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Giorgio Stefano GneccoView profile

Professor

• Optimization
• Machine Learning
• Game Theory
• +6 more

Giorgio Stefano Gnecco is a Full Professor in Mathematical Methods of Economics and of Actuarial and Financial Sciences at IMT School for Advanced Studies Lucca, where he works within the Analysis of compleX Economic Systems (AXES) research unit. His academic career spans multiple disciplines including optimization, machine learning, game theory, and their applications in economics, finance, and engineering. His research interests focus on optimization applied to actuarial sciences, economics, finance, and engineering; game theory; statistics; machine learning theory and applications; causal inference for economic policy evaluation; and environmental economics. His work demonstrates interdisciplinary connections between mathematical theory and practical applications across diverse fields, with particular emphasis on developing computational methods for complex economic systems. His publication record shows consistent output across multiple domains, with recent work spanning machine learning algorithms, image processing techniques, economic modeling, and applications in music performance analysis. The breadth of his research indicates strong methodological foundations in mathematical optimization and statistical learning, applied to problems ranging from flood hazard assessment to Parkinson's disease classification. Among his notable achievements are five Italian National Scientific Qualifications for professorial positions in various fields, demonstrating his recognized expertise across multiple academic disciplines. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, Action Editor for Neural Networks, and Associate Editor for Neurocomputing. Professor Gnecco leads numerous research projects including the "PRIN PNRR 2022" project "MOTUS - Automated Analysis and Prediction of Human Movement Qualities," the "INdAM-GNAMPA 2023" project on machine learning methods for Shapley Value estimation, and the "ROBOFARM" project on edge computing for precision agriculture. He has coordinated multiple international research collaborations between Italy and France through the Galileo program. His research unit (AXES) focuses on complex economic systems analysis, with applications spanning environmental economics, financial systems, and human movement analysis. The collaborative nature of his work is evident through his extensive network of domestic and international collaborators across multiple universities and research institutions.

Leonardo CappelloView profile

Assistant Professor

• Haptics
• Assistive Robotics
• Rehabilitation Robotics
• +4 more

Leonardo Cappello is an Assistant Professor at the BioRobotics Institute of Scuola Superiore Sant'Anna (Italy), where he founded the Textile Robotics Laboratory in 2022. His work focuses on textile-based robotic wearables for human sensorimotor restoration and augmentation. BS in Mechanical Engineering (2009) MS in Biomedical Engineering (2011) PhD in Robotics, Cognition, and Interaction Technologies (2016) His research spans haptics , assistive robotics , prosthetics , and motor neuroscience , with a focus on wearable systems for sensorimotor restoration. Recent publications highlight trends in soft robotics , haptic interfaces , and augmented feedback for neurorehabilitation. Scientific recognition includes: ERC Starting Grant (2024) Best Student Paper Award at IEEE ICORR 2015 He leads the Textile Robotics Laboratory within the Artificial Hands Area, developing innovative solutions for upper limb rehabilitation and augmentation.

• Gynecological Oncology
• Minimally Invasive Surgery
• Fertility Preservation
• +4 more

Marco Monti serves as Associate Professor at Sapienza University of Rome's Department of Maternal and Child Health and Urological Sciences, where he teaches Obstetrics (Corso di Laurea Ostetricia) with specific focus on childbirth assistance. His clinical expertise spans gynecological oncology, minimally invasive surgery, and reproductive medicine, supported by extensive publications in high-impact journals. His primary research interests include: Gynecological cancer management (particularly ovarian and cervical malignancies) Fertility-sparing treatments for early-stage endometrial/cervical cancers Innovative endometrial preparation techniques for hysteroscopic procedures Non-invasive therapies for genitourinary syndrome of menopause Obstetric complications in high-risk pregnancies Analysis of his 2022-2025 publications reveals three dominant trends: (1) Optimization of rapid-start hormonal protocols for outpatient hysteroscopy, (2) Advancement of PARP inhibitor therapies in ovarian cancer, and (3) Expansion into public health domains including pandemic risk assessment and disaster response. His work consistently bridges clinical practice with translational research through multicenter trials. Dr. Monti leads two major research initiatives: Conservative treatment of early-stage endometrial cancer - Investigating fertility preservation and obstetric outcomes Conservative treatment of early-stage cervical cancer - Evaluating pregnancy outcomes after conservative management His research receives institutional support through Sapienza University frameworks, with recent projects examining minimally invasive surgical approaches and menopausal symptom management using CO2 laser therapy.

