Find Academic Supervisors

Maria Elena ValcherView profile

Professor

• Control Systems
• Systems Theory
• Optimization
• +5 more

Maria Elena Valcher is a Professor at the Department of Information Engineering, University of Padova, Italy. She is an IEEE Fellow (since 2012), IFAC Fellow (since 2023), Socio Effettivo of Istituto Veneto di Scienze, Lettere ed Arti (since 2017, previously Socio Corrispondente 2008-2017), and Socio Effettivo of Accademia Galieliana di Scienze, Lettere ed Arti in Padova (since 2022, previously Socio Corrispondente 2017-2022). She currently serves as Administrator of the Istituto Veneto and holds leadership positions including EUCA President (2024-2025) and IEEE Control Systems Society Past President. Her research focuses on control systems, systems theory, optimization, Boolean control networks, multi-agent systems, and consensus problems. She has made significant contributions in distributed control, data-driven methods, and network optimization, with recent work exploring applications in opinion dynamics and social networks. Recent publications demonstrate a strong emphasis on data-driven approaches to control systems, particularly in distributed state estimation, unknown-input observer design, and multi-agent coordination. Her work shows consistent development in theoretical frameworks for networked systems with practical applications. Awards and Honors: IEEE Fellow (2012) IFAC Fellow (2023) Socio Effettivo, Istituto Veneto di Scienze, Lettere ed Arti (2017-present) Socio Effettivo, Accademia Galieliana di Scienze, Lettere ed Arti in Padova (2022-present) She teaches 'Controlli Automatici' (Bachelor in Information Engineering) and 'Systems Theory' (Master in Control Systems Engineering) during the 2024/2025 academic year. She has chaired major conferences including the 61st IEEE Conference on Decision and Control (CDC 2022) and serves as Program Chair for ICSTCC 2025.

• Hybrid Electric Vehicles
• Sustainable Mobility
• Optimal Control
• +5 more

Federico Miretti is an Assistant Professor at the Polytechnic University of Turin, affiliated with the Department of Energy and the interdepartmental center Cars@PoliTo. His research focuses on hybrid and electric vehicles, with emphasis on energy management strategies, battery state estimation, and sustainable transport solutions. PhD in Energetics from Polytechnic University of Turin Research areas include: Optimization-based Energy Management Strategies for hybrid propulsion systems Simulation and digital twinning of hybrid systems Thermal management for electrified vehicles Techno-economic assessment of mobility solutions Recent publications highlight advancements in battery temperature anomaly detection, wireless power transfer feasibility, and control algorithms for energy efficiency. His work aligns with SDGs 7 (Clean Energy) and 9 (Innovation). Teaching roles include: Fluid Machinery (2019-2025) Energy Management in Hybrid/Electric Vehicles (2021-2025) Projects: PRoSIT (2025): Predictive thermal management demonstrator Consulting contract with MIDAC SpA (2025): Battery Digital Twin development EBOAT project (2025-2026): Technical support for Vulkan

• Quantitative Finance
• Risk Management
• Operational Research
• +6 more

Sandra Paterlini is a Full Professor in the Department of Economics and Management at the University of Trento, Italy. She holds academic roles including Co-Chair of the ERCIM Working Group on Optimization Heuristics and Vice-Chair of the IEEE Task Force on Portfolio Optimization. Her career includes visiting positions at institutions such as the University of Minnesota and Ludwig-Maximilians-Universität München. She earned a PhD in Computational Methods for Financial and Economic Decisions from the University of Bergamo, an MSc in Financial Mathematics from the University of Warwick, and a Laurea in Economics from the University of Modena and Reggio E. Her research focuses on quantitative finance, risk management, portfolio optimization, and network analysis, with applications to ESG, systemic risk, and financial stability. Key research contributions include methodologies for sparse graphical modeling, systemic risk analysis, and ESG scoring frameworks. She has received multiple awards for research excellence and serves on editorial boards of journals like Computational Statistics & Data Analysis and Frontiers in Applied Mathematics and Statistics . Her work bridges academia and policy, with contributions to the European Central Bank’s Financial Stability Directorate and involvement in global conferences on computational finance and econometrics.

