Find Academic Supervisors

• Climate Change
• Disaster Risk Management
• Environmental Sciences
• +3 more

Jaroslav MYSIAK is an Adjunct Professor at the Department of Environmental Sciences, Informatics and Statistics (DEIS) at Ca' Foscari University of Venice. His research focuses on climate risk management, disaster resilience, and environmental policy, with particular emphasis on flood modeling, nature-based solutions, and decision support systems for sustainable resource management. He holds an academic appointment in the Department of Environmental Sciences, Informatics and Statistics at Ca' Foscari University of Venice. His teaching activities include advanced courses on climate risk modeling and assessment at the master's and doctoral levels. His key research contributions span climate adaptation pathways, flood damage assessment frameworks, and the integration of Bayesian networks for multi-sectoral risk analysis. MYSIAK has extensively engaged in developing policy-oriented tools such as the MULINO-DSS decision support system and the INFORM vulnerability framework. Recent work explores nature-based solutions for climate resilience and the governance mechanisms enabling their implementation. His publications demonstrate expertise in cross-disciplinary research, bridging hydrology, economics, and policy. Current research themes include: Climate services evaluation and socio-economic impact analysis Resilience assessment frameworks for coastal and urban areas Cost-benefit analysis of flood defense infrastructure Integration of machine learning in disaster risk reduction His work frequently addresses European and Mediterranean contexts, with studies on Adriatic coastal systems and Italian agricultural water management. MYSIAK's research has been applied in policy contexts including the EU's climate adaptation strategy and international disaster risk reduction frameworks.

• Geomatics
• Cultural Heritage Digitization
• 3D Modeling and Reconstruction
• +7 more

Francesca Matrone is a Fixed-term Assistant Professor in the Department of Environment, Land and Infrastructure Engineering (DIATI) at Politecnico di Torino, Italy. Her work bridges geomatics, artificial intelligence, and cultural heritage conservation, with a focus on 3D modeling, GIS, and HBIM integration for urban and historical environments. Her research interests include Geomatics, Cultural Heritage Digitization, AI in Geospatial Analysis, Drone-Based 3D Modeling, Semantic Segmentation of Point Clouds, and Smart Cities . She applies advanced technologies like UAVs, deep learning, and BIM-GIS integration to enhance the documentation, maintenance, and resilience of built heritage. The recent publications highlight a strong trend in using AI and machine learning for semantic segmentation of 3D heritage models, integration of HBIM with GIS using standards like IFC and CityGML, and drone-based photogrammetry for urban and territorial planning. These works reflect a multidisciplinary approach combining computer science, civil engineering, and digital heritage. Francesca Matrone is actively involved in research projects such as: HERITALISE (2025–2028, EU-funded): Digitization and cloud visualization of heritage buildings ACLIMO (2024–2026): Research collaboration with APAM on protected areas COGNITIO-FORT (2025–2026): Scientific Director for a project with Unione Montana Valle Stura She teaches across multiple programs: PhD : GeoAI for advanced urban analyses; Drones for 3D modeling (Main Teacher) Master’s : GIS Modeling for City and Land; Knowledge of Built Heritage under Climate Change Bachelor’s : GeoAI: Artificial Intelligence and Geospatial Data; Topography (Course Collaborator) She is a member of the Teaching Colleges for Civil and Building Engineering and Electronic, Telecommunications and Physics Engineering. She also holds a Learning to Teach (L2T) Open Badge from Politecnico di Torino, demonstrating her commitment to pedagogical excellence. Francesca is a co-inventor of the MAIN10ANCE PLATFORM , a national software patent aimed at enhancing the maintenance and conservation of historical built heritage through integrated HBIM-GIS systems.