• Artificial Intelligence
• Machine Learning
• Robotics
• +5 more

Raffaello Camoriano is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Polytechnic University of Turin. He is also an Affiliated Researcher at the Istituto Italiano di Tecnologia. His academic work spans multiple roles including serving as an Editorial Board Member for MACHINE LEARNING journal (since 2024) and Associate Editor for IEEE ROBOTICS AND AUTOMATION LETTERS (since 2022). He is an active member of IEEE Robotics and Automation Society (since 2024) and ELLIS - European Laboratory for Learning and Intelligent Systems (since 2022). Dr. Camoriano's research focuses on the intersection of artificial intelligence, machine learning, and robotics. His work particularly emphasizes robot learning, incremental/lifelong learning, structured learning, and kernel methods. His research has significant applications in medical robotics, particularly in laser-based skincare procedures and prosthetic control systems. He has developed innovative approaches for federated learning, point cloud processing for robotics, and trajectory generation for humanoid robots. His work bridges theoretical machine learning with practical robotics applications, addressing challenges in resource efficiency, adaptation to changing environments, and human-robot interaction. His recent publications demonstrate a strong trajectory in advancing federated learning techniques, with particular emphasis on resource-efficient personalization and handling heterogeneous data. He has made significant contributions to robotics applications including upper-limb prosthetics using high-density sEMG, autonomous navigation using point clouds, and medical robotics for laser procedures. His work consistently combines theoretical machine learning advances with concrete robotics applications, showing expertise in both domains. Best PhD Thesis Award of the Computational Intelligence Society Italy Chapter 2017 Dr. Camoriano actively supervises PhD students in the Intelligenza Artificiale program, including Stephany Ortuno Chanelo, Andrea Protopapa, and Leonardo Iurada. He has led research projects including the ECML-PKDD 2023 International Workshop 'Adapting to Change: Reliable Learning Across Domains' and the International Workshop 'Learning Meets Model-based Methods for Manipulation and Grasping' (2023). His teaching portfolio includes Fundamentals of Artificial Intelligence, Machine and Deep Learning, and Algorithms and Programming across multiple engineering programs at Polytechnic University of Turin.

• Condensed matter theory
• Quantum materials
• Superconductivity
• +7 more

Matteo Calandra Buonaura is a Full Professor at the Department of Physics, University of Trento, Italy, since February 2020. He also holds a permanent position as Directeur de Recherche de première classe at CNRS (Centre National de la Recherche Scientifique), Sorbonne Université, Paris, France. His career spans postdoctoral research at Max Planck Institute (Stuttgart) and Université P. et M. Curie (Paris), followed by CNRS research roles since 2003. Laurea in Theoretical Physics (University of Parma, 1995) PhD in Theoretical Condensed Matter Physics (SISSA, Trieste, 1999) His research focuses on condensed matter theory with emphasis on quantum materials , superconductivity , and 2D crystals . He develops density functional theory and machine learning frameworks to study vibrational properties , charge density waves , and strongly anharmonic solids under extreme conditions. His group (mattheory.physics.unitn.it) integrates numerical methods and quantum spectroscopy to explore graphene , ferroelectrics , and hydrogen-rich materials . Recent publications investigate light-induced phase transitions , chiral phonons , and machine learning for ab initio accuracy in novel 2D systems. His work bridges fundamental quantum physics and computational materials science . Scientific Awards ERC Advanced Grant (2022) Google Gift Awards for quantum technologies (2021, 2022) Prime d'excellence scientifique CNRS (2012, 2018) He has supervised research teams at CNRS and University of Trento, secured 7 PRACE/EuroHPC supercomputing grants , and contributed to the Graphene Flagship consortium and IIT Graphene Labs . His teaching includes Mechanics , Transport in Nanostructures , and advanced theoretical courses on 2D materials.

