Find Academic Supervisors

Mihai Teodor LazarescuView profile

Associate Professor

• Artificial intelligence
• Capacitive sensors
• Embedded software
• +10 more

Dr. Mihai Teodor Lazarescu is an Associate Professor at the Department of Electronics and Telecommunications (DET), Politecnico di Torino , where he contributes to research and teaching activities. He is also a member of the PolitoBIOMed Lab (Biomedical Engineering Lab) and the Ambient Sensing and Processing research group. Scientific Affiliation: IEEE Member (2019-present) Editorial Roles: Guest Editor for SENSORS, ELECTRONICS, and ACM Transactions on Embedded Computing Systems His research interests focus on hardware acceleration for machine learning algorithms, particularly using FPGAs for data center and embedded applications. He works on high-level synthesis optimization flows, capacitive sensor design for indoor monitoring, and low-power embedded systems . His work intersects Internet of Things , Wireless Sensor Networks , and Machine Learning with applications in human localization, environmental monitoring, and industrial automation. Recent publications demonstrate expertise in neural network optimization , DSP resource sharing , and multi-FPGA allocation . His teaching spans Applied Electronics , Digital Electronic Design , and Embedded Systems Optimization across bachelor's and master's programs in Electronic Engineering and Computer Engineering . Patents: Noise cancellation for single-plate capacitive sensors Capacitive sensor for space change detection Projects: Scientific Manager for Horizon 2020-S2RJU project (2018)

• 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.

• Electronic design automation
• Embedded systems
• Energy efficient electronic systems
• +16 more

Daniele Jahier Pagliari is an Associate Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where he is also a member of the Interdepartmental Center PEIC - Power Electronics Innovation Center. He is actively involved in teaching and research, focusing on embedded systems, electronic design automation, and machine learning for edge computing. He teaches courses such as Optimized Execution of Neural Networks at the Edge, Machine Learning for IoT, and Hardware/Software Codesign of Flexible Computing Systems for Edge AI across various engineering programs including Computer Science and Systems Engineering, Data Science, and Automotive Engineering. His research interests span electronic design automation, embedded systems, energy-efficient computing, low-power design, and machine learning. He is particularly engaged in applying machine learning techniques to improve the design and performance of digital and analog circuits, with a focus on edge AI applications. His work aligns with key scientific areas including computer architecture, cyber-physical systems, and scientific computing. The recent publications highlight a strong trend in optimizing deep learning models for resource-constrained environments, accelerating neural network inference on ultra-low-power devices, and integrating physics-based models with AI for battery state estimation. There is also significant focus on using machine learning to enhance electronic design automation, particularly for analog and mixed-signal circuits, reflecting a convergence of AI and hardware design. He leads and participates in several high-impact research projects, including EU-funded initiatives like HAL4SDV, ISOLDE, TRISTAN, and AMBEATion, as well as commercial projects such as MASAI and software platform development for production support. He serves as the Scientific Responsible or Director in multiple projects, demonstrating leadership in both academic and industrial research contexts. He supervises multiple PhD students in the Computer and Systems Engineering program, including Luca Benfenati, Mohamed Amine Hamdi, Beatrice Alessandra Motetti, Giovanni Pollo, and Matteo Risso, whose research topics include latency-optimized inference, compiler optimization for edge devices, and hardware-aware deep learning design. He is also involved in patent development, notably for an instrumentation method to dynamically modify circuit precision. He is a member of the College of Computer, Film and Mechatronics Engineering and contributes to various degree programs. His work supports UN Sustainable Development Goals related to good health, affordable and clean energy, industry innovation, and sustainable cities.

• Operations Research
• Stochastic Programming
• Energy Systems
• +5 more

Patrizia Beraldi is an Associate Professor of Operations Research at the Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria. She has been with the university since 2001, holding roles from Research Fellow to her current position. With over 80 publications, her work focuses on stochastic programming, probabilistic constraints, and their applications in energy systems, healthcare, finance, and logistics. Education: PhD in System Engineering and Computer Science (2000), University of Calabria Degree in Management Engineering (cum laude, 1995), University of Calabria Her research spans solution methods for network flow problems, stochastic programming with recourse, and probabilistic constraint modeling in diverse fields like energy procurement, drone routing, and financial optimization. Recent publications emphasize stochastic bilevel models for energy tariffs and ML-enhanced optimization frameworks. Scientific Awards: Best paper award, 2015 IMA Journal of Management Mathematics Best presentation award, ICEER Conference 2017 Best poster award, ICORES 2018 She supervises numerous master's and PhD students and leads the Financial Engineering and Risk Management Laboratory at DIMEG. Her editorial roles include Associate Editor for TOP , Algorithms , and IMA Journal of Management Mathematics .

