Giacomo Chiesa is a Full Professor at the Department of Architecture and Design (DAD) at Politecnico di Torino. He is a member of the Interdepartmental Center Ec-L - Energy Center Lab. His research focuses on Architectural Technology , Bioclimatic Design , Building Performance , and Urban Climate . Research Interests : Building Simulation, Passive Cooling, Smart Buildings, Digital Twin, Climate Change Adaptation Recent publications analyze urban weather datasets for energy simulations, shading control thresholds , and ventilation strategies in educational buildings. His work covers energy renovation roadmaps , thermal comfort , and climate-resilient building systems . Teaching : PhD courses in Human-Centric Methodologies and MSc courses in ICT in Building Design Research Leadership : Scientific Director for projects like Urban Generation and Prelude , EU-funded initiatives
Giovanna Turvani is an Associate Professor at the Department of Electronics and Telecommunications (DET) at Politecnico di Torino, with affiliations in both the College of Electronic, Telecommunications and Physics Engineering and the College of Computer, Film, and Mechatronics Engineering. Scientific Branch: IINF-01/A - Electronics ERC Sectors: PE7_4, PE7_11, PE6_1, PE6_14, PE7_3 SDG Goals: Quality Education, Gender Equality, Affordable Energy, Industry Innovation Her research focuses on advanced electronics and quantum technologies, including: Logic-in-memory computing Quantum computing architectures Microwave imaging for medical and agricultural applications CAD tools for emerging nanotechnologies Embedded systems for bee health monitoring IoT solutions for bio-waste valorization Publications show strong expertise in quantum computing, nanocomputing, and microwave imaging, with recent trends emphasizing quantum optimization frameworks, in-memory architectures, and IoT-based agricultural technologies. She supervises PhD students in areas like quantum machine learning algorithms, predictive on-board systems, and quantum hardware design. Collaborations span multiple disciplines, including medical device development and agricultural electronics. Patents include innovations in microwave imaging, racetrack memory logic functions, and in-memory computing devices.
Marco Toffolon is a Full Professor at the University of Trento's Department of Civil, Environmental and Mechanical Engineering, where he leads the Physical Limnology Laboratory. He serves as Deputy Director for International Relations and previously directed the Environmental Engineering programs. His research spans ecohydraulics, sediment transport, lake hydrodynamics, and environmental modeling. He investigates physical limnology, tidal morphodynamics, and stratified flows using analytical and numerical approaches. His work integrates field measurements with machine learning for water quality prediction and climate impact assessment. His publications focus on lake dynamics, river morphodynamics, and sustainable water management, with recent emphasis on climate-driven changes in alpine systems. Research demonstrates strong interdisciplinary linkages between hydraulics, ecology, and climate science. Awards: 2016 Coastal Engineering Journal Award 2013 Enrico Marchi Lecture invitation He leads international collaborations with institutions like EPFL and Sun Yat-sen University, and organizes conferences including the Physical Processes in Natural Waters workshop series.
Gias Uddin is an Associate Professor at York University's Lassonde School of Engineering and an Adjunct Professor at the University of Calgary . His research bridges Software Engineering (SE) and Artificial Intelligence (AI) , focusing on AI Trustworthiness Assessment (SE4AI) and AI-Driven Productivity Tools (AI4SE) . PhD in Software Engineering & AI, McGill University (2018) MSc in Software Engineering, Queen’s University (2008) BSc in Computer Science & Engineering, Bangladesh University of Engineering and Technology (2004) His research explores: Metamorphic Relations for LLM Hallucination Detection AI-Enhanced Software Documentation Foundational Models for Runtime System Modernization Developer-Centric AI Tooling Recent article trends show expertise in LLM Trustworthiness , Low-Code Platforms , and IoT Developer Communities . Awards include Distinguished Paper at FSE 2025 , multiple IBM Champion recognitions, and York Research Award . He leads the Data Intensive Software Analytics (DISA) Lab and mentors PhD students in SE-AI Intersections .