• Formal verification
• Model checking
• Binary decision diagrams
• +7 more

Gianpiero Cabodi is an Associate Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic of Turin, where he is also a member of the University Internship Commission. He is actively involved in teaching and research within the College of Computer, Film and Mechatronics Engineering. Research Interests: His research spans formal verification, model checking, binary decision diagrams, SAT solvers, embedded system design, and cybersecurity. His work emphasizes formal methods for ensuring correctness and security in hardware and software systems, particularly in safety-critical and automotive applications. Recent Publications: His 2024 publications demonstrate a strong focus on optimizing formal verification techniques. He explores interpolation-based improvements in bounded model checking, hardware model checking algorithms, and the use of binary decision diagrams in interpretable machine learning—highlighting a convergence of formal methods with modern AI interpretability and hardware verification. Teaching and Leadership: He has served as a course instructor for advanced data structures in Python, systems programming, and algorithms. He has also directed several doctoral programs in Computer and Systems Engineering. His leadership extends to research projects such as REBECCA and OSMOSIS, and he has chaired international workshops including FMCAD and DIFTS. Research Projects: REBECCA (2023–2026): Scientific Head of Structure, focusing on reconfigurable platforms for secure AI. OSMOSIS (2008–2010): Scientific Responsible for EU-funded codesign of embedded systems. Multiple commercial research projects on embedded systems in automotive, RFID traceability, cybersecurity, and HMI design (2008–2025). Advising and Grants: While no formal students are listed, he has supervised numerous research projects, often as Scientific Manager or Responsible, funded by both EU programs and industry contracts. His work bridges academic research and industrial applications, particularly in automotive and embedded domains. Labs and Teams: He is a key member of the FM - Formal Methods (DAUIN) research group, which focuses on formal verification techniques for digital systems.

• Sparse optimization
• Machine learning
• Convex and nonconvex optimization
• +7 more

Sophie Fosson is an Associate Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, Italy. She is a member of the System Identification & Control (SIC) research group and teaches courses such as Automatic Control and Modeling and Control of Cyberphysical Systems at both the master's and doctoral levels. Education: PhD in Mathematics for Industrial Technologies, Scuola Normale Superiore di Pisa Master’s in Mathematical Engineering, Politecnico di Torino Her research focuses on sparse optimization, machine learning, system identification, and control theory . She integrates mathematical modeling with practical applications in cyber-physical systems and neural networks. Her work emphasizes efficient and robust optimization techniques for deep learning and control systems. The recent publications highlight a strong trend in optimization of neural networks , particularly through sparse training, binary and ternary quantization, self-supervised learning, and fault tolerance . Her research bridges control theory with machine learning, applying mathematical rigor to modern AI challenges in safety-critical and embedded environments. Scientific Awards: No awards explicitly mentioned. She supervises several PhD students including Alice Re, Rosario Milazzo, and Simone Pirrera , and has been involved in research projects such as CRISP and collaborations with Centre Tecnològic de Telecomunicacions de Catalunya . She has held postdoctoral positions at DISMA and DET (Politecnico di Torino) and worked as a researcher at Istituto Superiore Mario Boella. Laboratories and Research Groups: System Identification & Control (SIC), DAUIN, Politecnico di Torin

• 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.

• Augmented Reality
• Virtual and Augmented Reality (VAR)
• Human-Computer Interaction
• +12 more

Federico Manuri is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN), Politecnico di Torino, and a member of the College of Architecture and Design. He is actively engaged in research and teaching with a strong focus on augmented reality, human-computer interaction, and computer vision. His educational background is not explicitly detailed in the provided text, but his research and teaching activities indicate a strong foundation in computer science and engineering. Manuri's research interests span Augmented Reality , Virtual and Augmented Reality (VAR) , Human-Computer Interaction (HCI) , Human-Robot Interaction (HRI) , Collaborative Robotics , Computer Vision , and Human-Centered Design . His work integrates AI and immersive technologies into industrial and educational applications. The recent publications highlight a consistent focus on applying AI and computer vision to real-world challenges in sports analytics, industrial manufacturing, and education. Key themes include synthetic data generation for 3D reconstruction, immersive training using co-embodiment in VR, and AI-driven adaptive learning systems, reflecting a strong interdisciplinary and application-oriented research profile. His scientific contributions are recognized through publications in reputable journals and conferences such as IEEE AICT, SUSTAINABILITY, and IET Collaborative Intelligent Manufacturing, though no specific awards or fellowships are mentioned. Manuri supervises PhD student Luca Francesco Rossi , whose research focuses on entity identification using computer vision in dynamic environments, supported by an industrial collaboration with netventure Group. He is also involved in teaching multiple courses, including Extended Reality and Human-machine interaction , across engineering and design programs. He leads the commercial research project NextMind Applications as Scientific Manager. He is affiliated with the GRAINS - GRAphics and INtelligent Systems research group at DAUIN, which serves as the primary hub for his collaborative research activities in graphics, AI, and intelligent systems.

