Find Academic Supervisors

• Digital Circuits and Systems
• Energy Efficiency
• Smart Cities
• +13 more

Enrico Macii is a Full Professor at the Politecnico di Torino, affiliated with the Interuniversity Department of Regional and Urban Studies and Planning (DIST) and the Department of Control and Computer Engineering (DAUIN). He leads the Electronic Design Automation (EDA) research group and holds key roles as Scientific Advisor for the Politecnico-STMicroelectronics partnership and Scientific Contact for the European Chips Joint Undertaking. Research Interests: His work spans digital circuits and systems, energy efficiency, smart cities, Industry 4.0, and smart manufacturing. He focuses on embedded and cyber-physical systems, low-power design, neuromorphic computing, AIoT, and sustainable urban development. Recent Publications: His recent research demonstrates strong trends in edge AI, neuromorphic computing, and smart energy systems. Articles highlight innovations in low-power hardware acceleration, federated learning, physics-informed AI, and digital twin applications for urban and industrial systems. There is a clear emphasis on deploying AI efficiently on constrained devices and integrating physical models with machine learning. J. William Fullbright Fellowship (1993) Best paper award IEEE European Design Automation Conference (1996) Best paper award ACM/IEEE Great Lakes Symposium on VLSI (2008) DAC Service Award (2014) IEEE Fellow (2006) DATE Fellow (2014) Advising and Grants: He has supervised over 25 PhD students in computer engineering, AI, and urban systems. His research is funded by major EU programs (Horizon 2020, PNRR, KDT JU), national (PRIN, FAR), and regional grants, as well as industrial contracts with STMicroelectronics, Michelin, and Cefriel. He leads numerous high-impact projects in smart manufacturing, energy efficiency, and digital twins. Labs and Teams: He is a core member of the EDA Group, an interdepartmental research team at Politecnico di Torino focusing on VLSI-CAD, bioinformatics, smart cities, and Industry 4.0. He also contributes to IAM@PoliTo (Integrated Additive Manufacturing) and leads multiple EU and national research consortia.

• Neuroscience
• Computational Modeling
• Meditation
• +5 more

Giuseppe PAGNONI is an Associate Professor at the Department of Biomedical, Metabolic and Neurosciences of Università di Modena e Reggio Emilia . His research spans neuroscience , computational modeling , meditation , and brain network analysis . He teaches courses such as Neural Systems Modeling for Bioengineering students and Human Physiology and Pathophysiology in Medicine programs, with a focus on predictive coding , default mode network , and emotional valence processing . Pagnoni has contributed to ENIGMA-Meditation , a global initiative exploring meditation's neuroscientific basis, and developed sparse Bayesian models for analyzing brain network individual differences. His work on Alzheimer's disease investigates anosognosia mechanisms through resting-state fMRI, while other studies examine ghrelin's role in obesity-related reward processing and oxytocin's effects on social anxiety . He employs dynamic causal modeling and predictive processing frameworks to study cognitive effort, mind wandering, and neuromorphic learning. His methodological expertise includes functional MRI , independent component analysis , and computational simulations .

