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Fosca Giannotti is a Full Professor at Scuola Normale Superiore in Pisa, Italy, and leads the Pisa KDD Lab - Knowledge Discovery and Data Mining Laboratory, a joint research initiative of the University of Pisa and ISTI-CNR. Founded in 1994, the Pisa KDD Lab is one of the earliest research labs focused on data mining. Giannotti is a pioneering scientist in mobility data mining, social network analysis, and privacy-preserving data mining. Her educational background includes a Master Degree in Computer Science from the University of Pisa (1982) with 110/100 cum laude. She has held numerous visiting positions including at MCC in Austin, CWI Amsterdam, UCLA, and the Barabasi Lab at Northeastern University. Giannotti's research focuses on social mining from big data, encompassing smart cities, human dynamics, social and economic networks, ethics and trust, and diffusion of innovations. She has authored more than 300 papers and coordinated tens of European projects and industrial collaborations. Her current work increasingly centers on Explainable AI (XAI), as evidenced by her prestigious ERC Advanced Grant for the XAI project focused on "Science and technology for the explanation of AI decision making." Her recent publications reveal a strong emphasis on trustworthy AI, with research spanning privacy-preserving techniques, fairness in machine learning, human-AI collaboration frameworks, and medical applications of explainable AI. The breadth of her work demonstrates how data mining principles are being applied across diverse domains from social sciences to healthcare. ERC Advanced Grant for XAI project Premio Internazionale Tecnovisionarie 2021 Intelligenza Artificiale Giannotti has coordinated numerous significant projects including SoBigData (the European research infrastructure on Big Data Analytics and Social Mining), XAI, TAILOR (Foundations of Trustworthy AI), HumanE-AI-Net, and AI4EU. As former coordinator of SoBigData, she led an ecosystem of ten cutting-edge European research centers providing an open platform for interdisciplinary data science. She leads the Pisa KDD Lab, which serves as a hub for research on knowledge discovery and data mining. The lab has been instrumental in developing techniques for mobility data analysis, social network mining, and privacy-preserving data analytics, with applications ranging from smart cities to pandemic response.

• Mobility Data Mining
• Social Network Analysis
• Privacy-Preserving Data Mining
• +6 more

Massimo Candiani is a Full Professor at Vita-Salute San Raffaele University in Milan, Italy, specializing in Obstetrics and Gynecology. He serves as Director of the Complex Structure of Obstetrics and Gynecology at San Raffaele Hospital and has held leadership roles at multiple institutions including San Paolo Hospital and Milanocuore. His career spans over three decades with extensive clinical, academic, and research contributions. Specialized in Gynecology and Obstetrics (1993) European University Certificate in Gynecologic Endoscopy (1993) Medical Degree from University of Milan (1989) His research focuses on maternal-fetal medicine, gynecologic endoscopy, and women's health disorders. Recent publications address topics like fetal brain imaging segmentation, MRKH syndrome patient experiences, and urinary incontinence epidemiology. His work bridges clinical practice with translational research. Over the past year, Candiani has contributed to studies on predictive biomarkers for preeclampsia, colposcopy quality assurance, and postoperative complication modeling. These publications demonstrate his expertise in integrating clinical data with innovative diagnostic approaches. He actively participates in national and international conferences as speaker/moderator, with over 200 presentations since 1989. Notably, he was coordinator of the endoscopy unit at Milan's University Center for Experimental Fetal Surgery.

• Obstetrics and Gynecology
• Fetal Surgery
• Gynecologic Endoscopy
• +4 more

Maria Vittoria Cicinelli, MD, FEBO, is a fixed-term researcher at Vita-Salute San Raffaele University in Milan, Italy. Specializing in Ophthalmology, her work focuses on medical retina, uveitis, ocular oncology, and ocular disease epidemiology. Graduated in Medicine and Surgery (2013) Completed Ophthalmology specialization (2019) Researcher in Ophthalmology (2021–present) Her research combines clinical investigation with advanced imaging techniques to study inflammatory and neoplastic eye diseases. She has contributed to >130 peer-reviewed publications and leads epidemiological analyses within the Global Vision Database. Recent publications highlight trends in: Multimodal imaging for uveitis and retinal lymphoma Pharmacotherapeutic monitoring in macular edema Epidemiological modeling for ocular disease prevalence Scientific Recognition: National Scientific Qualification for Associate Professor (2020) International Council of Ophthalmology Allergan Fellowship (2020) Premio SIUMIO (2019) As a mentor and educator, she serves as Tutor at the Ophthalmology Specialization School and contributes to editorial boards including Frontiers in Ophthalmology and European Journal of Ophthalmology.

