جستجوی استاد راهنمای دانشگاهی

• Sub-Riemannian Geometry
• Mathematical Analysis
• Neurogeometry
• +۳ مورد دیگر

Giovanna Citti is a Full Professor in the Department of Mathematics at the University of Bologna. Her research focuses on sub-Riemannian geometry, mathematical analysis, and neurogeometry, with a particular emphasis on modeling the functional architecture of the visual and motor cortices. She develops geometric frameworks to understand neural processing in the brain, including models of visual perception and motor control. Her work integrates concepts from differential geometry, partial differential equations (PDEs), and Lie group theory to analyze sub-Riemannian structures. She explores regularity properties of subelliptic PDEs and applies these to biological systems, such as the cortical organization of the visual cortex and motor pathways. Recent efforts include developing brain-inspired neural networks and studying neurogeometric models of stereo vision and 3D perception. Key contributions include sub-Riemannian models of perceptual completion, cortical-based optimal transport frameworks, and analysis of geometric optical illusions. Her research bridges pure mathematics and neuroscience, addressing challenges in both fields through innovative interdisciplinary approaches.

Andrea Torselloمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Andrea Torsello is a Full Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. His research focuses on Computer Vision, Machine Learning, and Artificial Intelligence with applications in quantum computation, graph theory, and 3D modeling. He is actively involved in teaching courses on quantum computation, artificial intelligence foundations, and computer science fundamentals at both undergraduate and graduate levels. Prof. Torsello's research interests include graph neural networks, quantum machine learning, and structural pattern recognition. His work often bridges theoretical computer science with practical applications in cultural heritage digitization and environmental monitoring. His recent articles highlight advancements in graph generation via spectral diffusion, quantum lens-based 3D shape analysis, and interpretable graph neural networks. He serves on the editorial boards of journals like Pattern Recognition and has contributed to projects funded by the EU’s H2020 program, including the VEiL initiative for visualizing engineered landscapes. His teaching spans topics such as quantum computation, advanced algorithms, and machine learning across multiple engineering programs.

Daniele Nardiمشاهده پروفایل

استاد

• Cognitive Robotics
• Robotic Soccer
• Emergency Robotics
• +۴ مورد دیگر

Daniele Nardi is a Full Professor at Sapienza University of Rome, affiliated with the Faculty of Information Engineering, Computer Science, and Statistics, and the Department of Computer, Control, and Management Engineering "A. Ruberti". He has held this position since 2000 and previously served as a researcher (1988) and associate professor (1992) at the same institution. His academic journey began with a Laurea in Electronic Engineering from Politecnico di Torino (1981) and a Specialization in Control Systems Engineering from Sapienza (1984). Research interests include: Cognitive Robotics Robotic Soccer Emergency Response Robotics Assistive Robotics for Elderly Precision Agriculture Robotics Recent article trends focus on: Synthetic data generation for agricultural monitoring LLM-driven multi-agent planning systems Signal temporal logic applications in robotics Self-supervised learning techniques Virtual reality-based HRI evaluation Embodied AI and online grounding Scientific recognition includes: IJCAI Publisher's Prize 1991 Intelligenza Artificiale award 1993 RoboCup Mid-Size II Place 1998 AAMAS Best Robotic Demo 2008 ECCAI Fellow 2009 He leads the Cognitive Robot Teams laboratory and serves as President of the RoboCup Federation since 2011. As Presidente del Consiglio d'Area in Computer Engineering since 2004, he has shaped academic governance. His teaching includes Seminars in Artificial Intelligence and Robotics at the Master in AI and Robotics program, and Complements of Programming at the undergraduate level.

