Find Academic Supervisors

• Aeroacoustics
• Computational Fluid Dynamics (CFD)
• Fluid Dynamics
• +6 more

Renzo Arina is a Tenured Associate Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at the Polytechnic University of Turin . His academic career spans decades of contributions to Aeroacoustics and Computational Fluid Dynamics (CFD) . Research Interests : Numerical simulation of flow-induced noise Drag reduction techniques for vehicle optimization Computational methods for aeroacoustic modeling Boundary layer dynamics and separation control Research Projects : Erasmus Mundus Master in Aeroacoustics (2024–2025): Member of research group Aerodynamic Optimization of Vehicles (2016): Scientific Director for industrial efficiency Detailed Numerical Modeling of Airborne Sound Field (2013–2016): EU-funded research Discontinuous Galerkin Method for Aeroacoustics (2010–2012): National PRIN project Advising : Supervises PhD candidates Daniele Fabbri (Mechanical Engineering, 2023–ongoing) and Francesco Bellelli (Aerospace Engineering, 2022–ongoing) Labs & Teams : Member of the Boundary Layer Flow, Separation and Control research group at DIMEAS

• Digital Circuits and Systems
• Energy-Efficient Circuits and Systems
• Embedded Systems
• +7 more

Massimo Poncino is a Full Professor at the Department of Control and Computer Science (DAUIN) within the Faculty of Engineering at Politecnico di Torino. He serves as Scientific Advisor for the STMicroelectronics partnership and coordinates basic engineering subjects. A Senior Member of IEEE since 2012 and Fellow since 2012, he has served on editorial boards for IEEE Transactions on Computer-Aided Design, IEEE Design & Test of Computers, and ACM Transactions on Design Automation. Education: Laurea in Electronic Engineering (1989) and PhD in Computer and Systems Engineering (1993) from Politecnico di Torino Academic Career: Visiting Scientist University of Colorado (1993-1994), Researcher at Politecnico di Torino (1995-2001), Associate Professor at University of Verona (2001-2004), Full Professor at Politecnico di Torino (2006-present) His research focuses on energy-efficient digital systems , including design automation of SoCs, hardware-aware AI, battery management, cyber-physical systems, and embedded systems. Recent publications highlight advancements in digital twins for batteries , low-power neural network deployment , and IoT privacy . Scientific Awards: Recognition of Service Award - ACM (2013) Certificate of Appreciation - IEEE Circuits and Systems Society (2006, 2008, 2009) IEEE Fellow (2012-) Research Involvement: EU H2020, VI/VII Framework Programs evaluator Scientific Director for projects: Approxim@ction, EMBAI, DISLO-MAN, DAMASCO Member of EDA research group Teaching: Course director for Energy Management for IoT (2019-2025) Lecturer for Computer Science courses (2003-2025)

• Flow in underground porous media
• Geoenergy
• Underground energy storage
• +12 more

Dario Viberti is a Full Professor at the Polytechnic University of Turin, affiliated with the Department of Environmental, Land and Infrastructure Engineering (DIATI). He is a member of the Interdepartmental Center Ec-L - Energy Center Lab and actively contributes to the Master's and Continuing Education School. He serves as Coordinator for both Master’s levels I and II of the Natural Resources Development and Storage program and participates in doctoral colleges for Civil and Environmental Engineering and Materials, Sustainable Processes, and Systems for the Transition. His research focuses on flow in underground porous media , geoenergy , and underground energy storage , particularly hydrogen and CO₂ storage. His expertise spans numerical modeling, reservoir engineering, fluid mechanics, well testing, and energy transition systems. He is involved in commercial research projects with ENI Corporate University and leads initiatives related to sustainable energy applications. His recent publications reveal a strong trend in underground hydrogen storage , biogeochemical modeling , pore-scale simulation , and PVT analysis of gas mixtures . These works emphasize safety, efficiency, and long-term integrity of subsurface storage systems using advanced computational and experimental methods. Scientific Manager, Underground CO2 Storage Project (2022) Scientific Manager, Reservoir Modeling Project (2022) Coordinator, Master’s Program in Natural Resources Development and Storage Member, SEASTAR Competence Center Committee (2020–2025) He mentors several PhD students, including Michel Tawil, Marialuna Loffredo, Alice Raeli, and Alice Massimiani, supporting research in microfluidics, Lattice Boltzmann simulations, and thermodynamic characterization. He teaches courses such as Numerical Modeling for Multiphase Flow , Underground Energy Storage , and Well Logging and Testing across multiple degree programs. His work aligns with UN SDGs 7 (Affordable and Clean Energy), 9 (Industry, Innovation, and Infrastructure), and 13 (Climate Action).