• Biomedical Signal Processing
• Neuroscience
• Artificial Intelligence
• +12 more

Gabriella Olmo is a Full Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where she also serves as a member of the Interdepartmental Center PolitoBIOMed Lab - Biomedical Engineering Lab. Her academic and research activities are centered on biomedical signal processing, neuroscience, and artificial intelligence, with a strong emphasis on healthcare applications. She is actively involved in doctoral education, supervising PhD candidates in Computer and Systems Engineering, Artificial Intelligence, and Biomedical Engineering, and has contributed to doctoral programs at both the Polytechnic University of Turin and the University of Turin. Her research interests include biomedical signal processing , neuroscience , artificial intelligence , telecommunications , and digital health , with applications in Parkinson’s disease, sleep disorders, neonatal monitoring, and stress assessment. She leads several major research initiatives funded by national and European programs, including PNRR, EU Horizon Europe, and private foundations, focusing on wearable technologies, AI-driven diagnostics, and telemedicine. Her recent publications demonstrate a consistent trend in developing AI-powered, non-invasive solutions for monitoring neurodegenerative and sleep disorders using wearable sensors and signal processing. These works span topics such as freezing of gait detection, REM sleep behavior disorder, neonatal pain assessment, and deep brain stimulation effects, reflecting a strong interdisciplinary focus on clinical translation of engineering innovations. She has led or managed numerous competitive research projects, including OMNIA-PARK, PERSIMMON, S-CoDe, and NODES, and holds patents in distributed arithmetic coding and video storage. Her leadership roles as Scientific Director and Contractual Manager highlight her active engagement in both academic and applied research. She advises a large cohort of PhD students working on AI in healthcare, computer vision, and biomedical signal analysis. Her teaching portfolio includes courses in Signal Analysis, Telemedicine Technologies, Artificial Intelligence in Medicine, and Machine Learning in Healthcare, across undergraduate, master's, and doctoral levels. She is a key member of the SMILIES research group, which focuses on resilient computing architectures for life sciences, and collaborates with healthcare institutions such as the Orfanelle Nursing Home on geriatric frailty monitoring.

• Bioinformatics
• Systems Biology
• Deep Learning
• +6 more

Gianfranco Michele Maria Politano is an Associate Professor at the Department of Control and Computer Science (DAUIN) at Polytechnic University of Turin, with affiliations to the College of Computer, Film and Mechatronics Engineering and College of Biomedical Engineering. His research focuses on Bioinformatics, Systems Biology, and Deep Learning applications in health. Research trends span computational modeling of biological systems, protein function prediction using machine learning, and public health analysis of disease burdens (influenza, rectal cancer, and COVID-19). He also investigates gene regulatory networks and microRNA roles in inflammation. His teaching includes Bioinformatics and Internship courses for Computer Engineering and Aerospace Engineering students since 2019. He supervises PhD student Sofia Ostellino on computational solutions for disease monitoring and has secured research grants for biostatistics projects like DIDES (exotic animal diagnostics) and rectal cancer ultrasound staging. Notable projects : DIDES (2020), Feasibility study for rectal cancer diagnostics (2021) Patents : Computerized culture protocols for biomanufacturing, Simulation of biological ontogeny

• Theoretical Physics
• Complex Systems
• Mathematical Physics
• +3 more

Andrea AUCONI is a Researcher in the Department of Molecular Sciences and Nanosystems (DSMN) at Ca' Foscari University of Venice, based at the Scientific Campus, via Torino. His academic position falls under the Scientific Disciplinary Sector PHYS-02/A: Theoretical Physics of Fundamental Interactions, Models, Mathematical Methods, and Applications. His research spans interdisciplinary domains including: Theoretical Physics Complex Systems Mathematical Physics Statistical Mechanics Network Theory Stochastic Processes Recent publications (2022-2024) demonstrate applications across social phenomena (election dynamics), financial systems (network uncertainty), biological processes (Bayesian chemotaxis), and fundamental information theory. This work consistently employs stochastic modeling and network analysis frameworks, published in high-impact journals like Scientific Reports and Europhysics Letters. For academic year 2024/2025, he teaches: Institutions of Mathematics with Exercises - 1 (9 credits) for Science and Technology for Cultural Heritage (Degree CT60) Institutions of Mathematics with Exercises - 1 (9 credits) for Chemistry and Sustainable Technologies (Degree CT7) No scientific awards or fellowships are documented in the available materials. His departmental affiliation with DSMN contextualizes his theoretical physics work within molecular and nanoscale science applications.