Hugo Georges Victor LavenantView profile

Assistant Professor

• Optimal Transport
• Calculus of Variations
• Convex Analysis
• +3 more

Hugo Georges Victor Lavenant serves as Assistant Professor in the Department of Decision Sciences at Bocconi University, Milan, where he has held a faculty position since 2020. Previously, he completed a postdoctoral fellowship at the University of British Columbia (2019-2020) under the Pacific Institute of Mathematical Sciences and earned his PhD in Mathematics from Université Paris-Sud (2016-2019) under Filippo Santambrogio's supervision. His academic foundation includes: PhD in Mathematics, Université Paris-Sud (2016-2019) Studies at École Normale Supérieure (2012-2016) covering mathematics, physics, history, and philosophy of science Classes préparatoires in mathematics and physics (2010-2012) Lavenant's research centers on optimal transport theory and its applications across mathematical disciplines. He investigates geometric structures in Wasserstein spaces, develops numerical methods for dynamical optimal transport, and bridges theoretical advances with Bayesian statistics. His work demonstrates particular innovation in trajectory inference for biological data and dependence measures for random measures, connecting pure mathematics with computational statistics. Recent publications reveal accelerating interdisciplinary impact, with 2024-2025 works extending optimal transport to machine learning (kernel methods, variational inference) and data science (opinion dynamics, single-cell analysis). This trajectory shows increasing methodological sophistication in handling measure-valued mappings and non-smooth geometries while maintaining computational tractability. Award recognition includes: Pacific Institute of Mathematical Sciences Postdoctoral Fellowship Lavenant actively mentors early-career researchers through formal advising relationships and collaborative projects. He currently supervises two PhD candidates (George Kanchaveli and Francesco Mascari, co-advised with Marta Catalano) and has guided Master's students including Mathis Hardion and Niccolò Bargellini. His teaching portfolio spans advanced analysis, optimization, and real analysis courses at Bocconi, reflecting his commitment to mathematical rigor in education. He operates within Bocconi's Decision Sciences ecosystem while maintaining international collaborations with researchers at UBC, Université Paris-Sud, and statistical groups worldwide. Current projects focus on entropy-based transport methods and geometric approaches to nonparametric statistics, positioning his work at the intersection of theoretical mathematics and data-driven applications.

Silvia BOZZAView profile

Associate Professor

• Forensic Statistics
• Bayesian Statistics
• Evidence Evaluation
• +4 more

Silvia BOZZA is an Associate Professor in the Department of Economics at Ca' Foscari University of Venice, where she also serves as Department Delegate for Teacher Training for High Schools. Her academic profile is centered around Statistics (SSD STAT-01/A), with a strong focus on forensic applications of statistical methods. She maintains dual institutional affiliations, working within both the Department of Economics (based in San Giobbe) and the Interdepartmental School of Economics, Languages and Entrepreneurship for International Exchanges (located in Treviso - Palazzo San Paolo). Professor BOZZA's research primarily explores the application of Bayesian statistics to forensic science and legal evidence evaluation. Her work demonstrates a consistent trajectory from early publications on spatio-temporal models to her current specialized focus on forensic statistics. She has developed expertise in probabilistic graphical models, Bayes factors, and statistical decision theory as applied to evidence interpretation. Her research addresses critical questions in forensic science regarding how to properly evaluate and communicate statistical evidence in legal contexts, with particular attention to handwriting analysis, DNA evidence, and authorship attribution. Her publication record shows remarkable consistency and productivity, with over 90 publications spanning from 2001 to 2025. Recent work (2022-2025) demonstrates her ability to address contemporary challenges, including AI authorship discrimination using Bayesian methods for distinguishing human from ChatGPT-generated text. Her articles frequently appear in high-impact forensic and statistical journals including Law, Probability & Risk, Forensic Science International, and Annual Review of Statistics and its Application. Professor BOZZA actively supervises undergraduate and graduate theses involving data analysis, requiring knowledge of statistical software including the R programming language used in the Statistics for Economics Laboratory course. She encourages students to propose topics while offering guidance to refine research questions. Her office hours show she maintains regular availability for student consultations, indicating active engagement in academic mentoring.

• Mathematical Modeling
• Kinetic Theory
• Socio-Economic Dynamics
• +3 more

Giuseppe Toscani is a Full Professor at the Department of Mathematics of the University of Pavia. His research focuses on mathematical modeling of socio-economic systems, epidemiological dynamics, and kinetic theory. He explores topics such as wealth distribution, opinion formation, and epidemic spread using advanced methods in partial differential equations and statistical mechanics. Key research areas include analysis of inequality indices (Gini Index extensions), multi-agent systems, and nonlinear diffusion processes. His work intersects with applications in public health, economic policy, and social behavior modeling, emphasizing the interplay between theoretical frameworks and real-world phenomena. Education: Not explicitly detailed in provided texts, but his academic role implies a doctoral degree in Mathematics or related field. Labs/Teams: Affiliated with the Methods and Models for Applied Sciences research group. Grants/Awards: No specific grants or awards listed in the text. Recent publications address condensation patterns in opinion dynamics, multidimensional inequality metrics, and pandemic modeling through compartmental systems. His kinetic models provide foundational tools for understanding complex social and biological systems.

