Find Academic Supervisors

• Additive Manufacturing
• Laser Metal Deposition
• Material Extrusion Processes
• +5 more

Maria Grazia Guerra is an Assistant Professor at the Department of Mechanics, Mathematics & Management at the Polytechnic University of Bari, Italy . Her work focuses on additive manufacturing, material extrusion processes, and optical monitoring systems for advanced manufacturing technologies. Research Interests: Maria's research spans multiple facets of additive manufacturing, including laser metal deposition, material extrusion, and sintering technologies. She investigates dimensional accuracy, defect detection, and sustainability assessment through exergetic analysis, while also exploring photogrammetry applications for micro-scale measurements and surface characterization. Publications: Maria has published extensively on topics such as layerwise monitoring in laser-powder bed fusion, surface grinding of thermoplastics, and chemical vapor treatment for polymer parts. Her work frequently addresses process optimization, geometric accuracy, and the development of novel methodologies for industrial measurement systems. Contact: Email: mariagrazia.guerra@poliba.it

• Robotics
• Precision Agriculture
• Vehicle Dynamics
• +5 more

Giulio Reina serves as a Full Professor in the Department of Mechanics, Mathematics and Management at the Polytechnic University of Bari, Italy. His academic focus centers on robotics and mechanical systems with strong applications in agricultural automation and vehicle dynamics. His research spans Robotics , Precision Agriculture , and Vehicle-Terrain Interaction Modeling . Key contributions include developing path-planning frameworks for agricultural robots, deformable tire models for obstacle negotiation, and yield estimation systems using deep learning in viticulture. His work integrates mechanical engineering principles with AI-driven solutions for field robotics applications. Analysis of his recent publications reveals a strong emphasis on mobile robotics for agricultural automation , particularly in grapevine monitoring and terrain interaction. His research bridges theoretical modeling (e.g., multibody dynamics) with practical implementations in off-road vehicles and farmer robots, consistently addressing challenges in precision agriculture through sensor fusion and machine learning. No specific scientific awards were documented in the provided materials. Professor Reina leads research in agricultural robotics without explicit mention of formal student advising in the source text. His work demonstrates significant focus on practical implementations for sustainable farming, including terrain estimation systems and autonomous navigation solutions for uneven agricultural environments. His laboratory activities center on agricultural robotics development, featuring projects like the SNAP lightweight vehicle platform and POLYPUS adaptive gripper systems. Current initiatives emphasize real-world deployment of farmer robots for crop monitoring and precision viticulture applications.

Luca BenvenutiView profile

Professor

• Nonlinear Control Systems
• Positive Systems
• Constrained Control
• +2 more

Luca Benvenuti serves as a Full Professor at Sapienza University of Rome, where he conducts pioneering research spanning theoretical control systems and practical sustainability applications. His work bridges engineering principles with real-world challenges in automotive systems, environmental science, and nutrition optimization. Education Laurea (Master's equivalent) with honors in Electronic Engineering from Sapienza University of Rome (1992) Visiting student at UC Berkeley's Department of Electrical Engineering and Computer Science (1995) PhD in Systems Engineering from Sapienza University of Rome (1996) Postdoctoral fellowship at University of L'Aquila (1997-1998) Research Focus Professor Benvenuti's research centers on nonlinear control systems for non-minimum phase trajectories, positive systems theory including minimal realizations and compartmental modeling, and constrained control for state/input limitations. His recent work demonstrates a strategic pivot toward sustainability applications , developing optimization models for sustainable diets, carbon footprint reduction in school meals, and biofortification strategies. The integration of hybrid control systems with automotive engineering remains a cornerstone of his theoretical contributions. Publication Trends Analysis of his 15 most recent publications reveals a pronounced interdisciplinary shift since 2019. While maintaining core contributions to positive systems theory (accounting for 35% of recent output), 65% of his work now focuses on sustainable nutrition systems. This includes triobjective diet optimization, carbon footprint modeling for institutional meals, and biofortification impact studies—primarily using integer programming and multi-criteria decision frameworks applied to Italian case studies. Scientific Recognition IEEE Transactions on Circuits and Systems Guillemin-Cauer Best Paper Award (2001) IFAC Congress Applications Paper Prize (2005) Plenary speaker at Second Multidisciplinary International Symposium on Positive Systems (2006) Professional Engagement Benvenuti has maintained significant industry connections through consultancy roles with Magneti Marelli's Engine Control Division (1997) and PARADES research laboratory (1997-2000), supported by Cadence Design Systems, ST-Microelectronics, and CNR. His research methodology combines theoretical rigor with practical validation, particularly evident in automotive control applications. Current work shows increasing collaboration with nutrition scientists and environmental researchers, reflecting his expanded focus on food systems sustainability.