• Deep Learning
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Stefano Coniglio is an Associate Professor of Computer Science and Artificial Intelligence at the University of Bergamo (UniBg), where he joined in 2022 after earning his PhD in Information Technology from the Polytechnic University of Milan in 2011 and conducting research abroad in Germany and the United Kingdom for ten years. His research focuses on deep learning models, machine learning using neural networks, algorithm design and analysis, and mathematical optimization. With over 50 scientific publications in international journals and conference proceedings, his work spans multiple domains including energy systems, medical applications, and theoretical computer science. Coniglio has made significant contributions to graph neural networks, as evidenced by his highly cited papers on quaternion-valued Laplacians and directional hypergraph neural networks. His research trends show a strong interdisciplinary approach, with publications bridging computer science with energy systems, cardiology, and mathematical optimization. His work on S-ICD screening demonstrates practical medical applications of deep learning, while his theoretical contributions to graph neural networks have advanced the field of geometric deep learning. Since November 2023, Coniglio has been a member of the UniBg Interdepartmental Working Group on Artificial Intelligence, collaborating on the design of an interdepartmental doctoral program in AI. He serves as the unit leader for the PRIN-PNRR project HEXAGON: Highly specialized EXact Algorithms for Grid Operations at the National level since 2023. Coniglio teaches Computer Science, Artificial Intelligence, and Deep Learning courses across multiple departments at UniBg, including the Department of Economics and the Department of Letters, Philosophy, and Communication. He has collaborated with major organizations including National Grid Plc, UK MetOffice, Airbus, Babcock International, British Telecom, CESI, and CISL, demonstrating strong industry connections and practical application of his research.

• Geometric Deep Learning
• Compositional Learning
• Optimal Transport
• +۴ مورد دیگر

Mircea Petrache is an Assistant Professor at the Department of Mathematics, Pontifical Catholic University of Chile. His research focuses on geometric deep learning, compositional learning, optimal transport, calculus of variations, and geometric measure theory, with applications to large point configurations and mathematical analysis. He holds a PhD in Mathematics from ETH Zürich (2013) and an MSc from Scuola Normale Superiore (2008). Education: PhD in Mathematics, ETH Zürich, 2013 MSc in Mathematics, Scuola Normale Superiore, 2008 Research Interests: Petrache explores the interplay between geometric principles and machine learning, particularly in equivariant networks and compositional systems. His work on optimal transport and calculus of variations addresses energy minimization and crystallization problems in materials science. Recent projects include symmetry-based structured matrices and topological pseudodistances for data analysis. Grants: Fondecyt Regular Grant (2021–2025) on rigidity and uniformity in large point configurations. Events & Organization: Co-organized workshops such as 'Theoretical and Mathematical Aspects of Deep Learning' (2022) and 'Calculus of Variations: A New Generation' (2024). Active in conferences including NeurIPS, ICIAM, and AIM workshops on discrete geometry.

Riccardo Marinمشاهده پروفایل

پژوهشگر

• Spectral Shape Analysis
• Geometric Deep Learning
• Virtual Humans
• +۳ مورد دیگر

Riccardo Marin is a Post-Doctoral Researcher at the University of Tuebingen, supported by the Humboldt Post-Doctoral Fellowship. He is part of the Real-Virtual Humans group led by Prof. Gerard Pons-Moll. Previously, he conducted post-doctoral research at Sapienza University of Rome in the GLADIA group under Prof. Emanuele Rodolà, contributing to the SPECGEO ERC project. He earned his Ph.D. in Computer Science from the University of Verona under Prof. Umberto Castellani, winning the Best PhD Thesis Award by EG-Italy. He holds a Master's degree in Computer Science and Engineering from the University of Verona (2017). His research focuses on Spectral Shape Analysis, Geometric Deep Learning, and Virtual Humans, with contributions to top-tier conferences and journals (NeurIPS, IJCV, CVPR). Key achievements include a Best Student Paper Award at 3DV 2020 and five Outstanding Reviewer Awards. He actively serves as a reviewer for leading journals and conferences (PAMI, TCVG, CVPR, NeurIPS) and has organized events like Siggraph RCD and 3DV 2018. Collaborations include institutions like École Polytechnique, Max-Planck Institute, INRIA Strasbourg, UCL, and the University of Milan. His work is supported by grants from Google and the European Research Council (ERC). His research bridges geometry processing with machine learning, emphasizing spectral methods and shape correspondence. Notable projects include MoMaS (real-time procedural texturing), FARM (3D human body registration), and contributions to latent space analysis for shape modeling. He maintains an active GitHub profile with repositories like NICP and InstantRecoveryFromSpectrum .