Lorenzo FarinaView profile

Professor

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

Marco GrangettoView profile

Professor

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

Gianvito PioView profile

Associate Professor

• Data Mining
• Bioinformatics
• Social Network Analysis
• +3 more

Gianvito Pio is an Associate Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. His research spans data mining, bioinformatics, social network analysis, multi-relational data mining, and big data analytics. He holds a PhD in Computer Science from the University of Bari (2015) and has taught courses such as Big Data Management and Analysis and Security in Blockchain Technology . As an expert in heterogeneous network analysis, he develops methods for anomaly detection in cryptocurrency trends, microbiome data interpretation, and legal judgment clustering. He leads the local research unit of the PRIN 2022 project COCOWEARS and contributes to journals like the Machine Learning Journal and Expert Systems with Applications as an associate editor. His recent work includes multi-view learning for risk identification in dynamic networks, spatially-aware models for energy forecasting, and biclustering algorithms for biological data. Collaborations with researchers such as Michelangelo Ceci and Antonio Pellicani highlight his interdisciplinary approach. Gianvito Pio also organizes academic events like the Discovery Science conference and contributes to open-source software tools including HOCCLUS2 and GENERE .

• Asymptotic statistics
• Bayesian inference
• Biostatistics
• +9 more

Enrico Bibbona is an Associate Professor at the Department of Mathematical Sciences (DISMA) at Politecnico di Torino, where he contributes to the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. His academic profile spans statistics, mathematical modeling, and data science applications across multiple engineering disciplines. His primary research interests include: Asymptotic statistics Bayesian inference Biostatistics Industrial statistics Mathematical biology and modeling in life sciences Statistical inference for stochastic processes Stochastic modeling His specific research lines focus on Reaction Networks approximation and inference, Statistical modeling of animal calls, Statistics and modeling for the SmartPad system (in collaboration with ITT), and Statistics for deterministic and stochastic dynamical models. His work bridges theoretical statistics with practical applications across various scientific domains, particularly in biological systems, industrial applications, and data science. His notable recognition includes the Elsevier/SPA Travel Award presented by Elsevier and Bernoulli Society, Denmark (2011). Professor Bibbona actively mentors PhD students across multiple programs, currently supervising Daniele Poggio and Saeed Sani in Mathematical Sciences, and has previously guided Elena Sabbioni (thesis: Statistical Methods for Complex Biological and Clinical Data) and Hiu Ching Yip (thesis: Statistical models for understanding biomedical data). His teaching spans Mathematical Engineering, Data Science and Engineering, and Communications Engineering programs, where he teaches courses including BioQuants, Statistical methods in data science, and Project: Software-defined communication systems. He is a key member of the Statistics and Data Science research group at DISMA and has contributed to practical applications through patents including "BRAKING SYSTEM TEMPERATURE ESTIMATOR" and "A method for estimating the wear of a vehicle brake element," demonstrating his ability to translate theoretical work into real-world solutions.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +9 more

Carlo Masone is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN), Politecnico di Torino, affiliated with the College of Electronic, Telecommunications, and Physics Engineering. He is also an invited member of the College of Management and Production Engineering and the College of Mathematical Engineering, reflecting his interdisciplinary contributions. His research focuses on artificial intelligence, machine learning, computer vision, and robotics, with significant work in visual place recognition, federated learning, and geospatial AI. His research interests span Artificial Intelligence , Machine Learning , Computer Vision , Robotics , Visual Place Recognition , Federated Learning , Open-Set Segmentation , and Geospatial AI . He has developed methods for uncertainty quantification in ML models for autonomous vehicles and contributed to digital heritage through the MAPP project. The recent publications highlight a strong trend in visual place recognition (e.g., EigenPlaces, MeshVPR), geolocalization of astronaut photography (Earthloc, EarthMatch), and robust segmentation (Mask2Anomaly). There is a clear emphasis on leveraging pre-trained features, federated learning for privacy-preserving recognition, and topological methods in vision. His work bridges robotics, AI, and real-world applications in autonomous systems and cultural heritage. Scientific Awards: Outstanding Reviewer at CVPR (2022, 2023, 2024) IROS JTCF Novel Technology Paper Award (2016) Best Paper Award Finalist at IEEE ICIA (2016) ELLIS Fellow (2024–) He advises PhD students in Artificial Intelligence and Computer and Systems Engineering and leads commercial research projects such as Quantifying Uncertainties in ML Models for Driver-Assisted and Autonomous Vehicles and MAPP - Phygital and Participatory Alpine Museums . He has previously collaborated with the Max Planck Institute for Biological Cybernetics (2014–2017). He teaches courses including Machine Learning for Mathematical Engineering , Robot Learning , and GeoAI , and contributes to programming and data science curricula. He is also a co-inventor on a patent for automated neural network design.

