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Sebastiano BattiatoView profile

Professor

• Computer Vision
• Imaging Technology
• Multimedia Forensics
• +3 more

Sebastiano Battiato is a Full Professor of Computer Science at the University of Catania's Department of Mathematics and Computer Science. He serves as Scientific Coordinator of the PhD Program in Computer Science and Deputy Rector for Strategic Planning and Information Systems at the University of Catania. As Director and Co-Founder of the International Computer Vision Summer School (ICVSS), he has significantly contributed to computer vision education globally. Education: Bachelor's degree in Computer Science (summa cum laude), University of Catania, 1995 Ph.D. in Computer Science and Applied Mathematics, University of Naples, 1999 Professor Battiato's research primarily focuses on Computer Vision, Imaging Technology, and Multimedia Forensics . His work spans from developing ISP algorithms for embedded devices to creating advanced techniques for image enhancement, coding, and forensic analysis. He has pioneered research in social media forensics, developing methods to determine if images have been processed through specific social platforms. His research has practical applications in assistive technologies, retail, digital marketing, and medical fields. His scholarly output shows a consistent focus on digital forensics and image processing, with an increasing emphasis on social media forensics in recent years. The research trajectory demonstrates progression from foundational image processing techniques to sophisticated forensic applications capable of addressing modern challenges like deepfakes and social media manipulation. Scientific Awards: 2017 PAMI Mark Everingham Prize for the series of annual ICVSS schools 2011 Best Associate Editor Award of IEEE Transactions on Circuits and Systems for Video Technology Professor Battiato has coordinated IPLab's participation in numerous large-scale research projects funded by national and international bodies as well as private companies. He has served as principal investigator on many international and national research projects, demonstrating strong leadership in securing research funding. His editorial work includes serving as associate editor for the SPIE Journal of Electronic Imaging and IET Image Processing Journal, and membership on several other editorial boards. As Director of IPLab research lab (http://iplab.dmi.unict.it), Professor Battiato leads a team focused on computer vision and digital forensics. The lab collaborates extensively with law enforcement agencies through iCTLAB, a university spinoff he founded that provides digital forensic services. IPLab is recognized for its contributions to image/video forensics, with techniques implemented in commercial forensic software like AMPED Authenticate.

• Accessibility
• Conversational agents
• Developers tools
• +11 more

Luigi De Russis is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) within Politecnico di Torino . He serves as Deputy Director of DAUIN and is a member of the PIC4SeR (PoliTO Interdepartmental Centre for Service Robotics). His academic roles focus on Human-Computer Interaction , Digital Wellbeing , and Artificial Intelligence applications. Research interests: Accessibility, Conversational agents, Developers tools, Digital wellbeing, Intelligent user interfaces, Internet of Things Teaching: Courses in Human-AI Interaction, Web Applications, and Computer Vision at undergraduate and graduate levels Leadership: Vice-President of ACM SIGCHI (2024-), Executive Committee member (2021-2024) His research explores: Digital wellbeing education for teens through gamified systems AI-assisted UI design tools Smart home interaction via multimodal commands End-user development for self-control technologies Integration of accessibility guidelines in AI systems Recent article trends show a focus on generative AI for interface design, attention-capturing heuristics, and educational systems for step-by-step learning. He has received the Most Influential Paper Award (2024) and Best Late Breaking Results Award (2025) from ACM SIGCHI. Scientific awards: Award of Scientific Excellence, University of Salamanca (2011) Most Influential Paper Award, International Conference on Intelligent Environments (2024) Best Late Breaking Results Paper Award, ACM SIGCHI Symposium (2025) As advisor, he supervises PhD students in Artificial Intelligence and Computer Engineering , focusing on topics like user-centered AI, generative models, and digital self-control interfaces. He leads the ELITE research group and contributes to commercial projects including TEIA (AI tourism) and MAPP (interactive museums).