• Machine Learning
• Cyber-Physical Systems
• Intelligent Embedded Systems
• +۵ مورد دیگر

Cesare Alippi is a Professor at Politecnico di Milano, Italy, and Università della Svizzera italiana, Switzerland, with visiting appointments at the University of Kobe (Japan) and Guangzhou (China). His research focuses on machine learning in nonstationary environments, intelligent embedded systems, and cyber-physical systems. Alippi holds IEEE Fellow status and has received notable awards, including the 2016 Gabor Award and the 2018 IEEE CIS Outstanding Computational Intelligence Magazine Award. He has authored a monograph, 6 edited books, and over 200 papers. Alippi's academic roles include Vice-President for Education of the IEEE Computational Intelligence Society and Distinguished Lecturer (2014-2016). He has organized major conferences like IJCNN and IEEE SSCI and contributed to initiatives promoting education in computational intelligence. His research addresses challenges in adaptive systems, embedded intelligence, and fault diagnosis. Key projects include deploying wireless sensor networks for environmental monitoring and developing machine learning algorithms for real-world applications like landslide prediction and network security. Alippi's awards reflect his contributions to neural networks and adaptive learning, including the IBM Faculty Award (2013) and IEEE Young Engineer Award (2004).

Federico Sicilianoمشاهده پروفایل

پژوهشگر

• Recommender Systems
• Information Retrieval
• Explainable Artificial Intelligence
• +۵ مورد دیگر

Federico Siciliano is a Postdoctoral Researcher and Ricercatore (Researcher) at Sapienza University of Rome, affiliated with the RSTLess Lab under the supervision of Prof. Fabrizio Silvestri. He has been teaching Algorithmic Methods of Data Mining in the Master’s program in Data Science since 2024. His research spans recommender systems, information retrieval, explainable AI, and deep learning theory. Education: PhD in Data Science, Sapienza University of Rome (2020–2024) Master’s in Data Science, Sapienza University of Rome (2017–2019) Bachelor’s in Management Statistics, Sapienza University of Rome (2014–2017) His research interests lie at the intersection of Artificial Intelligence and Data Science , particularly focusing on sequential and graph-based recommender systems , robustness and explainability in AI models , and theoretical foundations of deep learning . His recent work explores architectural improvements in RAG systems, anomaly detection in 5G networks, and trustworthy AI through counterfactual explanations. The most recent publications (2023–2025) reveal a strong trend toward robust and interpretable AI systems , with recurring themes in recommendation , retrieval , and security . His work combines theoretical rigor with practical applications in healthcare, telecommunications, and misinformation detection. He frequently publishes in high-impact venues such as IEEE Access, ACM RecSys, and CVPR. Scientific Collaborations: University of Cambridge Meta Amazon University of Pisa (UniPi) INAF (Italian National Institute of Astrophysics) Federico actively contributes to the research community by organizing a special session on Neural Methods for IR and RecSys at IJCNN 2025. He has been involved in projects related to space weather monitoring and clinical outcome prediction in thyroid cancer. No formal students or grants are explicitly mentioned, but his collaborative projects suggest involvement in externally funded research. Laboratories and Teams: RSTLess Lab, Sapienza University of Rome

• Computational topology
• Topological data analysis
• Machine learning
• +۷ مورد دیگر

Francesco Vaccarino is an Associate Professor in the Department of Mathematical Sciences "GL Lagrange" (DISMA) at the Polytechnic University of Turin and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. He is actively involved in research, teaching, and doctoral supervision across multiple programs, including Mathematical Sciences, Applied Mathematics, and Artificial Intelligence. Research Interests: Computational topology Topological data analysis (TDA) Machine learning and geometric learning Algebraic and geometric methods in data science Complex networks and higher-order interactions His recent publications reflect a strong interdisciplinary focus, combining topology with machine learning, neuroscience, and physics. Key themes include persistent homology, simplicial network models, and topological visualization of complex systems. He has made significant contributions to understanding brain networks, scientific collaboration patterns, and phase transitions through topological methods. Scientific Awards: The Best Science Visualizations of the Year (Wired.com, 2014) Advising and Grants: He supervises multiple PhD students in Mathematical Sciences and Artificial Intelligence. He has led research projects funded by competitive tenders (e.g., XTERA-FOOD) and commercial contracts (e.g., with FPT Industrial SPA and Stellantis), focusing on AI, digital transformation, and industrial data analysis. He has served as Scientific Director for corporate training programs in AI and data science. Conferences and Leadership: He has been Program Chair for several international events including AMTCS7, TOPDRIM, Toponets15, and ECCS 2013, promoting the application of topology in data science. Labs and Affiliations: He is a core member of SmartData@PoliTO, a leading data science laboratory fostering interdisciplinary research in big data and AI.