• Game Theory
• Control Systems
• Multi-agent Systems
• +4 more

Paolo Scarabaggio is an Assistant Professor (RTDA) at the Decision and Control Laboratory of Polytechnic University of Bari, Italy. He received his Ph.D. in Electrical and Information Engineering from the same institution and completed a research visit at the Delft Center for Systems and Control, Technical University of Delft in 2019. His academic work spans multiple high-impact publications across IEEE journals and conferences, with a focus on interdisciplinary applications of control theory. His research interests center around modeling, optimization, game theory, and control of complex multi-agent systems , with practical applications in energy distribution systems, social networks, warehouse automation, and collaborative robotics. His work demonstrates strong interdisciplinary connections between theoretical control concepts and real-world industrial applications, particularly in Industry 4.0 contexts. Dr. Scarabaggio's publications reveal a consistent trajectory of increasingly sophisticated applications of game theory and optimization techniques to emerging challenges in energy systems, logistics automation, and human-robot interaction. His research shows particular strength in developing mathematically rigorous frameworks that address practical constraints in real-world systems. 2022 IEEE CSS Italy Best Young Author Journal Paper Award As an educator, Dr. Scarabaggio teaches courses including Analisi e Simulazione dei Sistemi, Fondamenti di Automatica, and Game Theory for Controlling Autonomous Systems. His research collaborations span multiple institutions and industries, with frequent co-authorship with researchers from Polytechnic University of Bari and international partners. His work often addresses practical implementation challenges while maintaining theoretical rigor, making significant contributions to both academic knowledge and industrial applications.

• Computer Architecture
• High Performance Computing
• Parallel Programming
• +5 more

Andrea Marongiu is an Associate Professor at the Department of Physical, Computer and Mathematical Sciences at the University of Modena and Reggio Emilia, specifically affiliated with the Mathematics department. He maintains an active research profile while teaching multiple courses in computer architecture and parallel systems. His research interests span computer architecture, high performance computing, parallel programming, and embedded systems. Marongiu focuses particularly on memory systems, heterogeneous computing architectures, FPGA-based acceleration, and real-time performance analysis. His work bridges theoretical foundations with practical implementation challenges in modern computing systems, with special attention to predictable execution models and quality of service guarantees. Analysis of his recent publications reveals a strong emphasis on memory bandwidth management in heterogeneous systems, particularly focusing on FPGA-based architectures and multicore SoCs. His research trajectory shows consistent work in memory interference analysis, PREM (Predictable Execution Model) scheduling techniques, and fine-grained QoS control mechanisms. The publications demonstrate a progression from general parallel programming concepts toward increasingly specialized techniques for resource-constrained environments like autonomous vehicles and edge computing devices. Marongiu teaches several advanced computer science courses including Computer Architecture I & II, Compilers, High Performance Computing, and Electronic Calculators across multiple degree programs. His teaching approach emphasizes both theoretical foundations and practical implementation, with a focus on RISC-V architecture and modern parallel programming techniques. The course materials indicate he incorporates hands-on laboratory work as an essential component of his pedagogy, particularly in areas like compiler construction and parallel programming.