• Medical Retina
• Uveitis
• Ocular Oncology
• +3 more

Lorenzo Farina is a Full Professor at Sapienza University of Rome's Faculty of Information Engineering, Computer Science and Statistics, specializing in Electronic and Computer Bioengineering (ING-INF/06). With over 25 years of academic leadership, he co-founded Italy's first Bioinformatics degree program and established key oncology precision medicine initiatives, maintaining active collaborations with Harvard Medical School's network medicine division. His educational background includes a cum laude Electronic Engineering degree and PhD in Systems Engineering, both from Sapienza University. These foundational studies evolved into pioneering work in positive linear systems theory, evidenced by his highly-cited Wiley textbook Positive Linear Systems: Theory and Applications (2000). Farina's research centers on network medicine – applying complex network science to molecular medicine since his 2004 breakthrough. His work spans cancer mechanisms (breast, glioblastoma, lung), drug repositioning (including COVID-19 applications), and liquid biopsy biomarker development. Current projects focus on miRNA-based network biomarkers for cancer diagnostics and immunotherapy response prediction, integrating multi-omics data through advanced computational frameworks. Analysis of his 15 most recent publications (2024-2025) reveals dominant themes: sexual dimorphism in cancer networks (MIRROR platform), immunotherapy response signatures, and critical examinations of AI's role in precision medicine. His work consistently bridges computational innovation with clinical applications, particularly in oncology diagnostics and therapeutic optimization. His scientific recognition includes: 2001 Guillemin-Cauer Award for best IEEE Transactions on Circuits and Systems article 2014 SysBio Award for annual best publication Farina actively mentors through interdisciplinary programs he established, including the Network Oncology doctoral program. His laboratory collaborations span Sapienza's Oncogenomics and Immunology Laboratories, Harvard's Channing Division of Network Medicine, and clinical departments in oncology and radiology, driving translational research from computational models to patient applications. He leads multiple research teams focused on network-based diagnostics, including the MIRROR platform for cancer disparity analysis and liquid biopsy development teams investigating circulating miRNA networks for early cancer detection across multiple malignancies.

• Network Medicine
• Bioinformatics
• Systems Biology
• +6 more

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

• Computer Vision
• Image Processing
• Artificial Intelligence
• +5 more

Prof. Abele Donati is a full professor at the Polytechnic University of Marche (UNIVPM) within the School of Medicine and Surgery , specifically in the Department of Biomedical Sciences and Public Health . His clinical practice is based at Ospedali Riuniti Umberto I - Clinica di Anestesia e Rianimazione Generale Respiratoria e del Trauma Maggiore in Ancona, Italy. Research Focus: Critical care medicine, sepsis, microcirculation, anesthesia, and respiratory failure Key Contributions: Expertise in hemodynamic monitoring, antibiotic resistance, ECMO, and perioperative medicine Publications Trends: Recent work emphasizes antimicrobial therapy for multidrug-resistant pathogens, microcirculatory dysfunction in sepsis, critical care pharmacogenetics, and advanced monitoring techniques during surgeries. His studies frequently involve multicenter collaborations and clinical trials. Contact: Email available at a.donati@univpm.it

• Critical Care Medicine
• Anesthesiology
• Sepsis Management
• +3 more

Massimo Robiony is a Full Professor at the University of Udine, affiliated with the DAME Department of Medical Area. As Director of the Maxillofacial Surgery Clinic, School of Specialization in Maxillofacial Surgery, and Level II Master's Degree in Health and Humanization, he leads multidisciplinary initiatives in surgical innovation. His research spans 3D printing, virtual surgical planning, robotic surgery, and AI applications in craniofacial medicine. Academic Leadership: Director roles in clinical, specialization, and master's programs. Research Focus: 3D printing, AI diagnostics, and robotic surgery for maxillofacial pathologies. Email: massimo.robiony@uniud.it Recent publications emphasize AI-driven image processing, bioprinting, and augmented reality in surgical oncology. His work highlights the integration of virtual planning and 3D printing for trauma management, alongside advancements in robotic and transoral techniques for orbital and craniofacial disorders.