• Computational Biophysics
• Molecular Dynamics
• Polymer Physics
• +5 more

Luca Tubiana is a Full Professor of Applied Physics at the Department of Physics, University of Trento, Italy. He previously held academic positions as Assistant Professor (2005-2015) and Associate Professor (2015-2021) at the University of Bari and University of Foggia, respectively. He earned a High School Diploma in Bari (1992), an M.Sc. in Physics from the University of Bari (1997), and a Ph.D. in Condensed Matter Theory from SISSA Trieste (2001). Marie Curie Individual Fellowship (2002-2004) at Hahn-Meitner Institut, Berlin Marie Curie European Reintegration Grant (2004) at University of Bari His research focuses on applying computational and theoretical methods to biological and medical problems, particularly in polymer physics, DNA topology, and viral RNA structure. He has contributed to understanding knotting dynamics, topological indicators in macromolecules, and coarse-graining tools for biomolecular simulations. His recent work explores active matter, metaplasticity in neural networks, and topological entanglement in soft matter systems. Key teaching activities include Computational Biophysics (with Gianluca Lattanzi) and Introduzione alla Data Science per fisici at the Department of Physics. His scientific awards include Marie Curie Individual Fellowship Marie Curie European Reintegration Grant .

• AI Explainability
• AI Transparency
• Web Science
• +3 more

Marco Brambilla is a Full Professor of Computer Science and Engineering at Politecnico di Milano, Italy. He leads the Data Science Lab within the DEIB (Department of Electronics, Information and Bioengineering) and serves as director of the Computer Science and Engineering B.Sc. and M.Sc. curricula at the university. His research focuses on AI Explainability and Transparency , Web Science , Big Data Analysis , Social Media Analytics , and Model-driven Development . He is the co-inventor of the Interaction Flow Modeling Language (IFML) standard by the OMG and holds 2 patents on crowdsourcing and multi-domain search. His recent scholarly work demonstrates expertise across multiple domains of computer science, with particular emphasis on graph-based RAG architectures for energy efficiency, human-AI interaction studies, and interpretable network visualizations for CNN-based image classification. His publications show a consistent focus on practical applications of theoretical computer science concepts. Co-inventor of IFML standard by OMG 2 patents on crowdsourcing and multi-domain search GSE Academic Award for Excellence for correlating twitter sentiment analysis and stock price variations Author of multiple books including Model Driven Engineering in Practice , Web Information Retrieval , and Model-Driven UI Engineering of Web and Mobile Apps with IFML Professor Brambilla has been involved in the creation of four startups: WebRatio, Servitly, Fluxedo, and Quantia. He teaches Enterprise ICT Architectures, Systems and Methods for Big and Unstructured Data, Web Science, Digital Innovation Lab, and Model-driven Engineering courses. His Data Science Lab at Politecnico di Milano drives research in multiple cutting-edge areas of computer science and AI.

Maria PrandiniView profile

Professor

• Distributed Optimization
• Stochastic Hybrid Systems
• Randomized Methods for Systems and Control
• +3 more

Maria Prandini is a Full Professor at the Politecnico di Milano , where she chairs the Automation and Control Engineering Program within the Department of Electronics, Information, and Bioingegneria. With a PhD in Information Technology from the University of Brescia (1998), she conducted postdoctoral research at UC Berkeley (1998-2000) and held visiting positions at Delft University, Cambridge, and ETH Zurich. Research Interests: Her work spans Distributed and Stochastic Optimization Stochastic Hybrid Systems Randomized Methods for Control Air Traffic and Energy Management Identification Theory Recent Article Trends: Her publications focus on cyber-physical systems, multi-agent optimization, unmanned aircraft control, and computational geometry methods, with a strong emphasis on real-world applications in energy and transportation. Scientific Awards: IEEE CSS Distinguished Member (2018) IEEE Fellow (2019) Editorial & Conference Roles: She serves on editorial boards of Automatica , IEEE Transactions on Network Systems , and Cyber Physical Systems , and has chaired major conferences like IEEE CDC and IFAC World Congress. Teaching: Courses include Hybrid Systems, Nonlinear Control, and courses at Lund University, University of Brescia, and Politecnico di Milano.