Antonio VicinoView profile

Professor

• Control Systems
• Robust Control
• System Identification
• +6 more

Antonio Vicino is a Full Professor of Control Systems at the Department of Information Engineering and Mathematical Sciences, University of Siena, Italy. He has held several significant leadership positions including Head of Department (1996-1999), Dean of the Engineering Faculty (1999-2005), Director of the Center for Complex Systems Studies (1999-2012), Director of the PhD Program in Information Engineering and Science (2016-2021), and President of the Italian University Council (2019-2023). Professor Vicino earned his Laurea Degree in Electrical Engineering with honors from Politecnico di Torino in 1978, followed by fellowships with CSS-ATA (1979-1980) and ENEL-Politecnico di Torino (1981-1982). His academic career progressed from Assistant Professor at Politecnico di Torino (1983-1987) to his current position at the University of Siena. His research focuses span Control Systems, Robust Control, System Identification, Smart Grids, Energy Systems, Systems Biology, Time Series Analysis, Nonlinear Systems, and Applied System Modeling. His work has evolved from foundational theoretical contributions in robust control to practical applications in energy systems and biological modeling. Recent work demonstrates a strong emphasis on smart grids, electric vehicle integration, and energy storage optimization. Professor Vicino's extensive publication record reveals an evolution from theoretical control systems research to applied energy systems work. His recent publications (2018-2024) primarily focus on smart grid applications, electric vehicle integration, and energy storage optimization, reflecting the growing importance of renewable energy integration and sustainable technologies. Fellow of the IFAC (2008) Life Fellow of the IEEE (2000) Associate Editor of Automatica (1995-present) Associate Editor at Large of IEEE Transactions on Automatic Control (1998-2008) Professor Vicino has advised 23 PhD students and 10 Research Associates, along with over 150 Master's theses. His research has been supported by numerous grants including EC-FP7 Project ADDRESS (2008-2013), MPS Foundation projects, and multiple national government research projects. He has established four research laboratories: Mobile Robotics Lab, Haptics and Medical Robotics Lab, Process Control Lab, and Automatic Control Telelab (ACT), which has received international recognition including featuring on the cover of IEEE Control Systems Magazine.

• Machine Learning
• Artificial Intelligence
• Anomaly Detection
• +3 more

Alessio Mascolini serves as a Research Fellow at the Interuniversity Department of Territorial Sciences, Planning and Policies (DIST) and as an External Lecturer at the Department of Control and Computer Science (DAUIN) at Polytechnic University of Turin. His academic work bridges theoretical machine learning research with practical industrial applications, particularly in manufacturing and engineering contexts. His research interests focus on Machine Learning applications in data-limited scenarios , with specialization in anomaly detection systems , edge computing implementations , and generative modeling approaches . His work demonstrates strong connections between theoretical AI advances and real-world industrial challenges, particularly in semiconductor manufacturing and robotic production lines. Analysis of his recent publications reveals a clear trajectory toward developing resource-efficient AI systems for industrial environments, with increasing sophistication in combining neuro-symbolic approaches with diffusion models. His research consistently addresses the challenge of implementing robust machine learning solutions in resource-constrained edge computing environments. As an educator, Mascolini has served as Course Collaborator for multiple programs since 2020/21, demonstrating consistent engagement with student instruction across various engineering disciplines. His teaching portfolio shows strong alignment with his research interests, particularly in machine learning applications.

• Autonomous vehicles
• Control theory
• Differential games
• +11 more

Michele Pagone is a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) at Politecnico di Torino. His academic role focuses on advanced control theory and its applications in aerospace and automotive engineering. Current academic rank: Assistant Professor Department: DET Invited member of the College of Computer, Film, and Mechatronics Engineering Invited member of the College of Mechanical, Aerospace, and Automotive Engineering Research Interests His research spans autonomous and electric vehicles, nonlinear and optimal control, robust control, and space trajectory optimization. He integrates machine learning and data science into control systems for sustainable mobility solutions. Key areas: Control Theory, Differential Games, Lyapunov Stability, Mechatronics Applications: Aerospace (orbital maneuvers), Automotive (adaptive cruise control), Energy Systems Academic Collaborations Michele supervises PhD students Lucrezia Lovaglio and Lorenzo Calogero, focusing on advanced model predictive control strategies for sustainable mobility. His work bridges theoretical control algorithms with practical implementations in electric vehicles and spacecraft systems. Research Groups Member of the Automatica research group at DET Collaborates on interdisciplinary projects involving Big Data, Neural Networks, and Systems Automation

Marco GalloView profile

Research Fellow

• Electrical Engineering
• Energy Systems Optimization
• Renewable Energy Integration
• +3 more

Marco Gallo is a Research Fellow at the Department of Naval, Electrical, Electronic, and Telecommunications Engineering (DITEN) of the University of Genoa, Italy. His work focuses on advanced optimization techniques for maritime power systems and smart port infrastructures. Key Research Areas: Electrical Engineering, Energy Systems Optimization, Renewable Energy Integration Current Projects: Shipboard Microgrid Optimization, Battery Storage Sizing, Smart Port Development His recent publications demonstrate expertise in applying computational methods like Mixed-Integer Linear Programming and Model Predictive Control to zero-emission maritime systems. The research spans power quality analysis, carbon intensity assessment, and multi-energy port design with vehicle charging capabilities.