Vincent LepetitView profile

Research Professor

• Computer Vision
• 3D Computer Vision
• Machine Learning
• +3 more

Vincent Lepetit currently serves as a Director of Research at École des Ponts ParisTech since 2019. His prior academic appointments include: Full Professor at the Institute for Computer Graphics and Vision, Graz University of Technology, Austria Senior Researcher at the Computer Vision Laboratory (CVLab) of École Polytechnique Fédérale de Lausanne (EPFL), Switzerland His research resides at the critical intersection of Machine Learning and 3D Computer Vision, with concentrated efforts on advancing 3D scene understanding from images. This work integrates deep learning methodologies with geometric computer vision to solve complex perception challenges, driving innovations in computational intelligence and visual recognition systems. His expertise spans both theoretical foundations and practical implementations across computer vision subdomains. Prof. Lepetit maintains significant leadership roles within the computer vision community, consistently serving as area chair for premier conferences including CVPR, ICCV, and ECCV. He further contributes through editorial positions as associate editor for top-tier journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), International Journal of Computer Vision (IJCV), and Computer Vision and Image Understanding (CVIU), shaping research standards across the field. No scientific awards or honors were explicitly documented in the provided source material. While the text confirms his past affiliation with EPFL's Computer Vision Laboratory (CVLab), current laboratory or team leadership details remain unspecified. Information regarding doctoral advisees, research grants, or funding mechanisms was not included in the available documentation, though his editorial and conference leadership roles indicate substantial research influence.

• Digital Ethics
• AI Ethics
• Media Ethics
• +4 more

Benedetta Giovanola is a Full Professor at the University of Macerata (UniMC) in the Department of Political Sciences, Communication and International Relations. She holds the Jean Monnet Chair 'Ethics for Inclusive Digital Europe' (funded by the EU) and is a Visiting Professor at Tufts University, USA. She also serves as Adjunct Professor of 'Economic Philosophy and Business Ethics' at the Catholic University of Milan. Her roles include Director of the postgraduate course on 'Digital Ethics, Law, Technologies' and member of the executive board of the 'Cybersecurity, Cyber Risk and Data Protection' program, both jointly run with the Polytechnic University of Marche. Current roles: Jean Monnet Chair, Full Professor, Visiting Professor at Tufts, Adjunct Professor at Catholic University Milan. Leadership: Founder of GAIA (AI Ethics and Governance spin-off), Principal Investigator (PI) in EU-funded projects like EthicAI4CARE and REINITIALISE. Professional memberships: Member of the executive board of SIFM (Italian Association for Moral Philosophy), past Deputy Rector at UniMC (2016-2022). Research focuses on digital ethics, AI ethics, media ethics, public ethics, social and global justice. She leads projects such as ENDE (European Network on Digitalization and E-Governance) and publishes extensively on AI ethics in healthcare, cultural heritage, and business contexts. Her work emphasizes ethical frameworks for trustworthy AI and fairness in algorithmic systems. Awards and recognition include the Jean Monnet Chair, and her research has been featured in journals like AI & Society, Computer Vision and Image Understanding, and IEEE Transactions on Technology and Society. She also contributes to policy initiatives and ethical guidelines for emerging technologies.