• Quantum Mechanics
• Foundations of Quantum Physics
• Quantum Gravity
• +3 more

Angelo Bassi is a Full Professor at the Department of Physics, University of Trieste. He serves in multiple administrative roles including as Delegato per la mobilità studentesca internazionale in the Head's Office, and as a member of the Department's Board, Boards of Studies, and Doctoral Studies Boards across multiple physics programs. His research focuses on the foundations of quantum mechanics, quantum gravity, and quantum information science. He leads investigations into spontaneous wave function collapse models, quantum measurement problems, and the interface between quantum mechanics and gravity. His work bridges theoretical physics with potential experimental tests of quantum foundations. Professor Bassi's research spans quantum mechanics foundations, collapse models, quantum gravity, and quantum technologies. His work on the Schrödinger-Newton equation, spontaneous collapse models like Continuous Spontaneous Localization (CSL), and the quantum nature of gravity represents cutting-edge research at the boundary of quantum theory and general relativity. His publications reveal a strong focus on both theoretical frameworks and experimental tests of quantum foundations. His recent work shows a clear trend toward testing the quantum nature of gravity through various experimental approaches including atom interferometry, diffusion processes, and quantum communication protocols. His publications cover quantum information, quantum gravity, and foundational quantum mechanics, with particular emphasis on collapse models and their experimental verification. Professor Bassi leads or participates in multiple research projects including "QuCoM - Quantum Control of Gravity with Levitated Mechanics," "Quantum Italy Deployment," "National Quantum Science and Technology Institute," and "Collapse Points Quantum Mechanics." His work is supported by various funding sources including INFN (Istituto Nazionale Fisica Nucleare). He has served on multiple Doctoral Studies Boards for Physics programs across various cycles (XXIX to XXXIX), demonstrating his commitment to graduate education and mentorship. While specific students aren't listed in the provided information, his extensive involvement in doctoral programs suggests active supervision of PhD candidates. Professor Bassi is a member of the Quantum Mechanics and Foundations research group within the Theoretical Physics research area, focusing on Quantum Science and Technologies. His research has significant implications for understanding the quantum-classical boundary and the potential unification of quantum mechanics with gravity.

Yuri BorgianniView profile

Associate Professor

• Engineering Design
• Product Development
• Design Creativity
• +7 more

Yuri Borgianni is an Associate Professor at the Faculty of Engineering, Free University of Bozen-Bolzano (unibz), Italy. He teaches courses including Technical Drawing, Reverse Engineering & Rapid Prototyping, and Design Creativity. His research focuses on engineering design, product development, and sustainable innovation. Borgianni has established himself as a prominent researcher in design methodology, with particular expertise in TRIZ (Theory of Inventive Problem Solving) and Additive Manufacturing applications. Dr. Borgianni earned his Master's Degree in Mechanical Engineering from the University of Florence in 2005, followed by a Ph.D. in Industrial Engineering from the same institution in 2014. His academic journey has led him to specialize in the intersection of design creativity, sustainable development, and advanced manufacturing technologies, positioning him at the forefront of innovative research in engineering design. Professor Borgianni's research interests span value innovation, ideation within engineering design, creative product development, industrial process re-engineering, and computer-aided innovation. He has made significant contributions to understanding how sustainability principles can be effectively integrated into product design processes. His work often combines theoretical frameworks with practical applications, particularly exploring the potential of Additive Manufacturing for sustainable production. Borgianni has also investigated user experience aspects in design evaluation through advanced methodologies like eye-tracking and virtual reality. His recent publications reveal a strong focus on sustainable design education, manufacturing process selection using machine learning, circular economy implementation in product development, and the application of TRIZ for environmental problem-solving. Borgianni's work demonstrates a consistent trend toward integrating digital technologies with sustainable design principles, with increasing attention to circular economy metrics and the human factors involved in sustainable product evaluation. While specific scientific awards are not prominently mentioned in the available information, Professor Borgianni's extensive publication record (over 160 publications) and his role as editor for special issues on design processes indicate significant recognition within his field. His work has been cited over 50,000 times according to ResearchGate metrics, reflecting substantial impact in engineering design research. Professor Borgianni actively supervises research projects and mentors students in the Faculty of Engineering at unibz. His research has been supported by various grants focused on sustainable product development, additive manufacturing applications, and design methodology innovation. He collaborates extensively with researchers across Europe, particularly in Italy, Germany, and the UK, contributing to international projects that bridge academic research with industrial applications in sustainable manufacturing. At the Free University of Bozen-Bolzano, Borgianni is involved in research initiatives related to sustainable engineering design, working with interdisciplinary teams that combine expertise in mechanical engineering, computer science, and environmental science. His work with the university's engineering faculty contributes to the institution's focus on innovative, sustainable technologies and their practical implementation in industry.