Antonello GiannitrapaniView profile

Associate Professor

• Smart Grid
• Multi-agent Systems
• Aerospace Applications
• +3 more

Antonello Giannitrapani is an Associate Professor at the Department of Information Engineering and Mathematics at the University of Siena, Italy. He is affiliated with the Systems and Control Group and focuses on research in smart grids, multi-agent systems, aerospace applications, and mobile robots. His work emphasizes distributed control, energy storage optimization, and SLAM (Simultaneous Localization and Mapping) for robotics. Giannitrapani holds a PhD and MSc in Information Engineering from the University of Siena (2004 and 2000, respectively). Research interests include: Smart grid optimization for voltage control and energy storage systems Distributed algorithms for multi-agent systems and consensus problems Aerospace control, including spacecraft guidance and rendezvous Mobile robotics with a focus on SLAM, cooperative systems, and haptic interfaces Selected projects include CIDCABIP (2018-2020), addressing data collection and predictive maintenance, and participation in initiatives like the Energy-Aware Factory of the Future (2020-2023). Teaching roles span courses in Data Analysis, Decision Analysis, and Robotics (in Italian). Key contributions include: Optimal energy storage placement in low-voltage grids A distributed asynchronous method of multipliers for nonconvex optimization Variable-horizon guidance for spacecraft docking Development of remote labs for multi-robot experiments His research integrates control theory, optimization, and robotics to address challenges in energy systems, autonomous systems, and distributed intelligence.

PAOLA PIERLEONIView profile

Researcher

• Telecommunications
• IoT (Internet of Things)
• Wearable Sensors
• +3 more

Prof. Paola Pierleoni is a Researcher at the Department of Information Engineering of the University of Ancona and Marche Polytechnic (UNIVPM) . Her work focuses on IoT, telecommunications, and wearable sensor systems applied to healthcare, disaster management, and industrial automation. She leads projects in real-time data processing , embedded machine learning , and cross-protocol communication , with notable contributions to earthquake early warning systems and diabetes therapy optimization. Her research combines telecommunications engineering with health monitoring applications , including gait analysis for Parkinson’s disease, stress assessment via wearable sensors, and underwater diver monitoring. She has developed low-cost IoT solutions for infrastructure monitoring (e.g., water leak detection) and AI-driven algorithms for medical diagnostics and industrial predictive maintenance. Key projects include: Earthquake Early Warning systems using MEMS accelerometers AIoT frameworks for ambient assisted living (AAL) Bluetooth mesh network optimization for latency-sensitive applications Machine learning models for diabetes therapy personalization Her work emphasizes interdisciplinary collaboration , integrating engineering, computer science, and medical expertise. Office hours are held on Mondays 9:00–13:00 at the Department of Information Engineering, Via Brecce Bianche.

• Control Systems
• Model Predictive Control
• Robust Control
• +3 more

Sampath Kumar Mulagaleti is an Assistant Professor at the IMT School for Advanced Studies Lucca, part of the Faculty. His research focuses on control systems, with a strong emphasis on model predictive control (MPC), robust control, and linear parameter-varying (LPV) systems. He investigates invariant sets, data-driven methods, and optimization techniques to enhance system stability and performance under constraints. His work spans theoretical advancements and practical applications in control engineering, including decentralized MPC, dynamic output disturbance modeling, and robust regulation of linear systems. Notable contributions include configuration-constrained tube MPC algorithms and methods for computing safe disturbance sets using implicit RPI sets. Dr. Mulagaleti’s publications highlight a consistent focus on bridging theoretical control frameworks with real-world engineering challenges. His research often intersects with systems engineering, mathematics, and automation, addressing complex scenarios such as payload transportation by multiple vehicles and parameter-dependent control strategies. No scientific awards or grants are explicitly mentioned in the provided texts. His advising activities and student mentorship details are currently unavailable.

