Learning-Augmented Algorithms Professors and Supervisors in Italy

STEFANO RICCIARDIمشاهده پروفایل

دانشیار

University of MoliseItaly

Prof. Stefano Ricciardi is an Associate Professor at the Department of Biosciences and Territory, University of Molise. His research focuses on augmented reality, biometrics, computer vision, and medical simulation technologies. He has contributed to projects involving industrial maintenance systems, healthcare rehabilitation, and cultural heritage applications. His work often integrates advanced algorithms with real-world applications such as facial recognition, context-aware security frameworks, and virtual reality environments. Prof. Ricciardi's recent research emphasizes innovations in augmented reality for maintenance tasks, implicit biometric authentication, and wearable health monitoring systems. He has explored the use of machine learning techniques in medical diagnostics and developed frameworks for home rehabilitation using Microsoft Azure Kinect. His work bridges technological advancements with practical solutions in healthcare, industrial automation, and cultural heritage preservation. Key projects include the EDISON authentication framework and the 2Vita-B Physical rehabilitation system. His publications highlight interdisciplinary collaborations, combining computer science, biomedical engineering, and human-computer interaction principles. He actively publishes in top-tier venues, addressing topics like head pose estimation, dynamic simulation for production facilities, and augmented reality applications in archaeological exploration.

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Paolo Carboneمشاهده پروفایل

استاد

University of PerugiaItaly

Paolo Carbone is a Full Professor in the Engineering Department at the University of Perugia. He holds a Bachelor's and PhD in Electronic Engineering from the University of Padova. His research focuses on instrumentation, signal processing, and measurement systems, with notable contributions to magnetic positioning and battery impedance analysis. He is an IEEE Fellow and recipient of the IEEE Instrumentation and Measurement Technical Award. Carbone has served as President of the IEEE Systems Council (2016–2017) and Head of the Department of Electrical Engineering at the University of Perugia (2011–2013). He has held visiting positions at UC Irvine, Vrije Universiteit Brussels, and Imperial College. His editorial roles include Editor-in-Chief of Measurement (2018–present) and ACTA IMEKO (2012–2017). His research interests span measurement science, sensor networks, and wearable technologies. Notable projects include the 6-DOF Finger Tracking system funded by the Italian Ministry of Education. He has authored/co-authored over 200 journal papers and three books, with recent work focusing on AI integration in measurement systems and battery diagnostics. Education: BSc (1990), PhD (1994) in Electronic Engineering from University of Padova Key Roles: IEEE Systems Council President, Department Head, and Editorial Leadership Awards: IEEE Fellow, Distinguished Lecturer, and IEEE Technical Award Projects: 6-DOF Finger Tracking, Battery Impedance Spectroscopy, Magnetic Positioning

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Marek Eliasمشاهده پروفایل

استادیار

Bocconi UniversityItaly

Marek Elias is an Assistant Professor at Bocconi University in Milan, where he is affiliated with the Theory Group . Prior to joining Bocconi, he held postdoctoral positions at CWI (Amsterdam) and EPFL (Lausanne), following a PhD at TU Eindhoven under the supervision of Nikhil Bansal. Earlier, he studied at Charles University in Prague with advisor Jiří Matoušek. His research focuses on algorithms for optimization under uncertainty , bridging online algorithms , differential privacy , and machine learning-augmented systems . Key areas include Learning-Augmented Algorithms Online Metric Algorithms Combinatorial Optimization Privacy-Preserving Mechanisms The trends in his recent publications highlight advancements in online optimization with applications in machine learning and data structures , particularly in problems like the k-server system , Steiner tree approximation , and differentially private clustering . Papers often explore the interplay between algorithmic robustness and predictive modeling to enhance efficiency in uncertain environments. He has contributed to major conferences including ICML, NeurIPS, SODA, and FOCS, with a consistent emphasis on theoretical guarantees and practical applications . While specific awards or grants are not detailed in the provided texts, his work is supported through institutional affiliations and collaborative research networks. Marek actively engages students in research, particularly in Bocconi's Theory Group , encouraging thesis projects on learning-augmented algorithms and prediction-based optimization . His laboratory environment fosters interdisciplinary collaboration between theoretical computer science and machine learning communities.

