Find Academic Supervisors

• Evolutionary Computation
• Machine Learning
• Neurodegenerative Disease Prediction
• +4 more

Dr. Francesco Fontanella is an Associate Professor at the Department of Electric and Information Engineering (DIEI) at the University of Cassino and Southern Lazio. His research focuses on evolutionary computation, machine learning, and their applications in neurodegenerative disease detection (e.g., Alzheimer’s and Parkinson’s), cultural heritage preservation, and environmental monitoring. He leads the AIDA Lab and has contributed to major initiatives like the HAND project for neuromuscular disease diagnosis and the C4E environmental monitoring system. Fontanella holds editorial roles in journals such as Pattern Recognition Letters , Genetic Programming and Evolvable Machines , and Applied Soft Computing . He has organized international workshops including the XVI International Workshop on Artificial Life and Evolutionary Computation (2022) and chairs the IEEE Computational Intelligence Society’s Task Force on Evolutionary Computer Vision. His teaching responsibilities include courses on computer science fundamentals, operating systems, and cybersecurity at both undergraduate and graduate levels. Notable projects include developing handwriting analysis systems for medical diagnostics and AI-driven tools for medieval manuscript analysis. Fontanella’s work bridges computational techniques with real-world challenges, emphasizing interdisciplinary collaboration between computer science and fields like healthcare, archaeology, and environmental science.

• Theoretical Physics
• Complex Systems
• Mathematical Physics
• +3 more

Andrea AUCONI is a Researcher in the Department of Molecular Sciences and Nanosystems (DSMN) at Ca' Foscari University of Venice, based at the Scientific Campus, via Torino. His academic position falls under the Scientific Disciplinary Sector PHYS-02/A: Theoretical Physics of Fundamental Interactions, Models, Mathematical Methods, and Applications. His research spans interdisciplinary domains including: Theoretical Physics Complex Systems Mathematical Physics Statistical Mechanics Network Theory Stochastic Processes Recent publications (2022-2024) demonstrate applications across social phenomena (election dynamics), financial systems (network uncertainty), biological processes (Bayesian chemotaxis), and fundamental information theory. This work consistently employs stochastic modeling and network analysis frameworks, published in high-impact journals like Scientific Reports and Europhysics Letters. For academic year 2024/2025, he teaches: Institutions of Mathematics with Exercises - 1 (9 credits) for Science and Technology for Cultural Heritage (Degree CT60) Institutions of Mathematics with Exercises - 1 (9 credits) for Chemistry and Sustainable Technologies (Degree CT7) No scientific awards or fellowships are documented in the available materials. His departmental affiliation with DSMN contextualizes his theoretical physics work within molecular and nanoscale science applications.

• Bayesian inference
• Machine learning
• Nonparametric methods
• +5 more

Francesca Panero is an Assistant Professor (RTT) in Statistics at the Department of Methods and Models for Economy, Territory, and Finance (MEMOTEF) at Sapienza University of Rome. She also serves as a Visiting Fellow at the Department of Statistics of the London School of Economics and Political Science, where she was previously an Assistant Professor until May 2024. Additionally, she maintains an affiliation with the Grantham Research Institute on Climate Change and the Environment at LSE. Dr. Panero earned her PhD in Statistics from the University of Oxford in 2022, following undergraduate and master's studies at the Department of Mathematics of the University of Turin and Collegio Carlo Alberto. Her academic journey reflects a strong foundation in mathematical and statistical theory applied to complex real-world problems. Her research centers on Bayesian statistical methodologies with applications across multiple domains. She specializes in complex networks analysis using Bayesian nonparametric approaches, disclosure risk assessment for privacy preservation, spatio-temporal Gaussian process modeling for food insecurity prediction, and fair machine learning techniques. Her work demonstrates a consistent thread of developing rigorous statistical methods with practical societal applications, particularly in network science, privacy protection, and algorithmic fairness. Dr. Panero's publications reveal a strong trajectory in high-impact statistical research, with recent work focusing on sparse network modeling, fairness constraints in machine learning, and privacy-preserving statistical techniques. Her methodological contributions bridge theoretical statistics with applications in social sciences, public policy, and computational challenges. 2025 Visiting Lecturer at Faculty of Informatics, University of the Italian Switzerland (USI) Elected as j-ISBA Chair Elect for 2025-2026 Recipient of IMS junior research travel grant for ICSDS 2024 Awarded LSE Research and Impact Support Fund (RISF) grant for food insecurity research As an educator, Dr. Panero teaches Probability and Stochastic Processes at Sapienza University and has previously instructed Deep Learning and Artificial Intelligence courses at LSE. She actively mentors students and encourages PhD candidates interested in her research areas to contact her. She is also a core member of GENIAL, an LSE focus group examining how students utilize generative AI tools for learning. Beyond her technical work, she has been engaged in equality, diversity, and inclusion initiatives since 2020 and maintains an active academic blog discussing data visualization, research projects, and academic life.