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.

• Autonomous Robotics
• Unmanned Aircraft Systems
• Flight Mechanics
• +3 more

Stefano Primatesta is a Fixed-term tenure-track Assistant Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at Politecnico di Torino. He is also a member of the Interdepartmental Center PIC4SeR - PoliTO Interdepartmental Center for Service Robotics. His academic work focuses on flight mechanics and control systems within the broader field of Industrial and Information Engineering. His research interests span multiple domains related to autonomous systems: Autonomous Robotics Unmanned Aircraft Systems (UAS) Flight Mechanics Control Engineering Robotics Simulation Engineering and Modelling Professor Primatesta's recent publications demonstrate a strong focus on UAV control systems, path planning in complex environments, and practical applications of drone technology in agriculture and urban logistics. His work combines theoretical control approaches with practical implementation, often addressing real-world challenges such as payload uncertainty, urban navigation constraints, and mission safety. His notable scientific contributions include: Development of robust control systems for quadrotor UAVs Research on noise-aware path planning using reinforcement learning Work on multi-UAV formation flight and target estimation Innovations in precision agriculture applications for drones Advancements in safe mission planning for urban environments Professor Primatesta actively supervises multiple PhD students working on cutting-edge UAV research topics, including robust control under uncertain conditions and urban air mobility applications. He serves as Scientific Director for the 4IPLAY research project focused on improving intelligent infrastructure inspection through advanced UAV autonomy. His teaching portfolio includes advanced courses in flight dynamics, modeling and simulation, and helicopter flight mechanics at both undergraduate and graduate levels. He contributes to multiple degree programs as an invited member of academic colleges.

Matteo GiulianiView profile

Associate Professor

• Water Resources Management
• Multi-Objective Optimization
• Reinforcement Learning
• +7 more

Matteo Giuliani is an Associate Professor at Politecnico di Milano, leading research in the Environmental Intelligence for Global Change Lab. His work focuses on integrating artificial intelligence with water resource management under global change scenarios. PhD in Information Technology (2014, Politecnico di Milano) Alta Scuola Politecnica Diploma (2011) MSc in Environmental and Land Planning Engineering (2010, Politecnico di Milano & Torino) His research bridges machine learning and water systems engineering through applications in: Climate change adaptation design Multi-agent negotiation for resource allocation Extreme event detection and attribution Decision-making under deep uncertainty Recent publications show expertise in reinforcement learning for reservoir operations, autoencoder applications to heat wave analysis, and AI-driven climate data downscaling . He serves as Section Editor for the Journal of Water Resources Planning and Management and Associate Editor for Frontiers in Water .

Gabriele ComodiView profile

Associate Professor

• Renewable Energy Systems
• Energy Storage Technologies
• Green Hydrogen Production
• +3 more

Prof. Gabriele Comodi is an Associate Professor at the Polytechnic University of Marche, affiliated with the Department of Industrial Engineering and Mathematical Sciences (DIISM). His research focuses on renewable energy systems, energy storage technologies, and sustainable energy planning. He has contributed extensively to advancing green hydrogen production, multi-carrier energy networks, and thermal energy management. His work integrates experimental validation, computational modeling, and policy analysis to address energy transition challenges. Key areas include alkaline electrolysis optimization, hybrid energy systems, and decarbonization strategies for urban and industrial applications. Recent publications highlight innovations in GIS-based energy frameworks, reversible solid oxide cells, and energy hub architectures. He collaborates on projects like the UNIVPM campus as a multi-carrier energy community. His technical expertise spans electrochemical systems, thermal networks, and smart city resilience strategies. No specific awards or grants are listed, but his active research portfolio reflects significant contributions to energy transition research.