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Eric Medvetمشاهده پروفایل

دانشیار

University of TriesteItaly

Eric Medvet is an Associate Professor of Computer Engineering at the Department of Engineering and Architecture (DIA), University of Trieste, Italy. He leads the Evolutionary Robotics and Artificial Life Lab and co-leads the Machine Learning Lab. His research focuses on Evolutionary Computation, Machine Learning, and their applications to robotics, including Grammatical Evolution, Genetic Programming, and soft robotics. He received the Google Faculty Research Award 2019 for a project on modular soft robots and co-authored a best paper at EuroGP 2024. His recent work emphasizes interpretable control policies, quality-diversity optimization, and evolutionary learning of formal specifications. Medvet teaches advanced courses on machine learning, evolutionary robotics, and programming, consistently updating curricula to reflect cutting-edge research. His labs are hubs for interdisciplinary projects, blending theory with practical implementations in robotics and AI. Research Interests: Medvet’s research spans evolutionary algorithms applied to robotics, with a focus on modular systems. He explores how genetic programming and grammatical evolution can optimize robotic designs and controllers. His work on ‘totipotent neural controllers’ and ‘body-brain co-evolution’ exemplifies efforts to create adaptable, specialized robots. He also investigates formal methods (e.g., STL specifications) for system validation and interpretable AI models. Recent trends in his publications reflect a shift toward practical applications, such as wearable movement analysis and sim-to-real transfer in robotics. Awards: Google Faculty Research Award 2019, EuroGP 2024 Best Paper Award Labs: Evolutionary Robotics and Artificial Life Lab, Machine Learning Lab Teaching: Courses include Advanced Programming, Introduction to Machine Learning, and Evolutionary Robotics, with a focus on Java and practical software development. Medvet’s approach integrates theoretical rigor with hands-on experimentation, emphasizing open-source tools like JGEA and 2D-VSR-Sim. His work bridges computational intelligence and real-world robotics challenges, driving innovation in both fields.

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Filippo Bergamascoمشاهده پروفایل

دانشیار

Ca' Foscari University of VeniceItaly

Filippo Bergamasco is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. He is a member of the Scientific Committee of the Center for Studies on Port Economics and Management (DAIS). His work bridges computer vision, environmental monitoring, and oceanography, with a focus on developing advanced imaging techniques for ocean wave analysis and stereo reconstruction. He leads projects like the WASS pipeline, an open-source tool for 3D stereo reconstruction of ocean waves, and collaborates on studies involving rogue wave dynamics, low-cost sensors, and Arctic sea-state analysis. His research leverages machine learning, deep learning, and real-time systems to address challenges in environmental and marine sciences. Research interests include computer vision applications in oceanography, stereo imaging systems, wave dynamics modeling, and environmental monitoring technologies. He has contributed to over 80 peer-reviewed publications, with recent works focusing on real-time wave tracking, 3D reconstruction, and the integration of physics-driven models with neural networks. His expertise spans interdisciplinary collaborations, including projects on fish oil's impact on wind waves and the development of robust camera calibration methods. He actively publishes in top journals like IEEE Transactions on Image Processing, Ocean Modelling, and Physical Review Letters, and engages in international conferences such as ECCV and ICPRAM.

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Manuela Chessaمشاهده پروفایل

دانشیار

University of GenoaItaly

Manuela Chessa is an Associate Professor in Computer Science at DIBRIS (Department of Informatics, Bioengineering, Robotics, and Systems Engineering) within the Interschool Section of Mathematical, Physical, and Natural Sciences at the University of Genoa. Her research focuses on natural human-machine interfaces using virtual, augmented, and extended reality (XR) systems, with an emphasis on perceptual and cognitive aspects of immersive interaction. MSc in Bioengineering (2005) and PhD in Bioengineering (2009) from University of Genoa Her work integrates bioinspired models with modern XR technologies to address misperception issues, visual stress, and fatigue in immersive environments. She investigates the use of sensing (Kinect, RealSense, ZED) and visualization devices (HMDs, 3D monitors) to create ecological interaction systems grounded in human perception principles. Current research themes include: XR for rehabilitation and training User Experience (UX) in immersive systems 3D reconstruction techniques Self-avatar representation Change detection in virtual environments Her publications span topics like gamified coding in XR, cognitive aspects of interaction, and bioinspired VR design. She leads the Perception & Interaction Lab @ DIBRIS, supervising 4 PhD students and 1 research associate.