• Integrated Circuits
• Pervasive Computing
• Electronics
• +14 more

Matteo Nardello is a researcher affiliated with the Department of Industrial Engineering at the University of Trento. His work focuses on embedded systems, IoT, and energy harvesting technologies for sustainable applications. Current academic affiliation: Department of Industrial Engineering, University of Trento Research interests: IoT, embedded systems, energy harvesting, machine learning, cyber-physical systems Contact: matteo.nardello@unitn.it His research integrates hardware-software co-design for batteryless IoT systems, with applications in smart agriculture, industrial monitoring, and autonomous vehicles. Recent work explores deep learning at the edge, energy-efficient sensor networks, and microbial fuel cells for self-powered devices. Key article trends highlight a focus on sustainable power solutions, wireless sensor networks, and machine learning optimization for constrained environments. He contributes to courses on embedded systems, IoT, and AI-powered industrial applications at the University of Trento.

• Algorithm Theory
• Algorithms and Data Science
• Economics and Computation
• +6 more

Stefano Leonardi is a Full Professor in the Department of Computer, Control and Management Engineering Antonio Ruberti at Sapienza Università di Roma. His research focuses on Algorithm Theory, Algorithms and Data Science, and Economics and Computation. He leads the ERC Advanced Grant project AMDROMA, exploring algorithmic mechanisms for online markets. He has held roles as Conference Chair for STOC 2021, WWW 2015, and FUN 2018, and coordinates the Sapienza School of Advanced Studies (2016-2018). His work spans approximation algorithms, online algorithms, and mechanism design. Awards include the ERC Advanced Grant and EATCS Fellowship. His research interests emphasize foundational algorithmic problems in web-based markets, leveraging rigorous design and large-scale data analysis. Recent projects include ALGADIMAR (PRIN 2019-2022) for digital market algorithms. He chairs the Highlights of Algorithms conference series and serves on program committees for top venues like EC, ICALP, and SODA. His lab focuses on web algorithmics and data mining, addressing challenges in online labor markets and fair division. Leonardi's academic contributions include over 100 publications, with recent work on fair algorithms, prophet inequalities, and mechanism design in auctions. He has pioneered methods for submodular optimization, online learning, and multi-agent systems. Grants and awards reflect his leadership in bridging theory with real-world applications, particularly in digital economies. Grants: ERC Advanced Grant (2018-2023), PRIN ALGADIMAR (2019-2022) Leadership: Chair of ACM STOC 2021, WWW 2015, and 9th FUN Conference Labs/Teams: Laboratory on Web Algorithmics and Data Mining Key Projects: AMDROMA (algorithmic mechanisms), ALGADIMAR (digital markets)

Daniele LoiaconoView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +6 more

Daniele Loiacono is an Associate Professor at Politecnico di Milano's Department of Electronics, Information, and Bioengineering (DEIB), affiliated with the Artificial Intelligence and Robotics Lab (AIRLab). His research focuses on interdisciplinary applications of Artificial Intelligence, Machine Learning, and Deep Learning in medical imaging, radiation therapy, and procedural content generation for games. He leads projects in synthetic image generation for radiotherapy quality assurance, automated treatment planning, and bias analysis in medical AI systems. Key research areas include medical image synthesis using GANs, radiation therapy optimization, and algorithmic game design. His contributions span clinical applications such as total marrow irradiation (TMI) planning and lymph-node segmentation, alongside innovations in shader generation and interactive evolutionary tools for game development. Loiacono collaborates on multi-center studies to validate AI-driven workflows in healthcare and has pioneered methods combining lean Six Sigma with machine learning for treatment process improvement. His work bridges clinical medicine and computer science, addressing challenges in radiation oncology, anatomical imaging, and procedural content automation. The AIRLab serves as a hub for his research, integrating AI advancements into real-world medical and engineering solutions.

• Power System Analysis
• Distribution System Optimization
• Electrical Load Management
• +7 more

