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Lorenzo SchiavonView profile

Researcher

• Statistics
• Bayesian Statistics
• Data Modeling
• +5 more

Lorenzo Schiavon is a Fixed-term Researcher (RTDa) in the Department of Economics at Ca' Foscari University of Venice since 2023, with previous postdoctoral experience at the University of Padua. Education: PhD in Statistical Sciences, University of Padua (2018-2022) MBA in Science & Management, Collège des Ingénieurs, Turin (2019-2022) Master's in Statistical Sciences, University of Padua (2016-2018) Bachelor's in Statistics, Economics and Finance, University of Padua (2013-2016) Dr. Schiavon's research focuses on Bayesian infinite factorization methods , complex data analysis , and latent factor models , with innovative applications in sports analytics, particularly football tracking data. His methodological work bridges theoretical statistics with practical business and social applications. He has received multiple academic recognitions including a Best Poster Award (2024), Third Best Poster Award (2022), and the Research Prize 2021 from the University of Padova's Statistical Sciences Department. Teaching responsibilities include Statistics 2 exercises for undergraduate Economics students, Advanced Econometrics for PhD candidates, and Statistical Learning for Data Science for Master's students. He actively supervises undergraduate and graduate theses on statistics and data modeling topics across social, economic, and sports contexts.

Mattia StivalView profile

Researcher

• Health Inequalities
• Spatio-Temporal Modeling
• Sports Performance Analysis
• +7 more

Mattia Stival is a Researcher at the Department of Economics , Ca' Foscari University of Venice , with a focus on social statistics (SSD: STAT-03/B). His work bridges Bayesian and computational statistics with applications in public health and sports science. Current statistician for the Planet4Health project Previously postdoctoral researcher for the Age-it project on multi-morbidity modeling PhD in Statistical Sciences (University of Padua, 2022) with thesis on Sports Performance Analysis with State Space Models Research Interests combine methodological innovation with real-world impact: Applied : Health inequalities, aging population dynamics, sport-for-health promotion, competitive sports analytics (talent identification, performance monitoring), diffusion models Methodological : Bayesian inference, computational statistics, spatio-temporal modeling, machine learning, Monte Carlo methods Publications demonstrate interdisciplinary trends across: Sports statistics (decathlon/heptathlon scoring, youth-to-elite transition) Bayesian spatio-temporal health modeling Missing data patterns in longitudinal athlete datasets Scientific Recognition : 2023 Honorable Mention for Best PhD Thesis in Applied Statistics by the Italian Statistical Society (SIS) Teaching includes statistics exercises for economics degrees and R coding in finance analytics. Office hours: Wednesday 10-12 by appointment.

Jose Davila-VelderrainView profile

Research Professor

• Computational Systems Biology
• Neuroscience
• Single-Cell Genomics
• +4 more

Jose Davila-Velderrain serves as a Research Group Leader at HT's Neurogenomics Research Centre with adjunct affiliation at the Computational Biology Research Centre, commencing his role in January 2022 after five years as a postdoctoral researcher at MIT and the Broad Institute of MIT and Harvard. His position centers on leading interdisciplinary research into human brain cellular complexity through computational and theoretical frameworks. Research Interests: Dr. Davila-Velderrain's work integrates computational systems biology with neuroscience to decode the diversity and dynamic behavior of human brain cells. His group investigates cellular identity acquisition during development and vulnerability mechanisms in neurodegeneration, employing single-cell genomic measurements combined with machine learning, network theory, and statistical physics. This research reveals fundamental principles of neural circuit robustness and failure while developing conceptually intuitive computational resources for the life-science community. Advising and Collaborations: The Davila-Velderrain Group mentors four PhD students (Erik Bot, Carlo Manenti, Beatrice Paolucci, Asia Zonca) alongside postdoctoral researchers and bioinformaticians. Supported by the Neurogenomics Research Centre infrastructure, the team maintains active collaborations across computational biology and neurogenomics domains without specified external grant details in source materials. Laboratory Focus: The laboratory utilizes human brain tissue as a primary model system to elucidate systems-level mechanisms of cellular identity and state dynamics, complemented by cross-species comparisons in model organisms to identify conserved and divergent neurobiological properties. Methodological innovations prioritize creating accessible computational frameworks that bridge theoretical models with experimental neurogenomics data.