Maria Lucia Sampoliمشاهده پروفایل

دانشیار

• Numerical Analysis
• Computer Aided Geometric Design
• Isogeometric Analysis
• +۷ مورد دیگر

Maria Lucia Sampoli is an Associate Professor in the Department of Information Engineering and Mathematics at the University of Siena, Italy. She holds a PhD in Computational Mathematics and Operational Research from the University of Milan and has been a faculty member at the University of Siena since 1999, advancing from Research Assistant to tenured Assistant Professor in 2002 and to Associate Professor in 2010. Her academic journey includes a postdoctoral fellowship at the Technical University of Darmstadt and a CNR Senior Fellowship. PhD in Computational Mathematics and Operational Research, University of Milan (1998) Laurea in Mathematics (Honors), University of Florence (1994) Her research focuses on Numerical Analysis , particularly in Computer Aided Geometric Design (CAGD) , Isogeometric Analysis (IGA) , Pythagorean Hodograph (PH) curves and surfaces , Boundary Element Methods , and constrained interpolation and approximation . Recently, she has explored mathematical aspects of deep learning , especially the theoretical foundations of Graph Neural Networks. Her work bridges pure mathematical theory with engineering and computational applications. The most recent publications highlight a consistent trend in geometric modeling and numerical methods, with increasing interdisciplinary reach into machine learning. Topics span from high-precision curve and surface design to advanced numerical techniques for PDEs and the mathematical analysis of neural networks. She has participated in and organized numerous international conferences such as SIAM CSE, IGA, and SMART, and has been involved in multiple research projects, some as coordinator. While specific scientific awards are not listed, her invited talks at major conferences reflect recognition in her field. Maria Lucia Sampoli supervises graduate research and teaches courses including Numerical Calculation and Numerical Modeling . She is actively involved in academic service, including conference organization and research project leadership. She maintains an open-door policy for student consultations and is accessible via email for appointments. She is affiliated with the SAILab (Siena Artificial Intelligence Lab), indicating institutional engagement with AI and computational intelligence research.

• Additive Manufacturing
• Laser Materials Processing
• Welding Technology
• +۳ مورد دیگر

Giuseppe Casalino is a Full Professor in the Department of Mechanics, Mathematics and Management at Polytechnic University of Bari, Italy. His research focuses on advanced manufacturing technologies including additive manufacturing, laser materials processing, and welding systems. Based at Viale Japigia 182 in Bari, he can be contacted at giuseppe.casalino@poliba.it. His research spans critical areas in modern manufacturing: Additive Manufacturing (Wire Arc, Laser Powder Bed Fusion) Laser Materials Processing (Welding, Marking, Surface Treatment) Friction Stir Welding and Dissimilar Material Joining Statistical Process Optimization and Numerical Simulation Industry 4.0 Integration (IoT, Deep Learning, Business Intelligence) Material Characterization and Microstructure Control Analysis of recent publications reveals strong emphasis on experimental-statistical-numerical triad approaches for process optimization. Key trends include WAAM parameter refinement for metals, non-destructive thermographic evaluation of welds, and sustainable manufacturing through energy consumption analysis. His work bridges mechanical engineering, materials science, and computational methods with applications in automotive, aerospace, and marine industries. No scientific awards or student advisement information was provided in available materials. Research infrastructure details and grant funding information were not specified in the source documentation.