Gianfranco Chicco is a Full Professor at the Politecnico di Torino , Italy, within the Department of Energy (DENERG). He serves as Scientific Director of the Electrical Power Systems research group and the Turin unit of the ENSIEL Consortium. A Fellow of IEEE (since 2018), he has held editorial leadership roles as Editor-in-Chief of Sustainable Energy, Grids and Networks and Subject Editor of Energy , while chairing major conferences like IEEE ISGT Europe 2017 and UPEC 2020. Education : Laurea (cum laude) in Electrical Engineering (1987), Ph.D. in Electrical Engineering (1992), Doctor Honoris Causa (2017, Polytechnic University of Bucharest; 2018, Technical University 'Gheorghe Asachi' of Iasi). Research Interests : Focus on power transmission and distribution systems , distributed energy resources , and multi-energy smart grids , with applications in artificial intelligence , load management , and power quality . His work addresses technical, economic, and environmental aspects of distributed generation and smart grid sustainability . Publications Trends : Recent articles emphasize photovoltaic power optimization , dynamic grid modeling , and multi-energy system coordination , reflecting expertise in renewable integration , clustering algorithms , and adaptive control . Key subfields include voltage stability , load forecasting , and blockchain for energy trading . Awards : IEEE Fellow (2018-) Best Paper Awards at SEST (2018, 2020) Doctor Honoris Causa (2017, 2018) 'Giancarlo Vallauri' Degree Prize (1988) Advising and Grants : Supervises PhD students in power converters , renewable integration , and high-voltage measurement methodologies . Leads European projects like H2020 MIGRATE and OSMOSE, focusing on grid resilience , clustering-based network planning , and multi-energy sustainability .

Riccardo RaheliView profile

Professor

• Telecommunications
• Digital Signal Processing
• Wireless Communications
• +5 more

Riccardo Raheli is a Full Professor at the University of Parma , Department of Engineering and Architecture, with a career spanning over three decades in Information and Communication Technologies (ICT). He has served as Chair of the Councils for Telecommunications and Communication Engineering programs, and as representative of the University of Parma in CNIT and its Members' Assembly. Education: Laurea in Electronic Engineering (University of Pisa, 1983), M.Sc. in Electrical and Computer Engineering (University of Massachusetts, 1986), Postgraduate Diploma (Scuola Superiore Sant'Anna, 1987) Key Roles: President of Degree Councils (2002-2018), CNIT Committee Member (2000-2005), Editorial Board member for IEEE Transactions, Springer and MDPI journals His research bridges telecommunications , digital signal processing , and healthcare applications , producing extensive international publications and industrial patents. He has co-authored monographs including Detection Algorithms for Wireless Communications (Wiley, 2004) and LDPC Coded Modulations (Springer, 2009). Recent article trends show interdisciplinary work in automotive stress monitoring (IoT/Matlab-based systems), video processing for healthcare (neonatal seizures, respiratory monitoring), and acoustic field control (microphone virtualization, personal sound zones). His work spans machine learning applications in automotive systems, stochastic acoustic modeling , and power-line communications . Scientific Leadership : Co-Chair for IEEE conferences (ICC 2010, GLOBECOM 2011, ISPLC 2020) Editorial roles in 7+ international journals Grants & Collaborations : Led industrial patents in communications systems Coordinated CNIT Technical Reports series (2025) He teaches Wireless Communications and Digital Signals Laboratory , emphasizing Matlab/Simulink proficiency. His laboratory sessions focus on practical implementation of signal processing algorithms, requiring full software installation on personal devices.

Neil ShahView profile

Academic

• Graph Neural Networks
• Machine Learning
• Data Mining
• +5 more

Neil Shah is a Lead Research Scientist at Snap Inc., leading initiatives in user modeling, personalization, and trust and safety across Snapchat. His research focuses on advancing machine learning algorithms for large-scale structured data, including graph and sequential representations, with applications to recommendation systems and social platform security. PhD in Computer Science, Carnegie Mellon University (2017), advised by Christos Faloutsos B.S. in Computer Science, North Carolina State University Current research interests span: Graph Neural Networks (GNNs) for real-time inference and scalable training Cross-domain recommendation systems and generative modeling Test-time augmentation and hyperbolic geometry in representation learning Explainability methods for GNNs and fairness-aware outlier detection Recent publications highlight productionized GNN frameworks (GiGL), multimodal graph benchmarks, and novel approaches to link prediction and collaborative filtering. His work has appeared at top venues like KDD, ICLR, NeurIPS, and WWW. Scientific recognition includes: Outstanding Service Award at WSDM 2022 Best Paper Honorable Mention at CHI 2019

Martino BanchioView profile

Assistant Professor

• Microeconomic Theory
• Market Design
• Mechanism Design
• +2 more

Martino Banchio is an Assistant Professor at the Department of Economics of Bocconi University and an Affiliate of the Innocenzo Gasparini Institute for Economic Research (IGIER). He concurrently serves as a part-time Research Scientist at Google Research. His work bridges microeconomic theory, market/ mechanism design, and computer science. His PhD thesis earned an honorable mention in the 2023 ACM SIGecom Doctoral Dissertation Award. Research interests focus on market design, industrial organization, and computer science applications in economics. Notable contributions include studies on credible auctions, AI-driven collusion, and dynamic pricing mechanisms. He frequently publishes in top venues like the ACM Conference on Economics and Computation (EC) and the ACM Web Conference (WWW). His awards include the Research Paper Competition at MIT Sloan Sports Analytics Conference 2020 and the Exemplary AI Track Paper Award. His research explores cutting-edge topics such as AI ethics in market design and algorithmic game theory, reflecting a strong interdisciplinary approach. No student advising records are explicitly listed. His work bridges academic theory and industry applications, as seen through collaborations with Google Research and affiliations with leading economic institutes.