• Machine Learning
• Artificial Intelligence
• Human-AI Collaboration
• +3 more

Burcu Sayin Günel serves as a Research Fellow at the Department of Information Engineering and Computer Science, University of Trento, where she conducts cutting-edge research at the intersection of artificial intelligence and real-world critical systems. Her work bridges theoretical machine learning with practical implementations in high-stakes environments requiring human oversight. Her research portfolio centers on: Value-aware and cost-sensitive machine learning frameworks Human-AI collaboration mechanisms for medical diagnostics Safety-critical integration of ML in cyber-physical infrastructure Hybrid intelligence systems combining human cognition with algorithmic processing Active learning optimization for resource-constrained scenarios Economic valuation of machine learning models Analysis of her 2021-2025 publications reveals a consistent trajectory toward making AI systems more reliable, economically viable, and human-compatible. Key contributions include novel methodologies for prediction rejection in critical CPS, collaborative diagnostic tools like MedSyn for physician-AI teams, and value-based active learning frameworks that optimize data acquisition costs. Her work increasingly addresses medical domain challenges while maintaining strong foundations in cyber-physical systems safety. No scientific awards, grant funding details, or student supervision activities were documented in the available materials. Similarly, no specific laboratory affiliations or team leadership roles were mentioned within the provided institutional context.

• Data Science
• Machine Learning
• Natural Language Processing
• +7 more

Luca Cagliero is an Associate Professor (L.240) at the Department of Control and Computer Science (DAUIN) at Politecnico di Torino . He serves as Coordinator of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory and Scientific Advisor for the Partnership Agreement with TIERRA. His research spans Data Science , Machine Learning , and Natural Language Processing , with a focus on Financial Data Mining , Generalized Pattern Mining , and Legal AI . Scientific Branch: IINF-05/A - Information Processing Systems (Area 0009 - Industrial and Information Engineering) ERC Sectors: PE6_7 (Artificial Intelligence), PE6_9 (Human Computer Interaction), PE6_11 (Machine Learning), PE6_10 (Web and Information Systems) His scientific awards include the GiovedìScienza Award (2013), Working Capital PNI (2011), and Optime. Recognition of Merit in the Study (2008). He is a Fellow of ACM (2010-) and Effective Member of IEEE (2010-). As an Associate Editor for journals like Expert Systems with Applications and Machine Learning with Applications , he contributes to academic publishing. His conference roles include Program Committee memberships at ACM SIGMOD 2023, IEEE ICDM 2021, and ACM CIKM 2018. Luca supervises PhD students in areas such as AI-driven Cybersecurity , Conversational AI , and Neural Explainers , including Aurora Gensale, Giuseppe Gallipoli, and Irene Benedetto. His commercial research contracts involve projects like AI for Trend Analysis (Intesa Sanpaolo), Predictive Maintenance , and Legal Document Processing . Key research trends include Retrieval Augmented Generation for visually-rich documents, Bias Mitigation in speech models, and Shapley Value Estimation for model explainability. His work bridges Natural Language Processing with Cybersecurity in automotive systems.