• Manufacturing Technologies
• Additive Manufacturing
• Injection Molding
• +7 more

Leonardo Orazi is a Full Professor at the University of Modena and Reggio Emilia's Department of Engineering Sciences and Methods. He specializes in advanced manufacturing technologies, particularly laser processing, polymer engineering, and biomedical surface functionalization. His teaching roles include courses on Smart Manufacturing, Injection Molding, and Additive Manufacturing in Digital Automation and Mechatronic Engineering programs. Research focuses on laser-induced periodic surface structures (LIPSS), material characterization, and micro/nanostructuring for biomedical and industrial applications. Develops innovative manufacturing processes for antibacterial surfaces, microfluidic devices, and enhanced material properties. Research Interests: Laser texturing, polymer processing, surface engineering, additive manufacturing, and simulation-driven design. Labs/Teams: Active in laser-matter interaction research and collaborative projects on biomaterial functionalization. His work bridges computational modeling (e.g., Moldflow simulations) with experimental validation. Publications: Over 40 peer-reviewed articles since 2010, emphasizing laser-based manufacturing advancements, polymer molding optimization, and biomedical material surface treatments. Recent work includes antibiofouling polymer functionalization via ultrafast lasers and fiber orientation modeling in composites.

Giovanni RussoView profile

Professor

• Computational Fluid Dynamics
• Numerical Methods for Conservation Laws
• Numerical Methods for Kinetic Equations
• +5 more

Giovanni Russo is a Full Professor of Numerical Analysis at the Department of Mathematics and Computer Science, University of Catania, Italy . He coordinates the PhD program in Pure and Applied Mathematics and has been a visiting scholar at institutions including Courant Institute, University of California, Los Angeles, University of Michigan, and University of Bordeaux. His career spans over four decades across academia and research institutions. Education: PhD in Physics (1986, University of Catania), Laurea in Nuclear Engineering (1982, Politecnico di Milano). Research Interests: Russo specializes in Computational Fluid Dynamics , Numerical Methods for Conservation Laws , and Kinetic Equations . His work includes asymptotic preserving schemes , IMEX methods , semi-Lagrangian schemes , and high-order numerical techniques for PDEs with applications to fluid dynamics, plasma physics, and multiscale modeling. Scientific Trends: Recent publications focus on modeling epidemic dynamics , kinetic equations for inert mixtures , semi-Lagrangian methods , and uncertainty quantification in quantum systems. These works reflect his expertise in high-order numerical schemes , multiscale analysis , and applied mathematical modeling . Scientific Awards: CNR-NATO Fellowship (1987) Advising and Grants: Russo has supervised nine PhD students and served as Principal Investigator (PI) for major projects including MOSCOVID (modeling COVID-19) and ModCompShock (Horizon 2020 Marie Curie project). He has also organized international conferences like the 18th European Conference on Mathematics for Industry with 370 participants. Labs and Teams: Russo collaborates with research groups at the University of Catania and has been a visiting researcher at Courant Institute, University of Michigan, and GSSI L’Aquila. He contributes to journals as an editor and reviewer, including SIAM Journal of Numerical Analysis and Journal of Computational Physics .

• Machine Learning
• Signal Processing
• Telecommunications
• +3 more

Andrea Migliorati is a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino. He specializes in telecommunications and industrial/information engineering (IINF-03/A - Area 0009), with a focus on machine learning applications in signal processing and neural network optimization. Academic Role: Assistant Professor Department: Electronics and Telecommunications Research Branch: Telecommunications (IINF-03/A) University: Politecnico di Torino Migliorati's research explores neural network sparsification, quantization techniques, and robust training methods for secure systems. His work bridges telecommunications engineering with modern machine learning, targeting applications in wearable biometrics and edge computing. Key trends include: Advancing sparse trainable neural networks through concave regularization Developing ternary/binary quantization approaches for model compression Enhancing gait identification systems for wearable devices Improving adversarial robustness via Gaussian class-conditional training

Marta CatilloView profile

Researcher

• Cybersecurity
• Machine Learning
• Intrusion Detection Systems
• +3 more

Marta Catillo is a Researcher at the Department of Engineering (DING) of the University of Sannio (UNISANNIO) . She specializes in Cybersecurity , with focus on Machine Learning applications for intrusion detection , IoT security , and cloud auto-scaling mechanisms . Her research addresses challenges in Denial of Service (DoS) mitigation , anomaly detection , and deep learning architectures for security. Teaching: [803004] PROGRAMMING 1 for Electronic and Biomedical Engineering students (2025 cohort) Contact: Office hours: Thursdays 3-5 PM, Room 23, Bosco Lucarelli Palace Research trends: From 2019-2025 publications, her work spans adversarial attack resistance , collective anomaly detection , outlier-aware architectures , and empirical analysis of defense mechanisms , with recurring collaborations with Antonio Pecchia , Umberto Villano , and Massimiliano Rak . Key methodologies include deep autoencoders , hybrid detection systems , and measurement-based security evaluation . Technical Contributions: Developed the ZED-IDS framework for zero-day threat detection, MultiCIDS for multivariate time series intrusion detection, and DEFEDGE for edge-cloud security testing.