• Multi-criteria decision analysis
• Operations research
• Fuzzy sets and fuzzy numbers
• +7 more

Matteo Brunelli is Associate Professor of “Mathematical Methods of Economics and Actuarial and Financial Sciences” at the University of Trento , Department of Industrial Engineering, and Adjunct Professor (docent) at Lappeenranta University of Technology , Finland. He is nationally habilitated as Full Professor in Italy and has held long-term visiting positions at Berkeley, Turku, Auckland, JAIST and Binghamton. Education: Ph.D. (Doctor of Science) in Information Technologies, Åbo Akademi University, Finland, 2011 – graded Eximia cum laude approbatur M.Sc. in Economics, University of Trento, 2007 – grade 110/110 cum laude B.Sc. in Economics, University of Trento, 2005 Research focus: Brunelli’s work sits at the intersection of multi-criteria decision analysis , operations research and computational optimisation . He develops axiomatic foundations and algorithms for pairwise comparison matrices , consistency indices , the best-worst method and fuzzy preference relations , and applies them to energy planning, sustainable inventory, maintenance scheduling, 3-D printer selection, and blockchain governance. His 2023-2025 articles reveal intensified interest in uncertainty modelling (Dempster-Shafer theory), bi-objective optimisation of inventory and maintenance, and group decision protocols that integrate probabilistic or active-learning components, demonstrating both methodological depth and practical relevance. Scientific awards & grants: Academy of Finland Postdoctoral Researcher grant (€254 670, 2014-2017) Claudio Dematté Research Grant (€19 000, 2008) Teacher of the Year Award, Aalto University (2013 – both Spring & Autumn semesters) Bernard Roy Award 2021 for outstanding contribution to Multiple Criteria Decision Aiding (under-40 category) Supervision & funding: While specific doctoral students are not listed, Brunelli currently supervises graduate theses at Trento and has continuously held competitive national grants. His Academy of Finland project “Consistency of valued preference relations for decision analytics methods” financed three years of full-time research and international collaboration. Editorial & community roles: He serves on the editorial boards of International Journal of General Systems and Mathematical and Computational Applications , and acts as area editor for Journal of Multi-Criteria Decision Analysis , positioning him among the key gatekeepers of the MCDA community.

Carlo BaldassiView profile

Associate Professor

• Machine Learning
• Computational Neuroscience
• Statistical Mechanics
• +4 more

Carlo Baldassi is an Associate Professor at Bocconi University, where he has served as Director of the BSc in Mathematical and Computing Sciences for Artificial Intelligence (BAI) since 2023/24. He holds a background in Theoretical Physics from the University of Trieste and a PhD in Computational Neuroscience from the University of Turin. His research focuses on applying Statistical Mechanics to Machine Learning and Neural Networks, particularly studying loss landscapes, optimization problems, and the role of quantum annealing in nonconvex learning. He teaches courses in Machine Learning, Artificial Intelligence, and Computer Science, emphasizing Python and Julia programming. His work bridges theoretical physics and AI, exploring topics like synaptic stochasticity in low-precision neural networks and the efficiency of quantum vs. classical annealing. He has published extensively in top journals such as Physical Review Letters and Proceedings of the National Academy of Sciences , contributing to foundational understanding of neural network dynamics and optimization techniques. Teaching includes Machine Learning and Artificial Intelligence Computer Science I Machine Learning II Machine Learning and Artificial Intelligence Lab Research emphasizes large-scale inference problems, with a focus on the interplay between statistical mechanics and modern AI architectures.