Giuseppe Carlo CalafioreView profile

Professor

• Convex Optimization
• Control Systems
• Randomized Algorithms
• +6 more

Giuseppe Carlo Calafiore is a Full Professor at the Department of Electronics and Telecommunications (DET) of the Polytechnic University of Turin , where he has been since 1998. He has held visiting positions at institutions like Stanford University , University of California at Berkeley , and Vietnam National University . His research focuses on convex optimization , control systems , and randomized algorithms , with applications in finance , robotics , and machine learning . Education : Laurea in Electrical Engineering (1993) and Ph.D in Information and System Theory (1997) from Politecnico di Torino. Research Interests : Span large-scale optimization , robust portfolio optimization , and distributed algorithms , with emphasis on sparsity-based methods and data-driven control for autonomous systems. Recent Publications include works on nonlinear model predictive control , financial network robustness , and adaptive gain control in population games. His scientific awards include the IEEE CSS George S. Axelby Outstanding Paper Award (2008) and Fellow of the IEEE (2018) . He has advised students in artificial intelligence and communications engineering programs. His projects include research on smart energy allocation and financial risk modeling .

• Autonomous vehicles
• Drones
• Embedded systems
• +12 more

Marcello Chiaberge is an Associate Professor at the Department of Electronics and Telecommunications (DET) of the Polytechnic University of Turin. He serves as Coordinator of the Interdepartmental Center for Service Robotics (PIC4SeR) and oversees quality management for the Master's Degree in Mechatronic Engineering. With over 110 publications and 9 international patents, his work bridges academic research and industrial applications in robotics and electronics. Research Interests span a wide array of fields including Autonomous vehicles Drones Embedded systems Precision agriculture Search and rescue robotics Simultaneous localization and mapping (SLAM) Smart cities infrastructure His projects often integrate machine learning with hardware acceleration, focusing on real-time solutions for robotics applications. Recent Publications highlight his contributions to: Deep reinforcement learning for robot navigation Hybrid MPPT algorithms for solar energy optimization UAV-based remote sensing in agriculture Transformer architectures for action recognition Hardware-accelerated computer vision systems Research Leadership is evident in his coordination of PIC4SeR and principal investigator roles in projects like: HERITALISE (2025-2028) - Cultural heritage digitization EMPATHY (2024-2026) - Empathetic mobility platforms NG-UWB (2019-2023) - Next-generation localization systems MArcEL (2019-2022) - Electric agricultural machinery His teaching includes courses on electronic fundamentals and mechatronic systems, all delivered in English at the Master's level. He has served as doctoral consortium member since 2002 and currently supervises multiple PhD candidates in electronics and mechatronics fields.

• Operations Research
• Mathematical Programming
• Management Science
• +2 more

Edoardo Fadda is a Fixed-term tenure-track Assistant Professor at the Department of Mathematical Sciences (DISMA), Politecnico di Torino . He serves as a member of the College of Mathematical Engineering and College of Electronic, Telecommunications and Physics Engineering . Specializes in Operations Research and Mathematical Programming Active in stochastic optimization , reinforcement learning , and control applications Teaching roles include Optimization Methods for Control Applications and Stochastic Programming courses His research spans supply chain optimization , logistics , and AI-integrated decision systems , focusing on uncertainty modeling and multi-stage stochastic programming. He leads the Development of Decision Support Systems and the SUPERSONIC project for ecological logistics, alongside commercial consulting for RIDIX SPA through Fondimpresa contracts. Notable collaborations include Paolo Brandimarte and Francesca Maggioni . Edoardo supervises PhD candidates Alessia De Crescenzo (39th cycle) and Lorenzo Mazza (40th cycle). His publications emphasize stochastic customer behavior , perishable product policies , and kernel-based system identification , with applications in aerospace, smart cities, and industrial manufacturing.