Andrea TancrediView profile

Professor

• Bayesian Inference
• Bayesian Computational Methods
• Biostatistics
• +9 more

Andrea Tancredi is a Professor at the Department of Methods and Models for Economy, Territory, and Finance, Sapienza University of Rome. His research focuses on Bayesian inference, computational statistics, and their applications in biostatistics, population size estimation, and health economics. He has coordinated multiple research projects funded by the University, including studies on record linkage, approximate Bayesian computation (ABC), and stochastic frontier models. Research Interests: Bayesian Inference Computational Methods (MCMC, ABC) Population Size Estimation Record Linkage and Data Integration Extreme Value Theory Biostatistics and Health Economics Teaching: He teaches courses such as Basic Statistics, Advanced Statistical Methods, and Extreme Value Theory at the Faculty of Economics and doctoral programs in Economics. Publications and Citations: With 47 publications (23 journal articles), his work spans Bayesian modeling for microbial diversity, climate-conflict risk forecasting, and multi-state health models. His research is cited 682 times on Google Scholar (h-index 12) and 401 times in Scopus (h-index 9).

• Process Safety
• Risk Management
• Occupational Safety
• +12 more

Micaela Demichela is a Full Professor in the Department of Applied Science and Technology (DISAT) at the Polytechnic University of Turin, where she conducts research and teaches in the fields of process safety, risk management, and occupational safety. She is a member of the Interdepartmental Center R3C – Responsible Risk Resilience Center and the Master's and Continuing Education School. She also serves as vice-coordinator of the MISO (Occupational Safety Engineering) Master's program at the Polytechnic University of Milan and is actively involved in the Doctoral College in Chemical Engineering and Metrology at Politecnico di Torino. Her research interests include process safety, probabilistic risk assessment, safety in the workplace, human and organizational factors in risk analysis, and resilience in industrial systems. She integrates chemical engineering principles with safety science to evaluate the safety status of production lines by combining kinetic and heat transfer data with plant characteristics. Her work spans both fine and basic chemicals sectors and extends to petrochemical industries and international research centers. The recent publications highlight a strong trend toward integrating data-driven methodologies, human factors, and advanced technologies such as human-robot collaboration, Bayesian networks, and prognostics and health management (PHM) to enhance safety and resilience in process industries. Her work increasingly focuses on cyber threats, energy resilience, and decision-making frameworks for industrial safety. Fellow, 3ASI - Association of Environmental, Reliability and Industrial Safety Analysts (2013–2015) President, ESReDA - European Safety, Reliability and Data Association (2012–2014) Associate Editor, Journal of Loss Prevention in the Process Industries (2007–) Associate Editor, Frontiers in Chemical Engineering (2019–) She has supervised numerous PhD students and leads multiple research projects, including ND - Resilience against Cyber Threats, CISC - Collaborative Intelligence for Safety Critical Systems, and several Erasmus+ initiatives such as SAFETY4VET and RE@WBC. She teaches courses such as 'Risk Assessment as Decision Making Tools', 'Advanced Technologies for Risk-Based Decision Making', and 'Occupational Safety Engineering' across various engineering programs. She leads the SAfeR research group and the Applied Biotechnology team at DISAT, and is involved in laboratories including the Biotechnological Laboratory.