Luca OnetoView profile

Professor

• Statistical Learning Theory
• Trustworthy Artificial Intelligence
• Machine Learning
• +3 more

Luca Oneto is a Full Professor in the Department of Computer Science, Bioengineering, Robotics, and Systems Engineering (DIBRIS) at the University of Genoa's Polytechnic School, serving on the Department Board and ICT Commission. His academic trajectory spans from Postdoc (2014-2016) to Assistant Professor (2016-2019), Associate Professor at both University of Genoa (2019-2024) and University of Pisa (2019), culminating in his current Full Professor role. His educational foundation includes: BSc in Electronic Engineering (2008, University of Genoa) MSc in Electronic Engineering (2010, University of Genoa) PhD in Computer Engineering (2014, University of Genoa) Professor Oneto's research is anchored in Statistical Learning Theory and Trustworthy Artificial Intelligence , with pioneering work on reliable, interpretable machine learning systems. He bridges theoretical advancements with practical applications in Data Science , developing cutting-edge AI algorithms for real-world problem solving across industrial and European Union frameworks. His expertise spans foundational learning theory, robust AI deployment, and ethical algorithm design. His exceptional contributions are recognized by: Amazon AWS Machine Learning Award Somalvico Award for Italy's best young AI researcher As coordinator and local responsible for industrial projects, Horizon 2020, and Horizon Europe initiatives, he drives high-impact research translation. Co-founding the spin-off ZenaByte s.r.l. (2018), he actively commercializes AI innovations. His teaching portfolio includes graduate courses in Machine Learning, Deep Learning, Data Analysis, and Trustworthy AI across Computer and Management Engineering programs at the University of Genoa.

Alessio SignoriView profile

Associate Professor

• Medical Statistics
• Epidemiology
• Biostatistics
• +3 more

Alessio Signori is an Associate Professor in the Department of Health Sciences (DISSAL) at the University of Genoa, affiliated with the School of Medical and Pharmaceutical Sciences. He serves as a Member of the Joint Teacher-Student Commission and the research commission for the School. His teaching responsibilities span multiple degree programs including Rehabilitation Sciences, Medical Radiology Techniques, Dentistry, and Anesthesia Specialization. Professor Signori's research focuses on medical statistics and epidemiology, with applications in neurodegenerative diseases, infectious diseases in critical care, geriatrics, and oncology. His work integrates biostatistical methods to analyze longitudinal patterns in Alzheimer's disease, mortality determinants in immunocompromised patients, and frailty assessment in elderly populations. He also explores the application of deep learning for early diagnosis of bloodstream infections. His recent publications (2025) demonstrate a strong emphasis on clinical and epidemiological research. Key themes include neurodegenerative disorders (Alzheimer's), critical care infections (Pneumocystis pneumonia), geriatric assessment tools, and machine learning applications in infectious disease diagnostics. The work often involves multicenter collaborations and addresses mortality, frailty, and early diagnosis in vulnerable patient populations.

• Mechanical and Thermal Measurements
• Additive Manufacturing
• Thermography
• +7 more

Laura Fabbiano is an Associate Professor in the Department of Mechanics, Mathematics & Management at Politecnico di Bari, Italy. Her research focuses on mechanical and thermal measurements, additive manufacturing, thermographic inspection, and hydrogen separation technologies. Department: Mechanics, Mathematics & Management Email: laura.fabbiano@poliba.it Her work spans interdisciplinary domains including: Mechanical Engineering: additive manufacturing, fluid dynamics, and vibration analysis Thermography: non-destructive testing, melanoma detection, and conservation of historical artifacts Hydrogen Production: membrane reactors, biomass-derived syngas, and clean energy systems Smart Technologies: Industry 4.0 applications, energy-efficient grids, and IoT-based diagnostics Recent publications emphasize process optimization in 3D printing, metrological improvements in thermographic imaging, and sustainable energy solutions using Pd-based membranes. Her research integrates experimental, statistical, and numerical methodologies to address industrial and environmental challenges.

• Computer vision and pattern recognition
• Digital image processing
• Medical image processing
• +4 more

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.