Marco Badami is a Full Professor at the Department of Energy (DENERG) , Polytechnic of Turin. He serves as Scientific Director for national and EU-funded research projects in energy systems and has been a Course Lecturer for Energy Systems and Industrial Use of Energy since 2010. He supervises PhD students in Energetics and Electrical Engineering . Research Interests: His work spans energy systems optimization, machine learning applications for industrial energy efficiency, smart grids, cogeneration scheduling, blockchain-based energy data immutability, and predictive maintenance algorithms for photovoltaic plants. Current projects focus on AI-driven energy audits, optimized control systems for industrial microgrids, and digital twin architectures. Collaborations: He works with Trigenia Srl, Stogit SpA, and international institutions on commercial research contracts. His scientific contributions include 15+ publications on topics like LSTM forecasting for solar energy, deep reinforcement learning for multi-energy systems, and decentralized peer-to-peer energy trading platforms.
Alberto Macii is a Full Professor in the Department of Control and Computer Science (DAUIN) at Polytechnic University of Turin. He is also a member of the Interdepartmental Center IAM@PoliTo - Integrated Additive Manufacturing and serves on two colleges: College of Computer, Film and Mechatronics Engineering and College of Mechanical, Aerospace and Automotive Engineering. His research interests include: Digital circuits and systems Electronic systems, modeling, and simulation Energy-efficient circuits and systems Energy management and battery systems Embedded systems and cyber-physical systems Low energy building technologies His work aligns with several Sustainable Development Goals including affordable and clean energy (Goal 7), industry innovation and infrastructure (Goal 9), and sustainable cities and communities (Goal 11). His publication record shows a consistent focus on energy efficiency in electronic systems spanning over two decades, with particular emphasis on battery modeling and power management. Recent work has expanded into environmental applications with transformer neural networks for flood forecasting, demonstrating the evolution of his research into new application domains while maintaining core expertise in energy optimization. His scientific awards include: Best paper award ACM/IEEE Great Lakes Symposium on VLSI (2008) IEEE Fellow (2007-) Senior Member IEEE He has served as Editor-in-Chief for the Journal of Embedded Computing (2005) and as Program Chair for EUC2005: Embedded & Ubiquitous Computing. Professor Macii has advised PhD students including Alberto Bocca (2019-2023) whose thesis focused on compact modeling techniques for energy analysis and optimization of complex systems. He has led numerous research projects including CANP (2018-2020), R3-PowerUP (2017-2021), SERENA (2017-2020), AMable (2017-2021), and STAMP (2016-2019), demonstrating sustained research leadership across multiple funding mechanisms. He is active in the EDA - Electronic Design Automation research group and LAB 4 research laboratory at DAUIN, with research spanning VLSI-CAD, Smart City technologies, and Industry 4.0 applications. His work bridges theoretical computer engineering with practical applications in energy conservation across multiple domains.
Valeria Bruschi is a Researcher at the Department of Information Engineering (DII) within the Faculty of Engineering at Università Politecnica delle Marche (UNIVPM) in Ancona, Italy. Her academic profile was last updated on April 13, 2024, and she maintains her office at the Engineering Faculty on via Brecce Bianche, with contact information including phone +39 071-220-4486 and email v.bruschi@staff.univpm.it. Dr. Bruschi's research spans multiple domains within audio and signal processing, with particular expertise in spatial audio systems, automotive human-computer interaction, and biomedical signal applications. Her work bridges theoretical signal processing techniques with practical implementations across diverse fields including automotive safety systems, hearing aid technology, sleep medicine, and agricultural monitoring. She has made significant contributions to head-related transfer function (HRTF) processing, real-time audio enhancement algorithms, and innovative monitoring systems that utilize acoustic signals for various applications. Analysis of Dr. Bruschi's recent publications reveals a strong trajectory in developing practical audio processing solutions with real-world applications. Her work shows increasing integration of machine learning techniques with traditional signal processing approaches, particularly in areas like driver monitoring systems, snoring detection and cancellation, and spatial audio rendering. A notable trend is her focus on creating lightweight, real-time implementations suitable for embedded systems and practical deployment scenarios, while maintaining high performance standards. Her research consistently demonstrates interdisciplinary collaboration, connecting audio engineering with fields as diverse as automotive safety, sleep medicine, and agricultural technology. Dr. Bruschi actively contributes to advancing audio engineering through her research on equalization techniques, noise reduction systems, and immersive audio technologies. Her work on pulse compression techniques for hearing aid distortion measurement represents an important contribution to audiological assessment methodologies. Her publication record demonstrates consistent scholarly output with increasing impact across multiple application domains, reflecting her ability to translate theoretical signal processing concepts into practical engineering solutions.