Sabrina GiordanoView profile

Associate Professor

• Categorical Data Analysis
• Longitudinal Data Analysis
• Copula Functions
• +7 more

Sabrina Giordano is Associate Professor of Statistics at the University of Calabria , where she teaches Statistics and Data Science courses in both Italian and English. She holds a PhD in Methodological Statistics from the University of Milano-Bicocca and has held visiting positions at RWTH Aachen University, University of Florida, and University of Plymouth. Her research focuses on Statistical modeling of categorical and ordinal data Latent variable models and copula functions Longitudinal analysis and hidden Markov chains Applications in finance, health, and environmental statistics Her methodological contributions are implemented in the hmmm R-package and supported by grants like the PRIN2022 project "SMILE: Statistical Modelling and Inference to Live the Environment". She serves as Associate Editor for Biometrical Journal and Statistical Methods & Applications Director of the Post-Graduate Master in Artificial Intelligence & Data Science Local coordinator for the COST Fin-AI research group Her recent collaborative work explores Dynamic response styles in longitudinal data Fairness-aware classification algorithms Copula-based dependence structures Applications to financial vulnerability and risk perception

• Computational Neuroscience
• Theoretical Neuroscience
• Memory Systems
• +3 more

Misha Tsodyks is a Professor at the Weizmann Institute of Science in Rehovot, Israel, where he has held a faculty position since 1995 and achieved full professorship in 2005. Prior to this, he held research appointments in Moscow, Rome, Jerusalem, and San Diego. His educational background includes a Ph.D. in Theoretical Physics from the Landau Institute of Theoretical Physics in Moscow. Professor Tsodyks specializes in computational and theoretical neuroscience, with a primary focus on the mathematical foundations of memory systems. His research integrates advanced modeling techniques to explore memory formation, recall dynamics, and forgetting mechanisms. Key contributions include theoretical frameworks for power-law forgetting, retroactive interference, and hierarchical organization in memory representations. His work bridges cognitive psychology and neuroscience through rigorous mathematical analysis of neural information processing. Analysis of his recent publications (2017-2020) reveals a concentrated effort on developing fundamental laws of memory using computational approaches. His studies consistently employ attractor neural networks, Bayesian inference models, and synaptic plasticity mechanisms to explain complex memory phenomena across multiple scales. This research demonstrates strong interdisciplinary connections between theoretical physics, cognitive science, and neurobiology, with particular emphasis on hippocampal function and visual perception systems. He serves as Specialty Chief Editor for Frontiers in Computational Neuroscience, reflecting his leadership in the field. His research impact is evidenced by substantial publication views (over 180,000) and a robust professional network including 268 followers and collaborations with prominent neuroscientists.

Fabio PrevidiView profile

Professor

• Control Systems
• Mechatronics
• Aerospace Engineering
• +5 more

Fabio Previdi is a Full Professor in Control Systems at the Department of Management, Computer and Industrial Engineering, University of Bergamo. His career spans roles from Research Assistant at the University of Glasgow (1999-2000) to Associate Professor (2006-2017) before assuming his current position. He holds a PhD in Control Systems from Politecnico di Milano (1999), MSc in Electronic Engineering (1993) and Physics (2008) from Politecnico di Milano and University of Milan. Research focuses on control systems, mechatronics, and aerospace applications. Key areas include electromechanical actuators, fault detection, system identification, and biomedical engineering. Over 150 peer-reviewed papers highlight contributions to topics like robust control, machine learning for industrial systems, and pandemic modeling. Publications (2023-2020) address cutting-edge topics such as jamming detection filters for aircraft actuators, agent-based pandemic simulations, and kernel-based system identification. Awards: Not explicitly mentioned but notable contributions recognized through extensive publication record.