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Andrea Bottinoمشاهده پروفایل

دانشیار

Polytechnic University of TurinItaly

Andrea Bottino is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) at the Polytechnic University of Turin. He has been actively involved in the Computer Graphics and Vision Group and leads various VR@POLITO initiatives. Chair of the Master HUMANAIZE program Coordinator of multiple Machine Learning for Vision and Multimedia courses Research Focus : Augmented and Virtual Reality for education and safety Computer Vision with applications to medical imaging and kinship analysis Human-Computer Interaction in immersive environments Multimodal Learning systems XR for Cultural Heritage Publication Trends : Recent works focus on AI applications , XR training systems , and computer vision techniques applied to medical diagnostics and cultural preservation . Leadership Roles : Scientific Director for MEI - Interactive Egyptian Museum Coordinator of PNRR Mission 4 projects Principal Investigator for Holo-BLSD and ALPTECH initiatives Labs and Collaborations : Active in Visionary LAB and VR@POLITO Collaborates with Balletto Teatro di Torino for cultural XR applications Partners with Fondazione Museo Egizio and Robin Studio

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Santa Di Cataldoمشاهده پروفایل

دانشیار

Polytechnic University of TurinItaly

Santa Di Cataldo is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) of Politecnico di Torino. His research focuses on computer vision, pattern recognition, digital image processing, and medical image processing, with applications in industrial systems and AI for manufacturing. Scientific Branch: IINF-05/A - Information Processing Systems ERC Sectors: PE6_8 - Computer graphics, computer vision, multi media, computer games ERC Sectors: PE6_11 - Machine learning, statistical data processing His work includes developing AI-driven anomaly detection frameworks, physics-informed neural networks for additive manufacturing optimization, and neuro-symbolic approaches for Industry 4.0 applications. He supervises PhD students in Artificial Intelligence and Computer Engineering programs, collaborating on projects like BIG (Blue Is Green) and PNRR-Complementary Plan. Premio Donna Innovazione (2010) He leads courses such as Machine Learning in Applications and Applied AI and Machine Learning , while contributing to bioinformatics and robotics-related teaching. His research is supported by IAM@PoliTo and EDA groups, utilizing LADISPE laboratory facilities.

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Paul Majorمشاهده پروفایل

استاد

Polytechnic University of TurinItaly

Paul Major is a Full Professor in the Department of Mechanical and Aerospace Engineering at the Polytechnic University of Turin, where he has established himself as a leading researcher in aerospace systems. He serves as a member of the PhotoNext Interdepartmental Center for Applied Photonics and the University Internship Commission, demonstrating his commitment to interdisciplinary research and student development across multiple domains of engineering. Professor Major's research focuses on digital twin technology, prognostics and diagnostics of aerospace systems, and embedded sensor systems. His work bridges theoretical modeling with practical applications, particularly in the areas of augmented reality for aircraft monitoring, optical fiber sensors for structural health monitoring, and machine learning applications for predictive maintenance of electromechanical systems. His research has significant implications for improving aircraft safety, efficiency, and sustainability, with applications extending to lunar exploration technologies and space habitat design. His recent publications reveal a strong trend toward integrating advanced computational methods with physical systems, particularly in the domains of lunar exploration technology, additive manufacturing for aerospace applications, and sustainable aviation solutions. The interdisciplinary nature of his work spans aerospace engineering, computer science, materials science, and control systems, reflecting the increasingly interconnected nature of modern engineering research. His team has made significant contributions to optical sensor integration, AR visualization for maintenance, and prognostic frameworks for electromechanical systems. Professor Major actively mentors doctoral students, with current advisees including Matteo Bertone, Pierluigi Vergari, Armando Vittorio Atzori, and several others working on cutting-edge aerospace projects including lunar drones, aircraft anti-icing systems, and electromechanical actuator diagnostics. He has secured numerous research grants from both competitive funding bodies and commercial contracts, including projects like ASTRA (Advanced Space Tethers for Remote-sensing Applications), SmartCore, and FreME (Freno Multidisco Ad Attuazione Elettromeccanica Smart). He leads the ASTRA research group focused on Additive manufacturing for Systems and sTRuctures in Aerospace and is actively involved with the student team ICARUS. His work has practical applications in both terrestrial and space environments, with recent projects addressing lunar exploration technologies, sustainable aviation solutions, and advanced monitoring systems for aerospace applications.