Sebastiano VASCONView profile

Associate Professor

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +4 more

Sebastiano Vascon is an Associate Professor at Ca' Foscari University of Venice's Department of Environmental Sciences, Computer Science and Statistics (DAIS), and affiliated with the European Center for Living Technology. He earned his PhD in 2016 from the Italian Institute of Technology and University of Genoa, focusing on evolutionary game theory in pattern analysis and computer vision. His postdoctoral work spanned institutions like the Technical University of Munich and ETH Zurich, where he specialized in Active Learning and multi-object tracking. His research merges AI with interdisciplinary challenges, including climate change, environmental science, and cultural heritage preservation. Key areas include graph neural networks, computer vision, and game-theoretic models. He leads projects like RePAIR (AI for cultural heritage reassembly) and EasyWalk (AI-driven mobility solutions), and contributes to initiatives like MEMEX (digital storytelling). Teaching spans courses in Deep Learning, Machine Learning for Environmental Applications, and AI in Cultural Management. Research projects include: RePAIR: AI-driven 3D puzzle solving for artifact reconstruction EasyWalk: Socially-aware navigation systems MEMEX: AI for inclusive digital storytelling Climate modeling with IceBoost framework Publications highlight innovations in trajectory forecasting, environmental risk assessment, and graph-based methods. He actively reviews for top conferences (CVPR, ECCV) and journals.

• Fluid Dynamics
• Computational Fluid Dynamics
• Hypersonic Flows
• +5 more

Giuseppe Pascazio serves as a Full Professor in the Department of Mechanics, Mathematics & Management at the Polytechnic University of Bari, Italy. His research spans computational fluid dynamics with dual focus on aerospace applications and biomedical engineering, particularly in hypersonic flow phenomena and microwave ablation technologies for cancer therapy. His primary research interests include fluid dynamics, computational methods for high-enthalpy flows, thermochemical non-equilibrium modeling, turbulent boundary layer analysis, and biomedical device optimization. Pascazio develops advanced numerical techniques including high-order schemes, state-to-state kinetics implementations, and GPU-accelerated solvers to address complex flow physics in atmospheric entry and medical applications. His work bridges fundamental gas dynamics with practical engineering solutions for spacecraft thermal protection and minimally invasive cancer treatments. Analysis of his recent publications (2021-2025) reveals three dominant research thrusts: (1) High-fidelity simulation of hypersonic flows with detailed chemistry using state-to-state kinetics, (2) Development of robust numerical methods for shock-capturing in thermochemically non-equilibrium flows, and (3) Biomedical applications focusing on microwave ablation probe design and microcapsule transport in vascular systems. His aerospace work emphasizes atmospheric reentry physics while biomedical research targets cancer therapy optimization. Pascazio has participated in significant research projects including "PrInCE" (Innovative Processes for Energy Conversion) and "INNOVHEAD" (Advanced technologies for reduction of polluting emissions in Heavy Duty engines). His collaborative work involves industrial partnerships in aerospace and medical device sectors, though specific grant details beyond project names aren't provided. He maintains active research output with over 50 publications demonstrating consistent contributions to high-speed aerodynamics and biomedical fluid dynamics.

ANDREA DI DONATOView profile

Associate Professor

• Electromagnetic Fields
• Nanotechnology
• Biomedical Engineering
• +4 more

Prof. Andrea Di Donato is an Associate Professor at the Department of Information Engineering, University of Ancona and Marche Polytechnic (UNIVPM). His research focuses on electromagnetic fields, nanotechnology, and biomedical engineering with particular emphasis on scanning microwave microscopy, optical holography, and graphene characterization. He holds a position within the 'Electromagnetic Fields' research group, contributing to advancements in materials science, photonics, and biomedical imaging. His work integrates cutting-edge techniques such as inverted scanning microwave microscopy for nanoscale biological imaging and development of photo-mobile polymer films for light-controlled actuation systems. Key projects include holographic grating sensors, tunable lasers, and studies on light-induced effects in 2D materials like graphene oxide. Prof. Di Donato's laboratory specializes in multi-disciplinary approaches combining microwave engineering, optical physics, and nanotechnology. His contributions span from theoretical formulations (e.g., conductivity models for 2D materials) to practical applications like biomedical diagnostics and advanced material fabrication.