Bartolomeo MontrucchioView profile

Professor

• Computer Vision
• Quantum Computing
• Image Processing
• +5 more

Bartolomeo Montrucchio is a Full Professor of Information Processing Systems (ING-INF/05) at the Department of Control and Computer Engineering (DAUIN) of the Polytechnic University of Turin. He is a member of the Interdepartmental Center Photonext - PoliTo Interdepartmental Center on Applied Photonics and serves as deputy director at the Interuniversity Center of Regional Interest for the Training of Secondary School Teachers (CIFIS) since July 2012. Additionally, he has held an adjunct professor position at the University of Illinois at Chicago during July 2008. Professor Montrucchio's research spans several cutting-edge areas with a primary focus on quantum computing, computer vision, and sensor networks. His work encompasses image processing, scientific visualization, parallel and distributed systems, and wireless sensor networks. He actively contributes to European research initiatives including the EQUO (European QUantum ecOsystems) project as Scientific Responsible. His research bridges theoretical computer science with practical applications across multiple industries. His publication record shows a strong trajectory toward quantum technologies, with numerous recent publications focusing on quantum machine learning, quantum algorithms for financial applications, and quantum applications in cybersecurity. His work demonstrates increasing emphasis on practical implementations of quantum computing in real-world scenarios, particularly in industrial settings and telecommunications. Best student paper award at BIOSIGNAL2002, conferred by EURASIP, Italy (2002) Associate Editor of IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2019-present) Professor Montrucchio actively supervises numerous PhD students working on quantum computing applications across various domains including finance, cybersecurity, traffic optimization, and industrial use cases. His teaching portfolio includes courses on Quantum Computing, Parallel and Distributed Computing, and Image Processing and Computer Vision across multiple degree programs including Computer Engineering, Biomedical Engineering, and Quantum Engineering. He leads multiple research projects funded by both competitive calls and commercial contracts, with a significant focus on quantum technologies since 2019. His patent portfolio includes several inventions related to tire manufacturing processes and visual rehabilitation for telemedicine.

• 6G communications
• Information theory
• MIMO systems
• +8 more

Giorgio Taricco is a Full Professor at the Department of Electronics and Telecommunications (DET) of Polytechnic University of Turin. His research focuses on 6G communications, information theory, and MIMO systems, with expertise spanning telecommunications, signal processing, and wireless communication. He leads the Advanced techniques for digital transmission (CommGroup) research group and has been a Scientific Responsible for multiple projects, including EU-funded MASCOT and HICCUP, and commercial contracts like JEANS and NEXT GEN DATALINK. Research Interests: 6G networks, MIMO systems, information theory, satellite communication, energy-efficient coding Notable Projects: MASCOT (2006-2009), HICCUP (2002-2003), JEANS (2022-2024), WORK ITEM 4.4 (2024-2025) His recent publications analyze secrecy energy efficiency in metasurface-aided networks, IRS-NOMA for integrated sensing, and advanced precoding techniques, reflecting trends in wireless security and next-generation communication systems. As an IEEE Fellow since 2010, he contributes to editorial boards, including Entropy (2025-). He supervises PhD students like Agbotiname Lucky Imoize and Aysegul Ilay Tunali, integrating education with cutting-edge research in telecommunications.

• Systems and Control Engineering
• Control Theory
• System Identification
• +4 more

Michele Taragna is a Tenured Associate Professor in the Department of Electronics and Telecommunications (DET) at the Polytechnic University of Turin, actively teaching across degree programs: Experimental Modeling for PhD students in Electrical, Electronic and Communications Engineering (2019-2025), Estimation and System Identification for Mechatronic Engineering Master's program (2019-2026), and Automatic Control for Computer Engineering Bachelor's program (2019-2026) as course holder or collaborator. His research centers on Systems and Control Engineering , with primary interests in data-driven control for autonomous vehicles and fleets, direct virtual sensors, and machine learning-enhanced system identification. Key areas include Set Membership methods for robustness under bounded noise, computational complexity reduction in Nonlinear Model Predictive Control (NMPC), sensor fusion for robotics, and applications in automotive suspensions. This work aligns with ERC sectors PE7_1 (Control engineering), PE1_20 (Control theory), and PE6_12 (Scientific computing). Trends in his publications (2024-2004) reveal sustained innovation in applying Set Membership identification to NMPC for autonomous vehicles, achieving real-time feasibility through search domain reduction. Sensor fusion techniques using Kalman filters for mobile manipulators and data-driven filter design for uncertain LTI systems with bounded noise are recurring themes, emphasizing practical implementation and computational efficiency. Scientific awards: None documented in provided materials. Advising and research funding: Supervised PhD student Mattia Boggio (2020-2024) in Electrical, Electronic and Communications Engineering; thesis on Real-time Nonlinear Model Predictive Control with domain reduction. Led the nationally funded PRIN project Controllo ad alte prestazioni a partire dai dati sperimentali (2007-2009) as Scientific Responsible. He is a core member of the Automatica research group within DET, focusing on system identification, control design, and validation for dynamic systems with applications in automotive and robotics domains.