• Maxillofacial Surgery
• Head and Neck Surgery
• Virtual Surgical Planning
• +3 more

Patrizia Proia is an Associate Professor at the University of Palermo, Department of Psychological, Pedagogical, Physical Exercise and Training Sciences. Her research focuses on exercise physiology, sports genetics, and neurobiological mechanisms linked to physical activity. She teaches courses in biochemistry, physiology of nutrition, and sports training adaptation. Current research on microbiome-exercise interactions Developing protocols for MS and Parkinson's rehabilitation Investigating genetic polymorphisms in elite athletes Exploring nutrigenomics and functional foods Recent publications analyze: Muscle regeneration through probiotic supplementation Impact of nanotechnological devices on pain management Epigenetic regulation of executive functions Bone health in gymnasts and cancer patients Role of gut microbiome in exercise response

• Exercise Physiology
• Sports Nutrition
• Genetic Polymorphisms in Athletes
• +6 more

Guido Perrone is a Tenured Associate Professor at the Department of Electronics and Telecommunications (DET) , Politecnico di Torino, with a focus on Photonics, Fiber Optic Sensors, and High-Power Lasers. His research bridges industrial and biomedical applications, particularly additive manufacturing and laser therapy, aligning with SDG goals like Good Health, Industry Innovation, and Clean Water. Research Interests : Photonics, Fiber Optic Sensors, High-Power Lasers, Biomedical Applications, Additive Manufacturing, Electromagnetic Fields, Signal Processing, and Machine Learning. Projects : Led competitive grants including DESIGN (2024–2028), Yellow-FLiCkEr (2022–2025), and STELLAR (2021–2022) as Scientific Director/Manager. Teaching : Course instructor for Photonics , Industrial Photonics , and Passive Optical Components at undergraduate and graduate levels. Grants & Patents : Supervised >10 research projects and holds 2 patents (national/international) on optical transducers. Students : Mentored 4 PhD candidates in laser and sensor technologies.

• Photonics
• Fiber Optic Sensors
• High-Power Lasers
• +11 more

Ambrosi Alessandro is an Associate Professor in Medical Statistics at Vita-Salute San Raffaele University. Holding a PhD in Statistics from the University of Padua (1999), he has contributed extensively to biostatistical methodologies in medical research, particularly in gene therapy, hematology, and computational biology. Degree in Statistics, University of Padua (1996) PhD in Statistics, University of Padua (1999) His research focuses on non-parametric statistical methods, machine learning applications in medicine, and statistical genetics. He has developed analytical frameworks for retroviral integration site mapping and biomarker discovery in preeclampsia and cancer prognosis. Recent work examines placental gene expression in pregnancy complications, orthodontic biomechanics, and cardiovascular risk prediction models. His publications span top-tier journals such as EMBO, Blood, and Nature Biotechnology. Fellow, Cochrane (2024-present) Fellow, Società Italiana di Statistica (2024-present) Fellow, SISMEC (2013-present) Ambrosi has served as a contract professor at the University of Padua and participates in doctoral colleges across Italy. His collaborations include international institutions like the University of Aarhus (Denmark) and the University of Oslo (Norway).

• Non-parametric statistical methods
• Machine learning
• Statistical methods in genetics
• +3 more

Giorgio Brembilla is a Researcher in Medicine at the Vita-Salute San Raffaele University, specializing in medical imaging and oncology. His research focuses on advanced imaging techniques such as PET/MRI and PSMA-based radiotracers for prostate cancer diagnosis and management. He leads studies on lymph node detection, radiomics signatures, and predictive modeling for surgical outcomes. His work integrates clinical, imaging, and genomic data to improve diagnostic accuracy and treatment strategies. Affiliations: Vita-Salute San Raffaele University, Department of Medicine (implied). Teaching: Courses include 'Imaging in Urology' and 'Imaging of the Musculoskeletal System.' Key research interests include the development of hybrid imaging workflows, AI-driven segmentation of tumor lesions, and risk stratification models for kidney and prostate cancers. His recent studies address external validation challenges in radiomics and the impact of imaging quality on prostate cancer detection. Publications highlight contributions to PSMA PET-based radiomics, preoperative risk calculators, and the diagnostic accuracy of hybrid imaging modalities. Collaborations focus on translational research bridging imaging and clinical oncology.