Marco GrangettoView profile

Professor

• Computer Vision
• Image Processing
• Artificial Intelligence
• +5 more

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

Roberto MenichettiView profile

Researcher

• Theoretical Physics
• Computational Physics
• Statistical Mechanics
• +7 more

Roberto Menichetti is a theoretical physicist at the University of Trento , affiliated with the Department of Physics and the VARIAMOLS research group . His work integrates computational physics, statistical mechanics, and machine learning to model biomolecules and soft matter systems. Prior roles include postdoctoral research at the Max Planck Institute for Polymer Research (2016-2018) and academic training at the University of Rome 'La Sapienza'. PhD in Physics (La Sapienza, 2016) BSc/MSc in Physics (La Sapienza, 2012) His research spans: Coarse-Graining Methods for polymers, colloids, and biomolecules High-throughput Molecular Dynamics Simulations in drug-membrane permeability studies Integration of Machine Learning with physical models for multi-scale analysis Applications in Biophysics (e.g., chromatin condensates, protein dynamics) Article trends highlight his focus on computational frameworks like EXCOGITO and theoretical investigations of non-equilibrium systems and allosteric regulation . He actively develops tools for systematic model reduction and conformational sampling in biomolecular systems. Contact: roberto.menichetti@unitn.it

Tommaso GrigolettoView profile

Adjunct Professor

• Quantum Mechanics
• Control Engineering
• Theoretical Physics
• +3 more

Tommaso Grigoletto is an Adjunct Professor at the Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, and a Postdoctoral Fellow at the Department of Information Engineering, University of Padua. His research focuses on theoretical and numerical methods for complex dynamical systems, particularly quantum and classical probability models. He is part of the Quantum Dynamics, Information and Control group at the University of Padua, led by Prof. Francesco Ticozzi. Education: Ph.D. and Master's degrees in Information Engineering from the University of Padua. Research interests include quantum system control, model reduction techniques, and feedback stabilization strategies. He teaches 'Fundamentals of Automatic Controls' at Ca' Foscari University (2024-2025). His articles explore quantum model reduction, feedback control for quantum systems, and stochastic dynamics. He is actively mentoring students interested in control engineering and quantum systems simulations. Open to collaborative research and thesis supervision in these areas. Professional networks include the Quantum Dynamics group at Padua and academic affiliations with both universities. Engaged in open-source projects like an arXiv update bot for tracking research trends.

Giorgio VinciguerraView profile

Research Fellow

• Data Compression
• Algorithm Engineering
• Learned Data Structures
• +3 more

Giorgio Vinciguerra is a Research Fellow (RTD-A) at the Department of Computer Science of the University of Pisa since January 2023, and a member of the A³ Lab. His research focuses on compact data structures, data compression, and algorithm engineering, with a specialization in learned data structures that leverage machine learning to improve space-time trade-offs. He holds a PhD from the University of Pisa (2022), awarded the Best PhD thesis in Theoretical Computer Science by the Italian Chapter of EATCS. His academic journey includes postdoc research (2022), a visiting researcher role at KTH Royal Institute of Technology (2024), and Harvard University (2020). He has contributed to EU-funded projects like SoBigData.it and owns patents for innovations in data structure design. Key research interests include: Learned compression techniques for time series and string dictionaries Space-efficient indexing for massive datasets Algorithmic integration of machine learning into traditional data structures His work has been published in top venues including ICDE, Inf. Syst., and ACM Trans. Algorithms. Awards include the 2025 WSDM Outstanding Reviewer Award. He has co-supervised multiple theses on topics like adversarial query optimization and compressed indexing. Teaching roles include courses on programming, algorithms, and information retrieval at the University of Pisa. His software libraries (e.g., LeMonHash, PGM-index) are widely used in database systems and bioinformatics.

• Automotive Engineering
• Fluid Machinery
• Computational Fluid Dynamics
• +4 more

Alessandro Ferrari is a Full Professor at Politecnico di Torino's Department of Energy (DENERG) and a key member of the Center for Automotive Research and Sustainable Mobility (CARS@PoliTO) . His academic activities span teaching, research, and industrial collaboration, with a focus on automotive engineering, fluid machinery, and sustainable energy systems. Roles : Vice-Rector for Research Model Development, Scientific Director for industrial projects, PhD Thesis Supervisor Teaching : Advanced computational techniques for thermal/hydraulic machines, Computational heat/mass transfer Research : Fuel injection systems, ammonia combustion, cavitation dynamics, flow measurement Research Focus Professor Ferrari specializes in automotive propulsion systems and fluid machinery with emphasis on: Internal Combustion Engine optimization Computational Fluid Dynamics (CFD) modeling Novel fuel injection technologies Sustainable transport solutions His recent publications show strong trends in: Ammonia combustion for carbon-free fuels Real-time injection control using neural networks Cavitation analysis in GDI injectors Flow rate measurement techniques Academic Leadership As a PhD program director for Energetics since 2014, he has directly supervised 4 doctoral students. His industrial collaborations include patents for injection systems and flow measurement devices with commercial partners. Laboratory Involvement Active in the CARS@PoliTO center, he leads research on automotive sustainability and participates in fluid power experimental testing facilities.