Daniele Caviglia serves as Full Professor in the Department of Naval, Electrical, Electronic and Telecommunications Engineering at the University of Genoa, Italy. He holds the position of Coordinator for the Master's Degree in Electronic Engineering and teaches advanced courses including Radio Frequency Electronics, Electronic Devices, and Electronic Systems for Telecommunication across both Bachelor's and Master's programs. His research program focuses on ultra-low-power electronics for biomedical and environmental applications, with three primary thrusts: (1) nW-scale circuit design for bio-signal processing and neural interfaces, (2) advanced beamforming techniques in medical ultrasound imaging, and (3) energy harvesting systems for autonomous environmental monitoring. His group has pioneered inverter-based OTAs achieving sub-10nW operation and developed novel genetic algorithm-optimized apodization methods for plane-wave ultrasound imaging. Recent publications (2024-2025) reveal strong thematic continuity with increasing emphasis on practical implementations - particularly sea wave energy harvesters for environmental buoys and satellite microwave link systems for rainfall monitoring in urban settings. The work consistently bridges fundamental circuit innovation with real-world medical and environmental applications, maintaining high impact in IEEE and Elsevier journals.
Dr. Gianluca Demartini is a leading researcher in Human-in-the-loop AI Systems with significant contributions to Crowdsourcing , Information Retrieval , and Generative AI applications. His work bridges Machine Learning and Human-Computer Interaction , focusing on Bias Management , Fact-Checking , and Ethical AI . Major Affiliations : L3S Research Center, ScienceWISE platform, and collaborations with institutions like University of Queensland and University of Padua Over 15 years, his research has explored Crowdsourcing Quality Control (Mechanical Cheat 2012), Entity Ranking (2008-2013), and Semantic Search . Recent work (2024-2026) focuses on Generative AI Impacts in domains like Media Literacy , Data Curation , and Visual Analytics . Scientific Recognition : Best Paper Award (Top 1.4%) at ICTIR 2023 Best Short Paper Award (Top 0.6%) at ECIR 2020 Honorable Mention (Top 2%) at CSCW 2020 Best Demo Award at ISWC 2011 3rd Best Paper at LA-WEB 2008 His 15 most recent publications (2024-2026) demonstrate expertise in LLM-based Content Moderation , Immersive Data Visualization , and Trustworthy AI Systems . He has pioneered methods for Bias Detection in Wikipedia (2013), Entity Ranking (2008-2013), and Human-AI Collaboration frameworks. His work consistently addresses ethical challenges in AI for Social Good and Responsible Data Science .
Roberto Zanino is a Full Professor of Nuclear Engineering at the Department of Energy (DENERG) of the Polytechnic of Turin, Italy. He serves as Advisor to the Rector for relations with European and international university networks and for the UniTe project, Undergraduate Research Opportunities Coordinator, and Project management functions of PoliToArgentina. He is also Scientific Advisor for the Partnership Agreement with NEWCLEO. Dr. Zanino earned his Laurea cum laude in Nuclear Engineering from Politecnico di Torino in 1984 and his Ph.D. in Energetics in 1989. His academic progression includes Assistant Professor (1990-91), Associate Professor (1992-2000), and Professor (2001-present). He previously served as Director of Alta Scuola Politecnica (2007-2010) and Head of the Graduate Program in Energetics (2011-present). His research spans computational fluid dynamics, concentrated solar power, controlled thermonuclear fusion, Generation IV nuclear fission reactors, and plasma physics. His work focuses on thermal-hydraulic analysis of liquid metal systems, superconducting magnet design for fusion applications, and concentrated solar power optimization. His recent publications demonstrate strong expertise in coupling computational tools for nuclear applications, particularly in CFD-system code integration for liquid metal systems and fusion magnet analysis. Dr. Zanino has received recognition as an IEEE Senior Member (2012) and has supervised numerous doctoral students working on topics including thermal-hydraulic analysis of heavy liquid metal systems, superconducting magnet simulation for fusion applications, and concentrated solar power modeling. He has extensive international experience, having worked at Max-Planck-Institut für Plasmaphysik, Massachusetts Institute of Technology, and University of Illinois at Chicago. He is actively involved in major fusion projects including ITER, DTT (Divertor Tokamak Test facility), and EUROfusion. His teaching portfolio includes Computational Heat and Mass Transfer, Nuclear Fusion Reactor Engineering, Solar Thermal Technologies, and Computational Thermal Fluid Dynamics at both master's and doctoral levels.