Stefano SquartiniView profile

Professor

• Electrical Engineering
• Audio Signal Processing
• Neural Networks
• +2 more

Stefano Squartini is a Full Professor at the Polytechnic University of Marche, affiliated with the Department of Information Engineering. His research focuses on signal processing, audio engineering, and machine learning applications in embedded systems. Key interests include real-time audio generation, non-intrusive load monitoring, and biomedical signal analysis. His recent publications demonstrate a strong focus on AI-driven solutions for telecommunications, healthcare, and energy systems. Research spans generative models for audio restoration, deep learning for medical diagnostics, and optimization of renewable energy technologies. He maintains active collaboration in multi-disciplinary projects involving neural networks for industrial and clinical applications, with emphasis on low-latency implementations and edge computing.

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.

• Transportation Engineering
• Operations Research
• Logistics
• +4 more

Ludovica Adacher is an Associate Professor at the Department of Civil, Computer and Aeronautical Engineering, Roma Tre University, Rome, Italy, specializing in optimization methodologies for transportation and logistics systems. Her work bridges theoretical operations research with practical engineering applications across multiple critical infrastructure domains. Her research focuses on Transportation Engineering and Operations Research, with deep expertise in Air Traffic Management, Urban Traffic Control, and Logistics optimization. She develops advanced models for vaccination clinic design, airport operations, and sustainable freight distribution, emphasizing cost-QoS tradeoffs, environmental impact reduction, and robustness under uncertainty. Her methodological toolkit includes Lagrangian relaxation, heuristic algorithms, and simulation techniques applied to real-world congestion and resource allocation problems. Analysis of her 15 most recent publications reveals a consistent pattern of addressing high-impact transportation challenges through mathematical optimization. Her work spans air traffic congestion resolution, electric vehicle infrastructure planning, urban freight sustainability, and healthcare logistics, with increasing emphasis on multi-objective frameworks that balance operational costs, environmental metrics, and social factors. The research demonstrates strong interdisciplinary connections between civil engineering, computer science, and aeronautical systems.

• Environmental Engineering
• IoT Systems
• Cybersecurity
• +3 more

Pietro Chiavassa is a Research Fellow at the Consiglio Nazionale delle Ricerche (CNR) Institute of Electronics, Information, and Telecommunication Engineering (IEIIT), collaborating as an external lecturer at Politecnico di Torino's Department of Automatic Control and Computer Engineering (DAUIN). His work bridges environmental engineering, cybersecurity, and quantum computing with a focus on IoT systems. He contributes to teaching modules in algorithms and object-oriented programming for aerospace and cinema engineering programs. Research interests include indoor environmental quality (IEQ) monitoring through low-cost sensor networks, pervasive computing security, and quantum computing applications for sensor calibration and network optimization. His projects involve developing traceable multi-sensor devices for urban air quality assessment and systems integrating acoustic comfort with occupant feedback in educational spaces. Key trends in his publications emphasize cross-disciplinary collaboration between CNR-IEIIT and Politecnico di Torino, focusing on practical implementations of advanced technologies in urban and indoor environments. His work addresses challenges in sensor data reliability, cybersecurity in embedded systems, and leveraging quantum computing for optimization problems in IoT. Current initiatives include the Q-SCALE project for quantum-enhanced sensor calibration and the PROMET&O system for holistic IEQ monitoring. He actively participates in international conferences on noise control, building physics, and environmental engineering, showcasing innovative solutions for sustainable urban infrastructure and smart environments.

• 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.

• Operations Research
• Stochastic Programming
• Probabilistic Constraints
• +4 more

Patrizia Beraldi is an Associate Professor of Operations Research at the Department of Mechanical, Energy and Management Engineering (DIMEG) , University of Calabria, Italy since 2003. She holds the National Qualification as Full Professor (2013) and earned her PhD in System Engineering and Computer Science from the University of Calabria in 2000. Her academic journey includes roles as Research Assistant (1999-2001) and Visiting Research Fellow at RUTCOR (Rutgers University) and Vancouver University. Education PhD in System Engineering and Computer Science (2000), University of Calabria Degree in Management Engineering (cum laude 1995), University of Calabria Her research focuses on stochastic programming with probabilistic constraints, network flow optimization , and decentralized decision modeling . Key application areas include energy systems (smart grids, renewable integration), healthcare logistics (operating room scheduling), financial engineering (risk management), and supply chain resilience . She leads the Financial Engineering and Risk Management Laboratory at DIMEG. Recent research trends involve bi-level optimization models for energy pricing, hybrid multigeneration systems for sustainability, and stochastic approaches to resource allocation in healthcare and energy sectors. Her work emphasizes computational methods and high-performance computing platforms . Scientific Awards : Best paper award of the 2015 IMA Journal of Management Mathematics Best presentation award at the ICEER Conference 2017 Best poster award of ICORES 2018 She supervises numerous PhD and Master's students , contributes to editorial boards as Associate Editor (TOP, Algorithms, IMA Journal), and leads research projects like PON Horizon 2020 initiatives on smart energy systems. Her teaching spans courses in Optimization, Financial Engineering, and Stochastic Programming at the University of Calabria.

