Apron Safety Fleet Management' (2008-2011) European Research Project 'SCOOP - Sheet Cutting and Process Optimization for Furniture Enterprises' (2006-2008) His extensive publication record spans from 2002 to 2024, with numerous articles in high-impact journals such as Transportation Science, IEEE Access, and European Journal of Operational Research. Recent publications show a strong emphasis on data-driven approaches to air traffic management, vehicle routing and logistics optimization, and graph theory applications, demonstrating continued research productivity and relevance. Professor De Giovanni teaches courses in Operations Research, Methods and Models for Combinatorial Optimization, and Stochastic Optimization at the University of Padua. His office hours are on Thursdays from 10:30 am to 12:30 pm by appointment.

• Operations Research
• Optimization
• Mathematical Programming
• +5 more

Marianna De Santis is an Associate Professor in the Department of Information Engineering (Dipartimento di Ingegneria dell’Informazione) at the University of Florence, Italy. She previously held positions at Sapienza University of Rome and other institutions. Her research focuses on optimization, particularly in multiobjective programming, mixed-integer programming, and algorithm design. She has authored numerous articles in top journals and developed software tools like BBMOQIP and MOMIX for solving complex optimization problems. Education : PhD in Operations Research, Sapienza University of Rome (2012) MSc in Applied Mathematics (2007), BSc in Mathematics (2005) Research Interests : Dr. De Santis specializes in optimization theory and applications, including multiobjective optimization, integer programming, and algorithm design. Her work bridges theoretical advancements with practical implementations, addressing challenges in machine learning, bioinformatics, and operations research. Key areas include semidefinite programming, robust optimization, and exact methods for complex combinatorial problems. Articles Trends : Her recent work emphasizes multiobjective optimization, with contributions to hyperparameter optimization, robust combinatorial methods, and sustainable modeling. Publications span journals like Journal of Global Optimization and Mathematical Programming , reflecting her expertise in algorithmic innovation and mathematical rigor. Grants & Teaching : She leads projects on augmented Lagrangian methods and multiobjective integer optimization. Teaching includes courses on operations research, optimization, and data science at the undergraduate and graduate levels. She has also organized international workshops and served on conference committees. Labs/Teams : Her research group develops optimization software (e.g., BBMOQIP) and collaborates internationally on projects involving algorithm design and real-world applications.

• Control Systems
• Game Theory
• Robotics
• +3 more

Filippo Fabiani is an Assistant Professor at the IMT School. His research focuses on control systems, game theory, and optimization, with applications to robotics, multi-agent systems, and nonlinear dynamics. He explores topics such as Nash equilibrium seeking in strategic environments, fault-tolerant control, and neural network-based system identification. His work combines theoretical advancements in distributed algorithms with practical implementations in aerospace and robotic systems. Notable areas include spacecraft proximity guidance, resilient control for nonlinear systems, and adaptive strategies in competitive multi-agent scenarios. He also investigates belief-sharing mechanisms and robust stabilization under uncertainty. Publications highlight contributions to generalized Nash games, distributed equilibrium-seeking algorithms, and model predictive control. Research spans both foundational theory and applied engineering solutions, emphasizing the interplay between strategic decision-making and control system design.

Luigi GlielmoView profile

Professor

• Automatic Control
• Model Predictive Control
• Renewable Energy Systems
• +5 more

Luigi Glielmo is a Professor of Automatic Control at the University of Sannio in Benevento, Italy. He holds a Master's and PhD in Electronic Engineering and Automatic Control from the University of Naples Federico II. Previously, he taught at the University of Palermo before returning to Naples. His academic leadership roles include Head of the Department of Engineering at University of Sannio (2001–2007), Rector’s Delegate for Technology Transfer (2009–2019), and Coordinator of PhD programs in Information Engineering and Information Technologies for Engineering. Research interests focus on automatic control engineering, including model predictive control, renewable energy systems, smart grids, robotics, biomedical applications (e.g., deep brain stimulation modeling), satellite autonomy, and Boolean control networks. He founded the GRACE research group and has authored over 190 papers, two books, and holds two patents. He is a Senior Member of IEEE, serves on the editorial board of IEEE Control Systems Letters, and chairs the IEEE Control Systems Society Technical Committee on Automotive Controls. Leadership roles include General Co-Chair of the 2019 European Control Conference and ordinary member of IFAC’s Conference Board. His work bridges theoretical advancements with practical applications in energy systems, robotics, and industrial automation.

Stefano LucidiView profile

Professor

• Nonlinear Optimization
• Derivative-Free Optimization
• Mixed Integer Programming
• +3 more

Stefano Lucidi is a Full Professor of Operations Research at Sapienza University of Rome, where he is affiliated with the Department of Computer, Automatic and Management Engineering within the Faculty of Information Engineering, Computer Science and Statistics. He has held this position since November 1, 2000, after serving as Associate Professor from November 1, 1992 to October 31, 2000. He was coordinator of the PhD program in Operations Research from 2010 to 2012 and was a shareholder of the university spin-off ACTOR SRL until July 2024. His research interests span Nonlinear Optimization, Derivative-Free Optimization, Mixed Integer Programming, and Global Optimization. His methodological work focuses on unconstrained optimization methods, constrained optimization methods, non-differentiable optimization methods, derivative-free methods, and global optimization techniques. His applied research includes mathematical modeling of biological phenomena in cell kinetics, identification of astrophysical parameters, optimal management of bookable seats in rail transport, and optimal design of electromagnetic devices and industrial electric motors. His recent publications demonstrate a strong focus on derivative-free optimization methods, complexity analysis of algorithms, multi-objective optimization, and applications in diverse fields including healthcare, transportation, and neuroscience. His work bridges theoretical advances in optimization with practical applications across multiple domains. His significant contributions to the field include developing algorithms for simulation-driven design optimization, addressing nonsmooth optimization problems, and creating methods for multi-fidelity computations. His research has been published in top journals including Optimization Methods & Software, Journal of Optimization Theory and Applications, and Optimization Letters. Full Professor of Operations Research since 2000 PhD program coordinator (2010-2012) Shareholder of ACTOR SRL spin-off (2011-2024) Active contributor to optimization theory and applications Professor Lucidi has been instrumental in advancing optimization methodologies while maintaining strong connections to practical applications across various industries. His work continues to influence both theoretical developments and real-world implementations of optimization techniques.