Riccardo Muradore is an Associate Professor at the University of Verona in the Department of Engineering for Innovation Medicine, within the Engineering and Physics section. His academic work focuses on robotics, control systems, and automation technologies with applications across various domains including industrial, surgical, and autonomous vehicle systems. Professor Muradore's research spans several key areas in robotics and control engineering: Industrial robotics and automation systems Teleoperation techniques for remote control of robotic systems Surgical robotics for medical applications Control and systems theory fundamentals Process control and fault detection methodologies Adaptive Optics technologies Unmanned Aerial and Ground Vehicles development His research is supported by numerous national and European projects as well as industry contracts, with notable projects including SARAS (Smart Autonomous Robotic Assistant Surgeon), MURAB (MRI and Ultrasound Robotic Assisted Biopsy), and ISUR (Intelligent Surgical Robotics). Professor Muradore holds several administrative roles including Communication and Third Mission Representative for the Department of Engineering for Innovation Medicine, and serves on various teaching committees and councils. He is affiliated with multiple research groups: ForMe - Formal Methods for the Design of Engineering Systems Networked Systems and Technologies PARCO – Parallel Computing Robotics, Artificial Intelligence and Control Sistemi robotici e automazione (Robotic systems and automation) His teaching portfolio includes Advanced Control Systems, Dynamic Systems, Robotics, Vision and AI, and other courses related to control engineering and robotics.
Fabio Furini is an Associate Professor at the Department of Computer Science, Automatics, and Management (DIAG) at Sapienza University of Rome since September 2021. Prior to this position, he served as a CNR researcher at IASI-CNR in Rome (2020-2021), Maître de Conférences at Université Paris-Dauphine, France (2013-2019), postdoctoral researcher at Université Paris-13, France (2012-2013), and research fellow at the University of Bologna (2011-2012). His educational background includes a Ph.D. in Control Engineering and Operations Research from the University of Bologna in 2011. He further obtained the Habilitation à Diriger des Recherches (HDR) in France in 2017 and the National Scientific Qualification for Full Professor in Operations Research in Italy in 2019. Fabio Furini conducts theoretical and methodological research on Combinatorial Optimization and Operations Research. His primary focus is on developing exact algorithms based on decomposition and reformulation techniques for integer linear programming problems. His research spans various applications including network optimization, graph theory, and combinatorial problems such as the maximum clique problem, bin packing problem, and vertex separator problem. His work often bridges theoretical developments with practical applications in transportation, logistics, and network security. His recent publications demonstrate a strong focus on exact algorithms for combinatorial optimization problems, particularly in network interdiction, bin packing with temporal constraints, and graph-based problems. His work consistently combines integer programming techniques with combinatorial search methods to develop novel formulations and efficient solution approaches that advance the state-of-the-art in these domains. Among his notable scientific awards are the Prime d'encadrement doctoral et de recherche (PEDR), which he received annually from 2014 to 2020, recognizing him among the top 15% of researchers in the French university system. He also holds the prestigious Habilitation à Diriger des Recherches from France (2017) and the National Scientific Qualification for Full Professor in Operations Research from Italy (2019). Fabio Furini has been actively involved in supervising PhD students and has served as principal investigator for numerous national and international research projects. His extensive network includes over 60 co-authors across European and American universities. He is also a member of the editorial boards for three prestigious international journals: Omega, Annals of Operations Research, and Discrete Applied Mathematics. His research activities include collaborations with various institutions across Europe and the United States, including Imperial College London and the University of Colorado. These collaborations have resulted in a robust research program focused on advancing the theoretical foundations and practical applications of combinatorial optimization.
Giuseppe Cavallaro is an Associate Professor at the Department of Physics and Chemistry , University of Palermo. His research focuses on nanotechnology , biopolymer composites , and cultural heritage conservation . Key research themes include: Halloysite clay nanotubes for drug delivery and pollutant removal Hybrid materials integrating chitosan , cellulose , and metal oxides Applications in sustainable agriculture , art conservation , and environmental decontamination Recent publications highlight his work on thermoresponsive nanocarriers , antimicrobial composites , and green building materials . Contact: giuseppe.cavallaro@unipa.it
Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.
Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.