• Nuclear Medicine
• Prostate Cancer Imaging
• Radiomics
• +3 more

Giuseppe Agapito is a Professor at the Department of Law, Economics and Sociology (DiGES) at the University of Camerino, where he teaches courses such as Elements of Computer Science and Data Analysis. He specializes in computational biology, bioinformatics, and health informatics, focusing on genomic data analysis, machine learning applications in healthcare, and parallel computing methodologies. His research integrates multi-omics approaches, pathway enrichment analysis, and predictive modeling for drug response and disease mechanisms. Notable contributions include tools like BioPAX-Parser and cPEA, which enhance genomic data interpretation. He actively collaborates in international studies, such as the 4CE consortium analyzing SARS-CoV-2 impacts. His work addresses challenges in privacy-aware bioinformatics, high-performance computing for genomics, and AI-driven medical diagnostics. Education details are not explicitly provided in the texts, but his academic profile reflects extensive expertise in interdisciplinary fields bridging computer science and biomedical research. He maintains an active research agenda with over 50 publications since 2018, emphasizing scalable data analysis, drug biomarker discovery, and computational methods for clinical outcomes prediction. His teaching responsibilities include IT management and data analysis modules within social science curricula, reflecting a commitment to digital literacy across disciplines. Research interests span bioinformatics tool development, genomic data preprocessing, and AI applications in healthcare, with a focus on translational research. Recent articles highlight advancements in fMRI classification using graph neural networks, privacy-preserving genomic pipelines, and edge-based deep learning for medical signal analysis. Awards and grants are not explicitly listed, but his sustained contribution to international research consortia underscores his field influence. He advises students and researchers on computational methodologies and hosts weekly office hours for academic consultations.

• Bioinformatics
• Computational Biology
• Pharmacogenomics
• +5 more

Silvia Giordano is a Professor at the Department of Oncology, University of Turin. Her research focuses on oncology, molecular medicine, and cancer biology, with particular emphasis on gastric cancer, targeted therapies, and drug development. She contributes to courses like Basis of Human Morphology and Molecular bases of targeted therapies. Giordano leads studies on tumor cell signaling, resistance mechanisms, and translational models, including patient-derived xenograft (PDX) collections. She collaborates with institutions like the IRCCS Candiolo Cancer Institute and serves on academic bodies such as the Department Council. Her work bridges basic research and clinical applications, addressing challenges in cancer treatment and drug resistance. She has published extensively on topics like MET receptor biology, hepatocellular carcinoma models, and synergistic chemotherapy combinations.

• Oncology
• Molecular Medicine
• Cancer Biology
• +5 more

Umberto Ricardi is a Professor in the Department of Oncology at the University of Turin. His primary affiliation is with the Department of Oncology at the AO Città della Salute e della Scienza hospital. He specializes in Radiation Oncology, Clinical Oncology, Hematology, and Clinical Radiobiology. His research focuses on advancing radiation therapy protocols for cancers such as lymphoma, glioblastoma, and oligometastatic diseases. He contributes to international guidelines through groups like the International Lymphoma Radiation Oncology Group (ILROG) and EORTC-ESTRO. Dr. Ricardi teaches courses on Artificial Intelligence in Biomedicine and Healthcare, and AI-driven Radiation Oncology. He holds leadership roles in academic bodies such as the Department Council of the Department of Oncology and the Scientific Committee of the MD-PhD Program. His work integrates cutting-edge radiotherapy techniques with targeted therapies and immunotherapies, emphasizing multidisciplinary approaches to cancer treatment.

• Radiation Oncology
• Clinical Oncology
• Hematology
• +3 more

Lili Zhang, M.D. is a physician scientist at Montefiore Medical Center specializing in the intersection of cardiology and oncology. Her work focuses on understanding and managing cardiac complications associated with cancer therapies, with particular expertise in cardiotoxicity assessment and prevention. Dr. Zhang's research interests center on cardio-oncology, with specific focus on immune checkpoint inhibitor myocarditis, anthracycline cardiotoxicity, and cardiac complications of emerging cancer therapies like CAR-T cell therapy. Her work integrates clinical cardiology with oncology to develop better monitoring strategies and protective interventions for cancer patients at risk of cardiovascular complications. She employs multimodal imaging techniques to detect early cardiac changes in patients undergoing cancer treatment. Analysis of Dr. Zhang's recent publications reveals a strong focus on the rapidly evolving field of cardio-oncology, with particular attention to newer cancer therapies and their cardiac implications. Her work spans from basic mechanisms of cardiotoxicity to clinical management strategies, with an increasing emphasis on health disparities and social determinants of health in cardio-oncology outcomes. Dr. Zhang maintains an active research program with numerous publications in high-impact cardiology and oncology journals. Her collaborative network includes researchers across cardiology, oncology, and imaging specialties, reflecting the interdisciplinary nature of cardio-oncology research.

• Cardio-oncology
• Cardiotoxicity
• Heart Failure
• +4 more