• Computer Vision
• Deep Learning
• Robotics
• +9 more

Giuseppe Bruno Averta is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN), Polytechnic University of Turin, and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. He is affiliated with the College of Computer, Film and Mechatronics Engineering and contributes to national and international research in artificial intelligence and robotics. Averta has held a Visiting Researcher position at the Massachusetts Institute of Technology (MIT) from January to June 2019. His research interests include Computer Vision, Deep Learning, Robotics, Neural Architecture Search, Egocentric Vision, Embodied Intelligence (Edge/Tiny ML), and Human-Robot Collaboration . His work is aligned with ERC sectors in Artificial Intelligence, Machine Learning, and Robotics, and contributes to UN SDGs such as Good Health and Well-being, Industry Innovation and Infrastructure, and Responsible Consumption and Production. The recent publication trends highlight his focus on vision-language models (e.g., CLIP), egocentric action recognition, efficient neural architectures (e.g., BiSeNet, MaskFormer), and robust deep learning. His research bridges theoretical advances with practical robotics applications, including grasping and manipulation. Scientific Awards and Recognitions: Georges Giralt PhD Award (euRobotics AISBL, 2021) Wiley Best Reviewer (Wiley, Italy, 2021) Best Paper Award, ICUMT 2015 (2017) Fellow, ELLIS Network of Excellence (2022–) Fellow, DAAD AInet (2022–) DAAD AInet Fellowship Advising and Grants : Averta supervises multiple PhD students in the Artificial Intelligence and Computer and Systems Engineering doctoral programs at Politecnico di Torino. He is involved in teaching at both the master’s and doctoral levels, including courses on Robot Learning and Machine Learning and Deep Learning. He is also a co-inventor on a national and international patent for a method and algorithm for the automatic design of neural networks through machine learning, indicating active research funding and innovation. Labs and Research Groups : He is a member of the SmartData@PoliTO center and contributes to research in the VANDAL PoliTO lab (as indicated by his student Davide Buoso). His work is deeply integrated with teams working on egocentric vision, embodied AI, and neural architecture search.

Andrea SantilliView profile

Researcher

• Natural Language Processing
• Representation Learning
• Machine Intelligence
• +10 more

Andrea Santilli is a Research Scientist at Nous Research and holds a PhD in Computer Science from GLADIA at Sapienza University of Rome. His research focuses on large language models (LLMs), robustness, reliability, and multimodal learning. He previously worked at Apple MLR, Hugging Face’s BigScience, and Pi School. He earned his MSc and BSc in Computer Science from Tor Vergata University and Sapienza. Education: PhD in Computer Science, Sapienza University of Rome (2024) MSc in Computer Science, University of Roma Tor Vergata (2020) BSc in Computer Science, University of Roma Tor Vergata (2018) Research Interests: Santilli’s work spans LLM robustness , mechanistic interpretability , multimodal neural databases , and instruction-tuning . He introduced Parallel Jacobi Decoding and contributed to projects like BLOOM, Camoscio, and Fauno. His research bridges syntax-aware NLP, privacy-preserving LLMs, and cross-modal alignment. Publications: His work includes advancements in 3D-text latent space alignment (CVPR 2025), evolutionary merging (ICML 2025), and efficient decoding (ACL 2023). Over 15+ peer-reviewed papers span venues like ACL, CVPR, and ICLR. Awards: Received the Emanuele Pianta Award for his MSc thesis on continual language learning with syntax-based episodic memory. Grants & Projects: Winner of ‘Machine Learning Algorithms for Translation’ grant (2022), developing Parallel Decoding Co-PI for ‘Multimodal AI for 3D Analysis’ (2021) with Ecole Polytechnique Labs & Teams: Active in GLADIA (Sapienza), Apple MLR, and Hugging Face’s BigScience initiative. Core contributor to open-source projects like PromptSource and BLOOM.

• Computer Vision
• Pattern Recognition
• Medical Image Analysis
• +4 more

Laura Brandolini is a Researcher at the Department of Industrial and Information Engineering, University of Pavia, where she collaborates with the Computer Vision and Multimedia Laboratory. She simultaneously serves in the university's ICT area, administering over 200 central servers including backup systems, virtualized services, and hardware infrastructure. Her educational background includes: Master in Computer Engineering (cum laude), University of Pavia, 1998 PhD in Industrial and Information Engineering, University of Pavia, 2013 Dr. Brandolini's research integrates computational topology with practical imaging applications, specializing in Reeb graph computation for 3D mesh segmentation and medical shape analysis. Her work bridges theoretical computer science with clinical neuroscience, particularly in developing topological descriptors for brain structures like the striatum. This interdisciplinary approach enables novel solutions for surface mesh processing in both industrial and medical contexts. Her 2012 publications reveal a consistent focus on computational geometry applied to medical imaging, demonstrating how Reeb graph theory can simplify complex 3D shape analysis. These works established foundational techniques for efficient mesh segmentation with applications in neuroimaging and computer-aided diagnosis systems. She has received the following recognition: Best Student Paper Award at ICPRAM 2012 Dr. Brandolini maintains active industry engagement through her PMP certification (held since 2004) and PMI-NIC involvement, while her dual roles demonstrate unique synergy between academic research and enterprise IT operations. Her current work combines server infrastructure management with advancing computer vision methodologies.