• Bayesian inference
• Biostatistics
• Clinical trials
• +7 more

Mauro Gasparini is a Full Professor at the Department of Mathematical Sciences (DISMA) of Polytechnic University of Turin. He serves as Director of DISMA since 2019, Member of Academic Senate, and co-leader in the SmartData@PoliTO Big Data Laboratory. His career spans academia and industry, including roles at Purdue University (Assistant Professor 1992-1996) and Novartis (Senior Statistician 1996-1998). He has been Editor of Biometrical Journal (2012-2015) and maintains referee activities across international journals. PhD from University of Michigan (1992, Dirichlet process applications) Academic leadership: Department Director, Editorial boards, ISTAT Advisor Research spans Bayesian methodology with biomedical applications Maintains collaborations with Novartis, Chiesi, and research centers His research interests focus on Bayesian inference , Biostatistics , and Clinical trials methodology, particularly addressing issues in pharmaceutical development, genomic data analysis, and medical decision-making. Recent work includes vaccine efficacy modeling, optimal imaging timing for cancer diagnostics, and adaptive trial designs. Key publication trends show interdisciplinary applications in Statistics in Medicine , Biometrics , and Statistical Methods in Medical Research , with emphasis on biomedical data science, Bayesian adaptive methods, and clinical decision support systems. Scientific contributions include: Editor, Biometrical Journal (2012-2015) Advisor, Italian National Institute of Statistics (2020-2024) Leadership in multiple research projects (NODES, SORGENTE, IDEAS) As PhD advisor, he supervises students in: Shaoshi Tang (Clinical trial modeling) Saeed Sani (Biomedical data analysis) Marco Ratta (Genomic statistics) Luca Rondano (Bayesian methods) Vittorio Zampinetti (Tumor DNA sequencing) Fulvio Di Stefano (Evidence-based decision statistical methods) He leads research projects in pharmaceutical statistics, genomic surveillance, and spatial risk assessment frameworks, with recent emphasis on SARS-CoV-2 analysis and cancer progression modeling.

• Liver Surgery
• Hepatocellular Carcinoma
• Minimally Invasive Surgery
• +5 more

Professor Luca Antonio Maria Aldrighetti is a prominent figure in liver surgery at Università Vita-Salute San Raffaele's Faculty of Medicine and Surgery, Department of General Surgery. With over 1000 publications and leadership roles in major surgical organizations, he specializes in minimally invasive approaches for hepatocellular carcinoma and complex liver malignancies. His research focuses on: Development of prognostic models for liver resection outcomes Comparative studies of laparoscopic versus open liver surgery Innovative endpoints for liver surgery clinical trials (including CELS) Management of hepatocellular carcinoma in cirrhotic patients Colorectal liver metastases treatment strategies Professor Aldrighetti leads the I GO MILS Registry (Italian Group of Minimally Invasive Liver Surgery) and serves as President of AICEP (Italian Association of HPB Surgeons). His recent publications demonstrate a strong emphasis on predictive modeling, surgical innovation, and international collaboration in liver surgery. Professional affiliations include: President of AICEP (2019-present) President of I GO MILS Registry (2014-present) Member of ILLS Board of Directors (2019-present) Member of E-AHPBA Scientific Committee (2021-present) Full member of European Surgical Association (2022-present) His advising and research activities focus on surgical innovation, with extensive collaboration across international centers. The I GO MILS Registry serves as his primary research platform, facilitating multicenter studies on minimally invasive liver surgery techniques and outcomes.