• Tribology
• Mechanical Engineering
• Pneumatic Systems
• +3 more

Luigi Mazza is an Associate Professor in the Department of Mechanical and Aerospace Engineering (DIMEAS) at the Polytechnic University of Turin. His academic work focuses on tribology, pneumatic systems, and mechanical engineering, with particular expertise in friction phenomena and seal technology. Dr. Mazza's primary research interests include: Lifespan and wear of pneumatic seals Friction phenomena in seals Tribological measurements and analyses with pin-on-disc tribometers Controlled pneumatic systems His recent research has focused on the intersection of traditional mechanical engineering with modern computational approaches. Dr. Mazza has been investigating the application of machine learning for predictive maintenance in industrial systems, developing advanced modeling techniques for pneumatic components, and refining measurement methodologies for wear analysis in mechanical components. His work bridges theoretical understanding with practical engineering applications, particularly in the field of tribology and fluid power systems. Dr. Mazza has received the Frank P. Bussick 2012 award from the Society of Tribologists and Lubrication Engineers (STLE) USA, recognizing his contributions to the field of tribology. As an academic advisor, Dr. Mazza has supervised PhD students including Edoardo Goti (35th cycle, 2019-2023), whose thesis focused on "Graphene in Tribological Applications: Experimental Approaches." He has secured research funding through various competitive grants and commercial contracts, including projects such as SURF-TRIB (2024-2025), FreME (2021-2023), POCDE (2014-2016), and ISECOMP (2009-2011). Dr. Mazza is an active member of the Automation and Robotics research group within DIMEAS, contributing to both fundamental research and applied industrial projects in mechanical and aerospace engineering.

• Sleep Analysis
• Fault Injection
• Fault Tolerance
• +7 more

Massimo Violante is a Tenured Associate Professor at the Department of Control and Computer Engineering (DAUIN) , Politecnico di Torino. He serves as Deputy Director of the Polytechnic School and holds leadership roles in multiple research centers including the Interdepartmental Center PEIC (Power Electronics Innovation Center) and the Spin-Off/Start-Up Evaluation Commission . With expertise in embedded systems , functional safety , and sleep analysis , he bridges automotive engineering with biomedical applications through his research. Scientific Branch: IINF-05/A - Information Processing Systems ERC Sectors: PE7_11 (Components/Applications), PE6_1 (Computer Architecture), PE6_3 (Software Engineering) SDG Alignment: Goal 3 (Health) and Goal 9 (Innovation) His research focuses on fault tolerance , driver safety aid systems , and functional safety standards like ISO26262. He leads groundbreaking work in real-time sleep prediction and automotive electronics reliability , including patented solutions for drowsiness detection and safety-critical systems . Recent projects span from GreenChips-EDU (education ecosystem for sustainable microelectronics) to HiEFFICIENT (wide band gap power electronics for electric vehicles). Violante has supervised numerous PhD students including Sara Groppo (health monitoring for newborns), Michele Guagnano (sleep monitoring), and Pietro D'Agostino (Industrial IoT solutions). He has received prestigious recognition including the IEEE Best Paper Award (2005) and leadership roles as Program Chair for major symposiums like the IEEE European Test Symposium . His teaching portfolio includes foundational courses in Model-based Software Design , Operating Systems for Embedded Systems , and Technologies for Autonomous Vehicles . Major Projects: EU-funded: Cynergy4MIE (2024-2027), ShapeFuture (2024-2027), A-IQ Ready at EDGE (2023-2025) Nationally/Regionally funded: EMC2 (2014-2017), MIE (2014-2017), FELIX (2007-2010) Commercial contracts: Automotive Academy (2019-2020), Vodafone IoT Academy courses (2019-2021), Smartwatch-based drowsiness detection systems As a prolific inventor, he holds multiple international patents in driver state monitoring , microsleep prediction , and emergency intervention analysis . His work demonstrates a unique convergence of automotive engineering , biomedical applications , and high-reliability computing .