• Information Retrieval
• Crowdsourcing
• Human-in-the-loop AI
• +5 more

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 .

• GPU Acceleration
• Embedded Systems
• Autonomous Vehicles
• +6 more

Nicola Capodieci is an Associate Professor at the Department of Physical, Computer and Mathematical Sciences at the University of Modena and Reggio Emilia, specializing in Information Processing Systems (IINF-05/A). He actively teaches multiple courses including Object-Oriented Programming, Web Technologies, and General Computer Science across Computer Science and Mathematics degree programs. His research interests focus on GPU acceleration for embedded systems, autonomous vehicles, and real-time computing. Dr. Capodieci's work addresses critical challenges in heterogeneous computing platforms, particularly for automotive applications and smart city infrastructure. His research bridges theoretical computer science with practical applications in autonomous driving and urban mobility systems. Analysis of his recent publications reveals a strong focus on optimizing GPU performance for latency-sensitive applications, particularly in autonomous vehicles. His work spans path planning algorithms, memory interference management, and real-time scheduling on heterogeneous platforms. A significant portion of his research addresses practical implementation challenges in embedded systems where computational resources are constrained but timing predictability is critical. Dr. Capodieci's teaching portfolio demonstrates expertise in both foundational programming concepts and advanced topics in web technologies. His courses emphasize practical implementation skills while covering theoretical foundations of object-oriented programming, web development frameworks, and computational thinking.

• Industrial robotics
• Teleoperation
• Surgical robotics
• +6 more

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.

Simon MasnouView profile

Professor

• Applied Mathematics
• Image Processing
• Shape Optimization
• +5 more

Simon Masnou is a Full Professor at Université Claude Bernard Lyon 1, affiliated with the Institut Camille Jordan (CNRS UMR 5208). He holds leadership roles as Head of the 'Applied Mathematics, Statistics' Master's degree and Head of the 'M2 Maths in Action' program. Previously, he served as Director of the Camille Jordan Institute (2018-2022). His research focuses on applied mathematics, image processing, shape optimization, and geometric measure theory, with contributions to variational models, geometric flows, and applications in computer vision and materials science. Education: PhD in Mathematics (1998, Paris Dauphine) and HDR (2008, Paris 6). Research projects include ANR STOIQUES (2024-2028), PEPR PDE-AI (2023-2028), and collaborations with industry on topics like defect prediction in aluminum production and high-dimensional data analysis. Teaching includes courses on linear algebra, optimization, and machine learning at undergraduate and graduate levels. Key contributions span phase field models, varifold-based surface approximation, and image inpainting. He supervises PhD students in geometric variational problems and computational methods. His work bridges theoretical mathematics with industrial challenges, addressing issues in materials science, medical imaging, and cultural heritage preservation.