• Shape Analysis
• Permutation Tests
• Multivariate Statistics
• +4 more

Chiara Brombin serves as Associate Professor of Statistics (SECS-S/01) at Vita-Salute San Raffaele University's Faculty of Psychology and contributes to the University Center for Statistics for Biomedical Sciences (CUSSB). Her academic career at the institution spans from Research Fellow (2010-2013) through fixed-term researcher positions (2013-2021) to her current role, demonstrating sustained institutional engagement and scholarly progression. Education: PhD in Statistical Sciences (2009), University of Padua Bachelor's Degree in Statistical and Economic Sciences (2005), University of Padua Research Focus: Dr. Brombin specializes in shape analysis, permutation testing, and advanced multivariate modeling with biomedical applications. Her work develops computational frameworks integrating facial expressions/biosignals (FIRB 2012 project) and applies joint latent class models to clinical subgroups. Recent publications emphasize statistical innovation in gene therapy efficacy, cancer treatment optimization, and pandemic response analytics through rigorous longitudinal/survival modeling. Publication Trends: Her 2024-2025 output in Nature, Science Translational Medicine, and specialized biostatistics journals reveals three converging themes: (1) network-based approaches for psychophysiological healthcare data, (2) joint modeling of longitudinal biomarkers with survival outcomes in immunology/oncology, and (3) shape analysis applications in genomic editing safety assessment. These works consistently integrate Bayesian networks with permutation-based validation. Scientific Recognition: Futuro in Ricerca 2012 award (MIUR) for emotion interpretation research Academic Leadership: Dr. Brombin has coordinated doctoral committees for Cognitive and Behavioral Sciences (2022-2024 cycles) and secured FIRB project funding as national coordinator. Her teaching portfolio spans undergraduate statistics methodology to graduate advanced modeling, with current responsibility for five courses including Multidimensional Data Analysis and Advanced Modeling in Psychology. She maintains active collaboration with CUSSB research teams on gene therapy and cancer imaging projects. Research Infrastructure: As core faculty in CUSSB, she leads statistical development for interdisciplinary teams in hematopoietic stem cell research and prostate cancer radiotherapy trials, applying shape analysis to [11C]-choline PET/CT imaging data and developing open-source tools for joint model implementation.

• Stochastic Models
• Machine Learning
• Artificial Intelligence
• +4 more

Fabrizio Durante is a Full Professor of Probability and Mathematical Statistics at the University of Salento, Department of Mathematics and Physics "Ennio De Giorgi" in Lecce, Italy. Previously, he served as Full Professor of Mathematical Methods for Economics, Finance, and Actuarial Sciences from December 2016 to October 2024. Durante earned his PhD in Mathematics from the University of Lecce and completed his Habilitation in Mathematics at Johannes Kepler University of Linz in 2010. His academic career includes positions as Assistant Professor (2010-2014) and Associate Professor (2015-2016) of Statistics at the Free University of Bozen-Bolzano. His research focuses on stochastic methods and models for complex systems and machine learning, with significant applications in quantitative risk management across hydrology, environmental sciences, economics, and finance. Durante is particularly renowned for his contributions to copula theory, co-authoring the monograph "Principles of Copula Theory" with Carlo Sempi. Durante currently serves as national coordinator of the Italian MIUR project "Stochastic Modeling of Compound Events" (2023-25) and is affiliated with the ICSC National Research Center in High Performance Computing, Big Data and Quantum Computing. STAHY Best Paper Award 2015 from the International Commission on Statistical Hydrology (jointly with G. Salvadori and C. De Michele) Durante maintains significant editorial responsibilities as associate editor of "Dependence Modeling" and area editor of "Fuzzy Sets & Systems" and the "International Journal of Approximate Reasoning." He is co-chair of the "Dependence Models and Copulas" team of the ERCIM Working Group on Computational and Methodological Statistics. His expertise is regularly sought as an invited plenary speaker at international conferences in stochastic methods and dependence modeling.

Ilaria Lucrezia AmeriseView profile

Associate Professor

• Multivariate Analysis
• Time Series
• Nonparametric Statistics
• +3 more

Ilaria Lucrezia Amerise is an Associate Professor in the Department of Economics, Statistics and Finance 'Giovanni Anania' (DESF) at the University of Calabria (UNICAL). Her research focuses on multivariate analysis, time series, nonparametric statistics, and statistical methods for complex/high-dimensional data including functional and spatial data. Editor-in-Chief of JP Journal of Biostatistics (ANVUR Area 13) Editorial Board Member of International Journal of Statistics and Systems (ANVUR Area 13) Recent research involves: Statistical preprocessing of crowdsourced data for Nigerian food prices Quantile regression with heteroskedasticity and non-crossing constraints Electricity demand forecasting via Reg-SARMA models Exchange rate prediction using simultaneous prediction intervals Time series outlier detection and smoothing techniques She contributes to academic governance through the Laboratorio Statistico Informatico (Statistical Informatics Lab) within DESF. Teaching includes undergraduate and graduate courses in Statistics, with materials available in both Italian and English.