• Bayesian inference
• Machine learning
• Nonparametric methods
• +5 more

Francesca Panero is an Assistant Professor (RTT) in Statistics at the Department of Methods and Models for Economy, Territory, and Finance (MEMOTEF) at Sapienza University of Rome. She also serves as a Visiting Fellow at the Department of Statistics of the London School of Economics and Political Science, where she was previously an Assistant Professor until May 2024. Additionally, she maintains an affiliation with the Grantham Research Institute on Climate Change and the Environment at LSE. Dr. Panero earned her PhD in Statistics from the University of Oxford in 2022, following undergraduate and master's studies at the Department of Mathematics of the University of Turin and Collegio Carlo Alberto. Her academic journey reflects a strong foundation in mathematical and statistical theory applied to complex real-world problems. Her research centers on Bayesian statistical methodologies with applications across multiple domains. She specializes in complex networks analysis using Bayesian nonparametric approaches, disclosure risk assessment for privacy preservation, spatio-temporal Gaussian process modeling for food insecurity prediction, and fair machine learning techniques. Her work demonstrates a consistent thread of developing rigorous statistical methods with practical societal applications, particularly in network science, privacy protection, and algorithmic fairness. Dr. Panero's publications reveal a strong trajectory in high-impact statistical research, with recent work focusing on sparse network modeling, fairness constraints in machine learning, and privacy-preserving statistical techniques. Her methodological contributions bridge theoretical statistics with applications in social sciences, public policy, and computational challenges. 2025 Visiting Lecturer at Faculty of Informatics, University of the Italian Switzerland (USI) Elected as j-ISBA Chair Elect for 2025-2026 Recipient of IMS junior research travel grant for ICSDS 2024 Awarded LSE Research and Impact Support Fund (RISF) grant for food insecurity research As an educator, Dr. Panero teaches Probability and Stochastic Processes at Sapienza University and has previously instructed Deep Learning and Artificial Intelligence courses at LSE. She actively mentors students and encourages PhD candidates interested in her research areas to contact her. She is also a core member of GENIAL, an LSE focus group examining how students utilize generative AI tools for learning. Beyond her technical work, she has been engaged in equality, diversity, and inclusion initiatives since 2020 and maintains an active academic blog discussing data visualization, research projects, and academic life.

• Weed Science
• Precision Agriculture
• Soil Salinity
• +4 more

Roberta Masin serves as an Associate Professor within the Department of Agri-Food, Environmental and Animal Sciences (DAFNAE) at the University of Padua, Italy, specializing in AGR/02 (Agronomy and Field Crop Production). Based at the Agripolis campus in Legnaro, Padova, she conducts research at the intersection of agricultural systems and environmental sustainability, with significant contributions to Mediterranean agricultural challenges. Her research program focuses on weed ecology under climate stressors, particularly soil salinization and drought impacts on coastal agriculture. She pioneers precision agriculture techniques using UAVs and remote sensing for weed mapping, seed germination modeling, and site-specific management. Key innovations include nature-based solutions for salinity mitigation, herbicide reduction strategies, and assessment of invasive species like Ambrosia artemisiifolia across European ecosystems. Her work integrates field experiments with advanced data analytics to develop sustainable crop protection frameworks. Analysis of her 2023-2025 publications reveals a dominant research trajectory in climate-resilient agriculture, with 60% of recent work addressing seawater intrusion impacts and weed adaptability in coastal zones. She demonstrates strong European collaboration networks, particularly with Croatian institutions, focusing on translating hydrothermal emergence models into practical precision farming tools. Her publications consistently emphasize actionable strategies for reducing chemical inputs while maintaining productivity in saline-stressed environments.

• 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 .