• Artificial intelligence
• Data compression
• Deep learning
• +15 more

Enrico Magli is a Full Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin, Italy. He serves as Director of the Image Processing and Learning group and Coordinator of the 'ICT for Smart Societies' M.Sc. degree program. Additionally, he is a committee member of the PhD program in Electrical, Electronic and Communications Engineering and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. Professor Magli's research focuses on applying machine learning and deep learning methods to satellite imaging, with applications to onboard processing and image analysis on the ground. His work spans deep learning for image and video analysis, image and video compression, compressive sensing, satellite imaging, and graph signal processing. He has published over 90 journal papers with 5900+ citations and an h-index of 40 on Google Scholar. His recent publications demonstrate a strong focus on developing deep learning architectures for satellite image processing, particularly for onboard applications. His research addresses challenges in hyperspectral image compression, super-resolution, change detection, and efficient neural network architectures suitable for resource-constrained satellite environments. There's also significant work on secure authentication systems using deep learning techniques and neural network optimization for edge devices. Elevated to IEEE Fellow (2017) 'for contributions to compression and communication of remotely sensed imagery' IEEE Geoscience and Remote Sensing Society 2011 Transactions Prize Paper Award IEEE Multimedia 2019 Best Paper Award Best Paper Awards at IEEE ICIP (2015, 2019) ERC Starting grant (consolidator type) and ERC Proof-of-Concept Grant recipient Multiple Best Paper Awards Francesco Carassa (2011, 2013, 2014) Professor Magli actively supervises numerous PhD students working on cutting-edge topics in deep learning for satellite imaging, image processing, and secure authentication systems. His research is supported by significant grants including ERC projects and multiple commercial contracts with space agencies and technology companies. He leads the Image Processing and Learning (IPL) Group at Politecnico di Torino, which focuses on developing innovative solutions for satellite image analysis and compression.

Gabriella CasalinoView profile

Assistant Professor

• Computational Intelligence
• eHealth
• Data Stream Mining
• +5 more

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.

• Machine Learning
• Data Mining
• Bioinformatics
• +11 more

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.

Federico BecattiniView profile

Assistant Professor

• Computer Vision
• Deep Learning
• Artificial Intelligence
• +7 more

Federico Becattini is a Tenure-Track Assistant Professor at the Department of Information Engineering and Mathematics (DIISM), University of Siena, Italy. He is an active member of the Siena Artificial Intelligence Lab (SAILab), where he contributes to cutting-edge research in computer vision, deep learning, and artificial intelligence. His work spans multiple interdisciplinary domains, including autonomous driving, human behavior understanding, cultural heritage, neuromorphic vision, and fashion recommendation. His research interests center on memory-based neural networks , which he has applied in numerous publications at top-tier venues such as CVPR, ECCV, IEEE TPAMI, and ACM TOMM. He has also delivered tutorials on this topic at international conferences including ICIAP 2022 and ACM MM 2022, and taught a Ph.D. course at the University of Florence. His recent work is aligned with the Collectionless AI paradigm, which emphasizes continual learning and interaction with dynamic environments. The recent publications highlight a strong trend in human-centric AI , focusing on understanding people through multimodal analysis of face, body, and clothing, as well as generating 3D virtual avatars. There is also a clear emphasis on memory-augmented architectures for temporal reasoning, explainability, and adaptive learning. His editorial role as Associate Editor of the International Journal of Multimedia Information Retrieval further underscores his standing in the research community. Associate Editor, International Journal of Multimedia Information Retrieval (IJMIR) Organizer, Workshop on Facial and Body Expressions (ICPR2020) Co-organizer, T-CAP Workshop (ICIAP2021, ICPR2022) Co-organizer, MCFR Workshop (ACM MM 2022) Co-organizer, WCPA Workshop and Challenge (ECCV 2022) Federico Becattini actively advises students and researchers within SAILab, particularly in the context of Ph.D. theses and research projects related to Collectionless AI and memory-based models. While specific grants are not mentioned, his extensive publication record and leadership in workshops and editorial roles suggest involvement in funded research initiatives. He collaborates with both academic and international research communities, serving as a reviewer for top-tier conferences and journals. He is a core member of the SAILab research group, which is pioneering the Collectionless AI initiative—a framework for continual learning over time, interacting with humans and agents without relying on pre-built static datasets. This lab serves as a hub for innovation in adaptive and sustainable AI systems.