Pietro BelloniView profile

Researcher

• Epidemiology
• Medical Statistics
• Social Statistics
• +7 more

Pietro Belloni is a Researcher (RTDa) at the Department of Statistical Sciences, University of Padua, Italy. His primary role involves advancing statistical methodologies for drug safety and public health research through interdisciplinary projects like the Age-IT initiative. He holds a PhD in Statistics from the University of Padua, completed under Professors Giovanna Boccuzzo and Nicholas P. Tatonetti, with visiting research at Columbia University's Department of Biomedical Informatics. His academic journey includes a Master’s and Bachelor’s in Statistical Sciences from the University of Padua, complemented by an Erasmus exchange in Mathematics and Statistics at Århus University. Belloni’s research focuses on epidemiology, medical statistics, and social statistics, with particular emphasis on text mining, spatial epidemiology, and composite indicators for public health assessment. He actively contributes to research teams such as GLASS (a young Italian social statistics laboratory) and the Age-IT project addressing aging society challenges. Teaching responsibilities span epidemiology, medical statistics, and data analysis methods at both University of Padua and H-Farm College. His software contributions include R libraries like perla for mortality cluster analysis and MARMoT for statistical balancing techniques. Belloni’s work bridges theoretical statistics with practical applications in healthcare and social sciences, with publications in journals like Statistical Methods & Applications and Acta Otorhinolaryngol Italica , and book chapters on pharmacovigilance and text mining. His research has addressed topics ranging from cancer staging through NLP to pandemic sentiment analysis during the first wave of the COVID-19 crisis.

• Nuclear Fusion Engineering
• Plasma Physics
• Edge Plasma Modeling
• +3 more

Haosheng Wu is a Fixed-term Assistant Professor at Politecnico di Torino's Department of Energy (DENERG), affiliated with the College of Electrical and Energy Engineering. His research focuses on edge plasma modeling in fusion reactors, divertor physics, and computational plasma simulations using codes like SOLPS-ITER. He collaborates on teaching advanced master's courses including Nuclear Fusion Reactor Physics and Engineering. Wu leads the NEMO Research Group and supervises PhD student Matteo Maria Robaldo in Energetics. His work addresses critical challenges in fusion energy such as plasma detachment, heat load management, and impurity control in tokamaks like ASDEX Upgrade and J-TEXT. Recent studies include multi-machine benchmarking of edge transport models and SPARC divertor optimization. Teaching roles: Course collaborator in Nuclear Fusion Reactor Physics/Engineering since 2022 Research Group: Co-lead of DENERG's NEMO Group Key Projects: SOLPS-ITER validation, DTT facility design, SPARC divertor modeling Publications emphasize edge plasma dynamics, code comparison studies, and experimental data integration. Current research explores helium enrichment effects and Bayesian inference techniques for particle transport analysis. His contributions advance both fundamental plasma physics and applied fusion engineering solutions.

Igor PrünsterView profile

Professor

• Bayesian Nonparametrics
• Bayesian Asymptotics
• Foundations of Statistical Inference
• +7 more

Igor Prünster is a Professor of Statistics at Bocconi University, where he serves as Director of the Ph.D. Program in Statistics and Computer Science. He is a Fellow of the Bocconi Institute of Data Science & Analytics (BIDSA) and other distinguished societies. Bayesian Nonparametrics Bayesian Asymptotics Probabilistic Machine Learning Random Measures His editorial roles include Editor-in-Chief of Bayesian Analysis and Associate Editor of Annals of Statistics . He has mentored students who have won competitive awards, such as the Blackwell-Rosenbluth Award (2024) and Lawrence D. Brown Ph.D. Student Award (2024). Scientific Awards Fellow, American Statistical Association (2019) Fellow, International Society for Bayesian Analysis (2018) Fellow, Institute of Mathematical Statistics (2015)

Francesco Di RenzoView profile

Associate Professor

• Theoretical Physics
• Quantum Field Theory
• Lattice Gauge Theory
• +6 more

Francesco Di Renzo is Associate Professor of Theoretical Physics at the University of Parma's Department of Mathematical, Physical and Computer Sciences. Appointed faculty in 2001 and promoted to associate professor in 2016, he holds concurrent research appointments with INFN and coordinates the €4M EuroPLEx H2020 network. His academic background includes: Laurea (Master's equivalent) in Physics with honors, University of Parma PhD in Physics, University of Parma Di Renzo's research centers on lattice formulations of quantum field theories with emphasis on fundamental interactions. He pioneers computational approaches to critical phenomena using Lee-Yang zero analysis and thimble regularization to address sign problems in QCD. His work integrates high-performance computing architectures with emerging AI techniques for non-perturbative physics simulations. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Application of multipoint Padé approximants to locate QCD critical points through Lee-Yang edge singularities, (2) Development of Numerical Stochastic Perturbation Theory (NSPT) for O(N) sigma models at large N, and (3) Cross-disciplinary fusion of machine learning with lattice field theory for phase transition analysis. He currently supervises three PhD students and has served on the Doctoral Studies Committee since 2000. His grant leadership includes EuroPLEx (H2020 scientific coordinator), STRONGnet (FP7 Parma unit lead), and multiple PRIN projects where he coordinated research units. As an INFN-appointed researcher, he directs the QCDLAT initiative. Di Renzo actively contributes to international infrastructure through the International Lattice Data Grid (where he serves as Italian representative and former chairman) and the INFN SUMA supercomputing project. He previously led the AuroScience initiative and currently shapes UNIPR's high-performance computing strategy as committee member.