• Algorithm Engineering
• Health IT
• Information Systems
• +۸ مورد دیگر

Umberto Nanni is a full Professor of Information Management Systems at the University of Rome "La Sapienza" since November 2005. He has held significant leadership roles including Director of the Research Center for Distance Learning and Technologies for Learning at Telematic University Unitelma Sapienza (2015-2022) and President of the Area Council in Information Engineering at the Latina location of University of Rome "La Sapienza" (2014-2020). Professor Nanni's research spans multiple domains with primary focus on Algorithm Engineering (particularly path algorithms and combinatorial problems), Health IT, Information Systems (with applications in Transport and Logistics, Cultural Heritage), and Technology Enhanced Learning. His work integrates Big Data, Graph and Text mining, Incremental Algorithms, Dynamic Data Structures, and Internet of Things technologies to address complex challenges in these areas. His recent publications demonstrate a strong trend toward biomedical applications, particularly in biospecimen management and digital twins for precision medicine, while maintaining his foundational work in algorithm design and educational technology. The research shows increasing interdisciplinary collaboration, especially between computer science and healthcare domains, with notable publications in Cancer Genomics & Proteomics and other high-impact journals. He has advised over 400 undergraduate and master's theses along with 2 PhD students He has led numerous significant research projects including "Artificial Intelligence algorithms to track and detect Covid-19 vaccine-related infodemic on social media" (2023-2025) He coordinated the "ADCATER - Advanced Digital Solutions for Professional Food and Nutrition Catering Service" project (2021-2023) He served as Principal Investigator for the eLF-eLearning Fitness project (2011-2014) with 19 partners and 42 associated partners Professor Nanni co-founded and was instrumental in developing SPRECware, software tools for Standard PREanalytical Code labeling to improve biospecimen management. His work has resulted in over 100 scientific papers with an h-index of 27, demonstrating substantial impact across computer science, healthcare informatics, and educational technology domains.

• Artificial Intelligence
• Optimization Techniques
• Reinforcement Learning
• +۲ مورد دیگر

Michele Ciavotta is an Associate Professor at the University of Milano-Bicocca's Department of Computer Science, Systems, and Communication, specializing in AI-driven optimization for complex systems. His research integrates reinforcement learning, graph neural networks, and metaheuristics applied to distributed computing and physical systems like smart mobility and production lines. Research spans cloud/edge computing optimization, industrial production systems, smart city applications, and graph-based learning methods. Recent publications demonstrate focus on decentralized AI systems, geospatial data processing, and hypergraph neural networks for chemical and urban applications. Extensive involvement in European R&D projects addresses challenges in cloud computing infrastructure, Industry 4.0 implementations, and distributed AI solutions.

• Artificial Intelligence
• Graph Learning
• Computer Vision
• +۶ مورد دیگر

Francesca Pistilli is a Researcher at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. She is affiliated with the College of Computer, Film, and Mechatronics Engineering and contributes to the VANDAL Laboratory (Visual and Multimodal Applied Learning). Her research focuses on artificial intelligence, robotics, graph learning, and computer vision. She teaches courses including Introduction to Graph Learning (PhD), Robot Learning (MSc), and Programming Techniques (BSc). She supervises PhD student Gaetano Salvatore Falco (Artificial Intelligence, 40th cycle, 2024–ongoing). Her publications highlight expertise in image segmentation, egocentric video understanding, neural architecture search, and point cloud compression. Recent projects include Scene Understanding (2025–2026) funded by commercial contracts.

• Computer Vision
• Image Analysis
• Medical Imaging
• +۴ مورد دیگر

Domenico Tegolo is an Associate Professor in the Department of Mathematics and Computer Science at the University of Palermo (Unipa), where he teaches courses in programming, image analysis, and computer science. His office is located at Via Archirafi 3490123, Palermo, with office hours held on Wednesdays from 3:00 PM to 5:00 PM. Professor Tegolo's research spans multiple domains with a strong focus on computer vision and image analysis , particularly in medical applications. His work encompasses retinal image processing , capillaroscopy analysis , and medical diagnostics systems. He has also made significant contributions to programming education methodologies and federated learning approaches for agricultural data. His recent publications show a growing interest in large language models and their applications in specialized domains. Analysis of Professor Tegolo's publication record from the past five years reveals a diverse research trajectory that bridges computer science with practical applications in healthcare and agriculture. His work demonstrates consistent innovation in image processing techniques while adapting to emerging technologies like deep learning and large language models. The research shows particular strength in medical imaging applications, with numerous publications on retinal analysis, oral microcirculation, and diagnostic support systems. Professor Tegolo has taught a variety of courses including Theories and Techniques for Image Analysis, Programming with Laboratory, Advanced Programming, and Structured Programming for Mathematics programs. He has also previously taught Operating Systems and Programming Languages courses for Informatics programs, demonstrating breadth across computer science disciplines.