Laura AstolfiView profile

Associate Professor

• Biomedical Engineering
• Neuroscience
• Brain Connectivity
• +4 more

Laura Astolfi is an Associate Professor at the Department of Computer, Control, and Management Engineering , Sapienza University of Rome, and a Researcher at Fondazione Santa Lucia Hospital, Italy. She leads the Bioengineering and Bioinformatics Laboratory and is a Junior Fellow at the Sapienza School for Advanced Studies. Ph.D. in Biomedical Engineering, University of Bologna Master's in Electronic Engineering, University of Rome Sapienza Her research spans brain connectivity , high-resolution EEG source reconstruction , neurorehabilitation , hyperscanning , and social neuroscience . Key applications include disorders of consciousness and motor recovery post-stroke. Recent publications focus on deep learning for EEG localization , functional ultrasound imaging , and multi-subject brain network analysis . Awards include World's Top 2% Scientists (2021–2024) and the Best Under-40 Researcher Award at Sapienza (2010). Associate Editor, Brain Topography , Medical & Biological Engineering & Computing , and IEEE Open Journal of Engineering in Medicine and Biology 261 peer-reviewed papers, 8933 citations, H-index 45

Patrizio CampisiView profile

Professor

• Digital Signal Processing
• Image Processing
• Biometrics
• +12 more

Patrizio Campisi is a Full Professor in the Section of Applied Electronics at the Department of Engineering, Roma TRE University, Rome, Italy. He leads cutting-edge research in digital signal and image processing with applications to secure multimedia communications and biometrics. He has held visiting positions at the University of Toronto, Beckman Institute (UIUC), and École Polytechnique de Nantes, and has been a Marie Curie Fellow (2010–2014). His educational background includes a Laurea (summa cum laude) in Electronic Engineering from Sapienza University of Rome and a Ph.D. in Electrical Engineering from Roma TRE University. His research focuses on secure biometric recognition (signature, keystroke, EEG, vein), digital watermarking, blind image deconvolution, HDR imaging, and privacy-preserving technologies. He has contributed significantly to template protection, multimodal biometrics, and forensic image analysis. His work bridges engineering, computer science, and security, with strong applications in mobile authentication, border control, and social media safety. The recent publications reflect a strong trend in biometrics, image forensics, and privacy-enhancing technologies, with a focus on real-world applications in mobile systems, healthcare, and law enforcement. Key themes include secure authentication, de-identification, and computational imaging. IEEE Second International Conference on Biometric Systems 2008 Best Student Paper Award IEEE Biometric Symposium 2007 Best Paper Award IEEE International Conference on Image Processing 2006 Best Student Paper Award Marie Curie Fellow (2010–2014) NATO-CNR Advanced Fellowship (2003) IEEE Senior Member Prof. Campisi has supervised numerous students and leads the BioMedia4n6 lab. He has secured major EU grants including H2020 projects AMBER, COSMOS, and ENCASE. His editorial leadership includes serving as Editor-in-Chief of IEEE Transactions on Information Forensics and Security (2018–2020) and Chair of the IEEE Information Forensics and Security Technical Committee (2017–2018). He is actively involved in research networks such as COST Actions on de-identification and biometrics-forensics integration, and has contributed to EU policy via the BEST Thematic Network on biometrics and fundamental rights. His lab, BioMedia4n6, focuses on biometrics, multimedia forensics, and privacy-aware systems.