• Medical Image Analysis
• Video Surveillance Systems
• Pattern Recognition
• +3 more

Lorenzo Putzu is a Researcher (Assistant Professor) at the University of Cagliari's Department of Computer Science. He holds a PhD in Computer Science from the same institution (2016). His work focuses on pattern recognition techniques applied to medical imaging and video surveillance, including automated diagnosis support systems, security applications, and synthetic data generation for AI training. Since 2017, he has been part of the PRA Lab, contributing to projects like the IMMAGINA initiative for trustworthy AI in surveillance. Education: Bachelor's Degree in Computer Science (2010) Master's Degree in Computer Science (2012) PhD in Computer Science (2016) Research Interests: Medical image analysis for automated diagnosis (e.g., blood cell classification, histopathology) Video surveillance systems using AI (e.g., person re-identification, crowd counting) Synthetic data generation for improving model robustness Federated learning in healthcare applications Adversarial defense mechanisms in computer vision Key Projects: Development of MIRIAMS for multiple sclerosis segmentation Advancing leukocyte classification methods for hematological analysis Pioneering work on federated learning for cell nuclei segmentation Grant & Advising Activities: No explicit grants or advising roles listed Labs/Teams: Active contributor to the PRA Lab, collaborating on interdisciplinary AI projects in healthcare and security.

Giorgio FumeraView profile

Associate Professor

• Machine Learning
• Computer Vision
• Video Surveillance
• +3 more

Giorgio Fumera is an Associate Professor of Computer Engineering at the University of Cagliari. His research focuses on statistical pattern recognition, machine learning, and their applications in video surveillance, particularly in person re-identification, adversarial robustness, and uncertainty quantification. He has authored over 100 papers in leading journals and conferences, and serves as an associate editor for Pattern Recognition and Pattern Analysis and Applications . He is a member of IEEE and IAPR. His work explores cutting-edge topics such as synthetic data for surveillance systems, adversarial training defense mechanisms, and Bayesian inference for crowd counting. Recent contributions include studies on the robustness of machine learning models against adversarial attacks and the application of neural networks in microwave medical imaging. His research also emphasizes practical applications in cybersecurity, medical diagnostics, and ethical AI systems like the IMMAGINA project. Fumera’s publications span domains like ensemble methods, human-in-the-loop systems, and sparse learning. Despite extensive contributions, no specific awards or grants are explicitly mentioned in the provided materials. His research team actively collaborates on interdisciplinary projects, blending computer vision, medical imaging, and trustworthy AI principles.

Dirk HovyView profile

Associate Professor

• Natural Language Processing
• Computational Social Science
• Ethics and Fairness in AI
• +3 more

Dirk Hovy is an Associate Professor in the Department of Computing Sciences at Bocconi University. He serves as Scientific Director of BIDSA’s Data and Marketing Insights (DMI) research unit and heads the MilaNLP lab. His work focuses on natural language processing (NLP) and computational social science, with a particular interest in ethics and fairness in AI, large language models, transfer learning, and computational methods for social science. He has organized major conferences such as EMNLP 2017 and workshops on abusive language detection and computational social science. Education: PhD in Computer Science from the University of Southern California (USC), Master’s in Sociolinguistics from Germany. Research interests span NLP techniques like reinforcement learning and adversarial learning, alongside societal impacts of AI. His recent work explores sociodemographic variables in NLP models and their integration with demographic information. Notable contributions include geolocation via social media text, gender bias in NLP systems, and hate speech detection on Twitter. Labs/Teams: Leads MilaNLP and directs BIDSA’s DMI unit. Grants: Received a 2020 ERC Starting Grant to study sociodemographic variables and NLP models.