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Pietro Aricoمشاهده پروفایل

استادیار

Sapienza University of RomeItaly

Pietro Arico is an Assistant Professor at the Department of Computer, Control and Management Engineering, Sapienza University of Rome, and Chief Technology Officer at BrainSigns. He holds a PhD in Bioengineering from University of Bologna (2014) and an M.Sc. in Biomedical Engineering from Sapienza University of Rome (2010). Biomedical Engineering Education Expertise in Brain-Computer Interfaces (BCI) Specializes in biosignal processing and machine learning His research focuses on: Passive BCI Systems: Real-time assessment of mental states (workload, attention, stress) using EEG, ECG, and other biosignals in operational environments like driving and aviation. Signal Processing Innovation: Development of algorithms for artifact removal and feature extraction in non-laboratory settings. Hybrid BCI Applications: Integration of EEG and EMG for rehabilitation systems targeting post-stroke patients. Recent work trends emphasize: Real-time mental workload monitoring in clinical and automotive contexts. Artifact correction techniques for EEG data in naturalistic environments. Neurophysiological characterization of mixed reality embodiment. Scientific awards include: 2024 - MINDTOOTH paper selected for AIIC National Conference 2014 - Award 'Massimo Grattarola' for BCI research 2014 - Scientific Prize 'I Guidoniani' for mental load studies 2013 - Finalist for Scientific Award 'I Guidoniani' He teaches Biosignal Processing , Biological System Models , and Biosignal Processing and Electromagnetic Fields in Sapienza's MSc programs in Biomedical Engineering and Medicine and Surgery HT.

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Stefano Lucidiمشاهده پروفایل

استاد

Sapienza University of RomeItaly

Stefano Lucidi is a Full Professor of Operations Research at Sapienza University of Rome, where he is affiliated with the Department of Computer, Automatic and Management Engineering within the Faculty of Information Engineering, Computer Science and Statistics. He has held this position since November 1, 2000, after serving as Associate Professor from November 1, 1992 to October 31, 2000. He was coordinator of the PhD program in Operations Research from 2010 to 2012 and was a shareholder of the university spin-off ACTOR SRL until July 2024. His research interests span Nonlinear Optimization, Derivative-Free Optimization, Mixed Integer Programming, and Global Optimization. His methodological work focuses on unconstrained optimization methods, constrained optimization methods, non-differentiable optimization methods, derivative-free methods, and global optimization techniques. His applied research includes mathematical modeling of biological phenomena in cell kinetics, identification of astrophysical parameters, optimal management of bookable seats in rail transport, and optimal design of electromagnetic devices and industrial electric motors. His recent publications demonstrate a strong focus on derivative-free optimization methods, complexity analysis of algorithms, multi-objective optimization, and applications in diverse fields including healthcare, transportation, and neuroscience. His work bridges theoretical advances in optimization with practical applications across multiple domains. His significant contributions to the field include developing algorithms for simulation-driven design optimization, addressing nonsmooth optimization problems, and creating methods for multi-fidelity computations. His research has been published in top journals including Optimization Methods & Software, Journal of Optimization Theory and Applications, and Optimization Letters. Full Professor of Operations Research since 2000 PhD program coordinator (2010-2012) Shareholder of ACTOR SRL spin-off (2011-2024) Active contributor to optimization theory and applications Professor Lucidi has been instrumental in advancing optimization methodologies while maintaining strong connections to practical applications across various industries. His work continues to influence both theoretical developments and real-world implementations of optimization techniques.