• Structural Health Monitoring
• Seismic Analysis
• Bayesian Calibration
• +14 more

Gaetano Miraglia is a Fixed-term Assistant Professor in the Department of Structural, Building and Geotechnical Engineering (DISEG) at Politecnico di Torino, where he conducts research in structural health monitoring, seismic analysis, and computational modeling. He is a member of the Interdepartmental Center R3C – Responsible Risk Resilience Centre, contributing to interdisciplinary efforts in risk mitigation and infrastructure resilience. His work spans both theoretical and applied domains, with strong emphasis on heritage preservation and sustainable urban development. His research interests include Bayesian calibration of nonlinear models, hybrid simulation, peridynamics, masonry structures, and the integration of satellite interferometric (InSAR) data with in-situ measurements for structural monitoring. He applies advanced computational and machine learning techniques to improve the accuracy and reliability of structural assessments, particularly in historical and monumental buildings. His work supports UN Sustainable Development Goals 9, 11, and 13. His recent publications demonstrate a consistent focus on data fusion, digital twinning, domain adaptation, and real-time damage detection. He frequently collaborates with researchers such as Rosario Ceravolo and Erica Lenticchia, publishing in high-impact journals like Computer-Aided Civil and Infrastructure Engineering , Structures , and Scientific Reports , as well as at major conferences including EWSHM, SAHC, and EVACES. His research is applied in projects such as the monitoring of the Vicoforte Sanctuary and the development of the CAMELOT and HY-LEARN toolboxes. Research Projects: MONITORAGGIO VICOFORTE (2024–2026) – Member of Research Group CAMELOT – PoC Transition (2023–2024) – Member of Research Group HY-LEARN – Model Calibration via Hybrid Simulation and ML (2022–2024) – Scientific Manager (PNRR Mission 4) He teaches in various programs, including as a course collaborator in PhD, Master’s, and Bachelor’s level courses such as Earthquake Engineering , Structural Consolidation , and Seismic Risk of Cultural Heritage . He is also an inventor on national and international patents and software related to the CAMELOT toolbox, highlighting the translational impact of his research. He has no listed scientific awards or formal advisees in the provided text.

• Control and Systems Engineering
• Automotive Engineering
• Model Predictive Control
• +10 more

Massimo Canale is a Tenured Associate Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino , and a member of the CARS@PoliTO Interdepartmental Center for Automotive Research and Sustainable Mobility. His academic career spans over two decades, focusing on control systems engineering with applications in automotive technology. Scientific Branch: Systems and Control Engineering (IINF-04/A) ERC Sectors: Automotive Engineering, Control Engineering, Control Theory Dr. Canale's research bridges theoretical advancements in Model Predictive Control (MPC) with practical applications in autonomous vehicles , hybrid/electric propulsion , and active suspension systems . His work integrates reinforcement learning and dynamic programming for optimizing vehicle performance and energy efficiency. Recent publications demonstrate trends in autonomous driving architectures (2024), sliding mode control for highway scenarios (2024), and energy management for sustainable mobility (2023-2024). He has developed patented solutions for semi-active suspension control and autonomous vehicle guidance. Award: IEEE Transactions on Control Systems Technology Outstanding Paper Award (2011) Editorial Roles: Associate Editor, IEEE Open Journal of Control Systems (2022–present) Dr. Canale supervises PhD students like Francesco Cerrito and teaches courses on digital control technologies , automatic control , and reinforcement learning at Politecnico di Torino. His research is funded through competitive grants (e.g., MPC4AVP 2021-2022) and commercial contracts (AD Shuttle 2024).

Luciano LavagnoView profile

Professor

• Field Programmable Gate Arrays (FPGA)
• High-Level Synthesis
• Indoor Positioning
• +10 more

Luciano Lavagno is a Full Professor at the Department of Electronics and Telecommunications, Polytechnic University of Turin, with over two decades of academic and research contributions. His work bridges hardware acceleration, low-power electronics, and intelligent system design. Research Focus: Hardware-accelerated machine learning, high-level synthesis (HLS) for FPGA/ASIC, heterogeneous CPU/GPU/FPGA platforms Key Projects: SPACE (predictable acceleration), REBECCA (secure AI acceleration), HPC-National Center (quantum computing), and oral history preservation via "Ti racconto una storia" initiative His recent publications analyze CNN inference optimization, subgraph isomorphism, and superword-level parallelism exploitation. Lavagno supervises multiple PhD students working on FPGA acceleration, neural network hardware, and embedded systems. As Principal Investigator for national and EU-funded projects (PRIN, JTI-ECSEL, PNRR), he drives advancements in sustainable computing infrastructure. His patented technologies include MIx&Latch timing methodology, capacitive sensing innovations, and 5G acceleration frameworks.