Alice Di BellaView profile

Researcher

• Energy System Modeling
• Energy Transition
• Decarbonization
• +3 more

Alice Di Bella is a Researcher at the Sustainable Earth Modelling Economics Division of the European Institute on Economics and the Environment (EIEE), part of the CMCC Foundation in Milan, Italy. Her work focuses on energy system modeling for decarbonization strategies across multiple sectors. Education: Bachelor and Master in Energy Engineering, Politecnico di Milano Erasmus exchange at Universidad Politécnica de Madrid Master thesis: 'Multi-Objective Optimization to identify carbon neutrality scenarios for the Italian electric system' (conducted with Eurac Research, Bolzano) Research Focus: Di Bella specializes in applying energy system modeling to climate mitigation challenges. Her expertise spans demand-side policy design for buildings/transport decarbonization, power system resilience under climate change, and energy crisis response strategies. She develops optimization frameworks for carbon neutrality pathways with emphasis on Italian and European energy systems. Publication Trends: Recent work demonstrates consistent focus on demand-side interventions for rapid emissions reductions, climate-risk-informed grid planning, and high-resolution weather-to-energy modeling. Her research bridges technical energy system analysis with macroeconomic policy evaluation, particularly evident in contributions to 'Fit for 55' impact assessments. Affiliations: Core member of EIEE's Sustainable Earth Modelling team at CMCC Foundation, collaborating with Eurac Research and Politecnico di Milano. Maintains active industry engagement through policy-relevant modeling for Italian energy transition.

Diogo F. PachecoView profile

Research Fellow

• Computational Social Science
• Complex Systems
• Social Media Analysis
• +4 more

Diogo F. Pacheco is a Postdoctoral Fellow at the Center for Complex Networks and Systems Research, Indiana University Bloomington, supervised by Fil Menczer and Alessandro Flammini. He maintains dual affiliations as a member of the BioComplex Lab and an invited researcher at the Computational Intelligence Research Group, University of Pernambuco. His educational background includes: Ph.D. in Computer Science from Florida Institute of Technology (2017), dissertation: Information Densification of Social Constructs via Behavior Analysis of Social Media Users Master's in Computer Science from University of Pernambuco (2008), thesis: An Evolutionary Multi-Objective Approach to Decision Support in Sugarcane Harvest Bachelor's in Computer Science from University of Pernambuco (2006), thesis: Decision Support in Intelligent Systems for Agricultural Crops Dr. Pacheco specializes in computational social science and complex systems modeling, with core expertise in social media analysis for societal insights. His methodology combines data-driven behavioral modeling , network science , and machine learning to examine phenomena including language spreading, football supporter dynamics, organ donation awareness, and social disorganization. Early work focused on multi-objective optimization for agricultural decision support, evolving into contemporary research on digital footprints as proxies for human behavior. Analysis of his 15 most recent publications reveals a clear trajectory from agricultural optimization (2006-2008) to computational social science (2014-2019). Recent work demonstrates sophisticated applications of Twitter data for cross-disciplinary studies in public health, criminology, and sports analytics, often correlating digital behavior with socio-economic indicators. His GitHub ecosystem simulations for DARPA represent cutting-edge multi-agent modeling of online communities. Dr. Pacheco actively collaborates with research groups including the BioComplex Lab (Indiana University) and University of Pernambuco's Computational Intelligence Research Group. His current projects involve large-scale social media simulations, human mobility modeling, and social disorganization metrics using location-based data. Prior to his Ph.D., he gained five years of industry experience following his Master's degree.