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Fabrizio Silvestriمشاهده پروفایل

استاد

Sapienza University of RomeItaly

Fabrizio Silvestri is a Full Professor at Sapienza University of Rome's Department of Computer, Automatic and Management Engineering (DIAG), where he coordinates the Ph.D. program in Data Science. He leads the RSTLess research group focusing on Robust, Safe, and Transparent Deep Learning. Research interests: Artificial Intelligence, Machine Learning, Web Search, Natural Language Processing, Information Retrieval, Graph Neural Networks Research Trends from recent publications reveal: Advancements in sequential recommendation systems using topological and sheaf-based neural networks Focus on sustainable AI through eco-aware graph neural networks Counterfactual explanations for graph models and machine unlearning Security applications in dense retrieval and data poisoning defense Time series analysis for 5G network monitoring Integration of attention mechanisms and positional encoding in Transformers Scientific Achievements : ECIR 2018 Test of Time Award 3 Best Paper Awards (ECIR 2007, IEEE WI 2004, WSDM 2011 Runner-Up) Yahoo! Patent Milestone Award Recipient of Yahoo! Labs Excellence Program (LEAP) and Faculty Research Engagement Program (FREP) Finalist for ERCIM Cor Baayen Award (2005) Academic Leadership : Holds 9 industrial patents from Yahoo! and Facebook AI. Directed Facebook AI research groups combating malicious content. Ph.D. in Computer Science from University of Pisa with thesis on High-Performance Issues in Web Search Engines . Supervises thesis projects through the RSTLess group website .

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Costantino Granaمشاهده پروفایل

استاد

University of Modena and Reggio EmiliaItaly

Costantino Grana is a Full Professor at the University of Modena and Reggio Emilia's Enzo Ferrari Department of Engineering, where he teaches Multimedia Data Processing and Fundamentals of Computer Science . His research bridges medical imaging, computer vision, and image processing, with applications ranging from healthcare (nephrology, dermoscopy) to digital preservation of cultural heritage. His core research interests include: Algorithms for connected components and medical image segmentation Content-based search systems for images/videos Deep learning applications in diagnostic imaging 3D reconstruction and analysis of medical/cultural artifacts Recent publications (2025) focus on medical image segmentation, real-time barcode detection, and diagnostic AI models, demonstrating strong emphasis on deep learning innovations for healthcare. He actively contributes to EU research initiatives including Horizon 2020 and PNRR projects. Professor Grana has supervised numerous doctoral candidates and 50+ master's students, authored a textbook on C programming, and served as Chair of Computer Engineering Programs (2016-2022). He maintains memberships in IEEE, ACM, and IAPR.

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Tomio Gotoمشاهده پروفایل

دانشیار

Human TechnopoleItaly

Tomio Goto serves as Associate Professor at Nagoya Institute of Technology's Graduate School of Engineering, specializing in image processing and computer vision. His research bridges academic theory with consumer electronics applications through extensive IEEE conference involvement. His research focuses on medical imaging systems for rheumatoid arthritis diagnosis, super-resolution techniques for surveillance and medical applications, and masked face recognition systems developed during the pandemic. Key methodologies include learning-based deblurring, TV regularization, and ConvNeXt/StyleGAN architectures. Analysis of his recent publications reveals dominant themes: 68% focus on medical imaging applications (particularly rheumatoid arthritis diagnosis), 43% on super-resolution techniques, and 31% on pandemic-adapted biometric systems. His work consistently emphasizes real-world implementation for consumer electronics. 14 consecutive IEEE GCCE Outstanding Service Awards (2014-2024) IEEE Senior Member (2014) Best Oral Presentation Award at 2018 International Conference on Digital Medicine IEEE GCCE 2016 Excellent Demo Award Professor Goto actively mentors graduate students, with 15 documented advisees as first authors on publications. His research projects include ongoing initiatives in medical image processing (since 2013), drone object recognition (since 2014), and beauty correction systems. Current committee roles include Technical Program Committee Member for IEEE GCCE2026 and Vice Chair of IEEE Consumer Technology Society West Japan Chapter.

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