• Microwave Engineering
• Semiconductor Device Modeling
• Nonlinear Circuit Analysis
• +3 more

Giorgio Vannini is a Full Professor of Electronics at the Department of Engineering, University of Ferrara, where he has been since 2005. His academic career spans over three decades, with prior roles as a Research Fellow and Associate Professor at the University of Bologna. He has held leadership positions including Head of the Engineering Department (2007-2015) and membership in the Academic Senate (2012-2015). Education: Laurea in Electronic Engineering from the University of Bologna (1987), Ph.D. in Electronic and Computer Science Engineering from the University of Bologna (1992). Research: Focused on nonlinear semiconductor device modeling (e.g., GaN HEMTs, III-V FETs), advanced microwave circuit design, and electromagnetic simulation techniques. Key contributions include the Nonlinear Integral Model (NIM) and dynamic I/V characterization systems. Awards: Best Paper Awards at the European Microwave Conference (1995), GAAS98 (1998), and GAAS2001 (2001). Projects: Led over 20 research contracts with industry leaders like Leonardo, Sumitomo, and Huawei, and participated in EU Framework Program initiatives including TARGET and Eurostars. Spin-off: Co-founded MEC srl, an academic spin-off for microwave engineering solutions.

Sandro Luigi FioreView profile

Associate Professor

• Data Science
• Big Data
• Scientific Data Management
• +13 more

Sandro Luigi Fiore is an Associate Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy. He has held academic positions at the University of Salento and visiting scientist roles at the University of Chicago and Lawrence Livermore National Laboratory. His research integrates data science, big data, and high-performance computing with climate informatics and open science. Education: Ph.D. in Innovative Materials and Technologies, University of Lecce, 2004 Master of Science in Computer Engineering (with honors), University of Lecce, 2001 His research interests span Data Science, Big Data, Scientific Data Management, Artificial Intelligence, and Distributed/Cloud/Parallel Computing , with a strong application focus on Climate Change and Open Science . He develops FAIR-enabled data analytics solutions and contributes to large-scale data infrastructures such as ESGF, EOSC-hub, and INDIGO-DataCloud. His work emphasizes provenance, reproducibility, and interoperability in scientific computing. The articles reflect a consistent trajectory in large-scale scientific data systems , evolving from Grid-DBMS and parallel computing to modern applications in climate informatics, AI, and FAIR data. Key themes include distributed data management, middleware, exascale software, and reproducible workflows in HPC environments. Scientific Awards: Earth System Grid Federation Team Award (2017) UNIDATA Community Equipment Award (2011) Best Student Paper Award, ITCC2003 Fiore has advised on and led multiple national and European projects including EOSC-hub, IS-ENES, and BARRACUDA (RDA-Europe3). He actively supports the adoption of FAIR data principles in scientific repositories, advising on data policies, architecture, provenance, and interoperability. He is a member of the FAIR Champions group and serves on the Advisory Board of FAIRsFAIR. He is involved in key research teams and projects such as the Earth System Grid Federation (ESGF) , Globus Lab (University of Chicago) , and PCMDI/LLNL . His work contributes to climate model intercomparison (CMIP5/6) and the development of next-generation data infrastructure for open science.

Vadim ZaytsevView profile

Associate Professor

• Software Evolution
• Software Analysis
• Modelling and Restructuring
• +9 more

Vadim Zaytsev is an Associate Professor of Software Evolution at the University of Twente, specializing in software analysis, modelling, and restructuring since 2004. He has previously worked as a Chief Science Officer, developing compilers and analyzing migration projects, and his past affiliations include institutions in the Netherlands, Belgium, Germany, and Russia. His work bridges academia and industry, focusing on industrially relevant research, teaching, and prototype software development. Research Interests: Software evolution and structure elicitation Domain-specific languages (DSL) and cyber-physical systems (CPS) Automated program repair using large language models Modernity signatures in programming languages (PHP, Python, C#, Rust) Educational software assessment (Apollo++ tool, BabyCobol) Grammarware and code transformation (CFG to VPG converter) Recent Article Trends: His 2025 work explores large language models for automated program repair (SAC, GPCE), while earlier studies (2024) focus on DSL-based CPS modelling, learning-by-doing assessment pipelines, and modernity signatures in programming languages. His research often involves collaboration with students and interdisciplinary groups like HMI at UTwente. Teaching and Practice: He supervises students in research projects, teaches the MSc course Software Evolution , and contributes to curriculum re-accreditation efforts. His practice includes open-source tools (GPL-licensed CFG to VPG converter) and conference leadership roles (Program Co-Chair for SLE 2025 and ICT.OPEN 2025).