Alfredo PulvirentiView profile

Professor

• Bioinformatics
• Biomedicine
• RNA Interference
• +9 more

Alfredo Pulvirenti is a Full Professor of Computer Science at the University of Catania, Italy. He holds a joint appointment with the Department of Clinical and Experimental Medicine and the Department of Mathematics and Computer Science. Born in 1974, he earned his Laurea (1999, summa cum laude), PhD (2003), and post-doc (2004) in Computer Science from the University of Catania. His academic career includes roles as Assistant Professor (2005-2014), Associate Professor (2014-2023), and Full Professor since 2023. His research bridges Bioinformatics and Biomedicine , focusing on RNA interference (microRNAs, long non-coding RNAs) via stochastic and network-based inference methods. Key contributions include subgraph matching , motif finding , and multiple network alignment using Monte Carlo techniques for analyzing biological networks . He also applies time series analysis to seismic and infrasonic signals through collaborations with Italy's National Institute of Geophysics and Volcanology (INGV). Recent publications highlight advancements in graph algorithms (e.g., MultiGraphMatch, ArcMatch) and systems biology tools like MITHrIL and SPECifIC. He co-directs the Jacob T. Schawartz International School for Scientific Research and leads national/regional projects on Bioinformatics and Big Data in Cancer . His group includes 3 researchers, 1 post-doc, and 3 PhD students in Complex Systems. Awards and Leadership : Best Paper Award at CBS 2009 Guest Editor for BMC Bioinformatics, Briefings in Bioinformatics, Elsevier Program Committee Member for major international conferences International Collaborations span institutions like NYU, Ohio State University Wexner Medical Center, Brown University, Tel Aviv University, Toronto University, and Università di Ancona.

Yashar DeldjooView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Recommender Systems
• +7 more

Yashar Deldjoo is an Assistant Professor specializing in artificial intelligence, machine learning, and their applications in healthcare, user modeling, and recommender systems. His research focuses on explainable AI, ethical AI practices, multimodal learning, and brain-computer interfaces. He has contributed to projects like WeBIUM (Wearable Devices and Brain-Computer Interfaces for User Modelling) and ARIEL (emotional support via BCI and LLMs). His work bridges technical advancements with societal impacts, addressing challenges in healthcare diagnostics (e.g., Alzheimer’s disease prediction), environmental sensing (real-time pollution-aware recommendations), and cybersecurity (data poisoning attacks on recommendation systems). He also explores fairness in large language models and the integration of EEG-based emotion recognition systems. Notable contributions include frameworks like BRAINEX (CNN model evaluation for brain age prediction), Ducho (multimodal feature extraction for recommendation systems), and KGUF (knowledge-aware graph-based recommenders). He chairs conferences like RecSys and contributes to industry-relevant standards for trustworthy AI systems. His research emphasizes interdisciplinary collaboration, combining neural networks, ethical considerations, and human-computer interaction. Current projects involve neurorehabilitation tools, quantum security for smart cities, and AI-driven usability testing.

Craig A. GlastonburyView profile

Researcher

• Computational Biology
• Machine Learning
• Genetic Epidemiology
• +4 more

Craig A. Glastonbury is a Researcher and Research Group Leader at Human Technopole (since 2022), leading the Glastonbury Group. He holds a PhD in computational biology from King’s College London (2013-2017) and previously served as a machine learning researcher at BenevolentAI (2019-2022) and Postdoctoral Fellow at the University of Oxford (2017-2019). His work focuses on integrating machine learning with genetic and imaging data to uncover disease mechanisms, particularly in histopathology and cardiovascular phenotypes. He currently advises BenevolentAI and serves as a guest associate editor for AHA Circulation. Research interests include computational biology, histopathology imaging, GWAS, and single-cell RNA-seq. The Glastonbury Group develops methods to extract phenotypic traits from imaging modalities like whole slide imaging (WSI) and links these to genetic data to study disease pathways. Current projects involve analyzing large-scale biobank data to identify genetic contributions to complex diseases such as inflammatory bowel disease and oncology. Group members include PhD students Francesco Cisternino, Giuditta Clerici, Emma Esther Didelon, and Emanuel Soda, along with postdoctoral researchers and bioinformaticians. The team actively recruits for positions in machine learning, phenotyping, and genetic discovery. Craig’s work emphasizes scalable pipelines for GWAS and unsupervised phenotype discovery, with applications in cardiovascular and metabolic research. Contact: craig.glastonbury@fht.org