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Davide La VecchiaView profile

Professor

• Time series analysis
• Robust and semiparametric inference
• Financial econometrics
• +3 more

Davide La Vecchia is a Full Professor at the Research Institute for Statistics and Information Science (RISIS) at the University of Geneva, Switzerland. He holds dual PhDs in Statistics from Bocconi University (2007) and Economics from Università della Svizzera italiana (2011). His academic journey includes roles as an Assistant Professor at the University of St. Gallen and Monash University, and he has held visiting positions at Princeton University, the University of Copenhagen, and CREST (Paris). His expertise spans time series analysis, robust and semiparametric inference, financial econometrics, and spatial statistics. Key research contributions include work on saddlepoint approximations, optimal transportation methods, and latent variable models. Davide has led multiple grants, including from the Swiss National Science Foundation and the Australian Research Council. He serves as a referee for top-tier journals in statistics and econometrics. Teaching focuses on advanced statistical methods, including probability theory, time series analysis, and multivariate inference. His recent work emphasizes high-dimensional data modeling, spatio-temporal factor models, and applications to commodities trading networks. He has been honored with editorial roles and speaker invitations at global conferences, reflecting his leadership in statistical methodology.

Georg SpinnerView profile

Lecturer

• Medical Image Analysis
• Bayesian Networks
• Computational Biology
• +3 more

Georg Spinner is a Lecturer and Head of the Research Group for Medical Image Analysis & Data Modelling at the Institute of Computational Life Sciences, Zurich University of Applied Sciences (ZHAW). His work integrates computational methods with biomedical research, focusing on stroke management, intracranial aneurysm risk modeling, and quantitative medical imaging. Primary affiliation: Zurich University of Applied Sciences Research focus: Bayesian networks, medical imaging, computational epidemiology Projects: GEMINI (digital twins for stroke), Stroke DynamiX (causal networks in stroke care), IVIM muscle activation studies Using Bayesian networks and advanced imaging techniques like IVIM DWI, his research aims to develop data-driven decision support systems for neurovascular diseases. He has contributed to modeling stroke health data and intracranial aneurysm risk stratification through international multicenter collaborations. His work spans computational biology, neuroinformatics, and digital health, with publications in journals like Medical Image Analysis and conferences including the International Conference on Computational and Mathematical Biomedical Engineering. Key methodologies include causal inference, dynamic disease modeling, and quantitative image analysis.

Markus HuggenbergerView profile

Assistant Professor

• Quantitative Risk Management
• Insurance
• Asset Pricing
• +3 more

Markus Huggenberger is an Assistant Professor of Risk Management and Insurance at the University of St. Gallen (HSG). Previously, he served as an Assistant Professor at the University of Mannheim, where he also earned his doctoral degree. He holds diplomas in Business Administration and Mathematics. His research focuses on quantitative risk management, insurance, and asset pricing, particularly exploring techniques for managing extreme event risks, risk sharing mechanisms in insurance, and solvency regulation design. He has published in leading journals such as the Journal of Risk and Insurance , Journal of Financial Economics , and Insurance: Mathematics and Economics . Notable contributions include analyzing solvency regulation from a policyholder perspective and examining multivariate crash risk dynamics. Awards: NAAJ Outstanding Reviewer of 2024 Executive Programs: Executive Master Insurance & Financial Services, Insurance Management Diploma Program Professional Affiliations: Involved in events like the IVW Jahrestagung 2025 and research stays at institutions like UNSW Sydney.

Renato PajarolaView profile

Professor

• Computer Graphics
• Scientific Visualization
• 3D Modeling
• +5 more

Prof. Dr. Renato Pajarola is the Head of the Visualization and MultiMedia Lab at the Department of Informatics, University of Zurich. His research focuses on computer graphics, scientific visualization, and geometric processing, with applications in 3D scanning, point cloud analysis, and real-time rendering. He leads a team developing advanced visualization techniques for high-dimensional data, parallel rendering frameworks, and interactive systems for complex datasets. Key research areas include: 3D reconstruction of indoor environments Tensor approximation for volume visualization Interactive ray tracing and point cloud processing Parallel rendering frameworks (e.g., Equalizer) Scientific computing and sensitivity analysis His recent publications emphasize: High-dimensional data exploration using tensor methods Efficient rendering techniques for large-scale point clouds Integration of citizen-reported weather data for environmental analysis Prof. Pajarola’s lab collaborates on projects like VIAN (visual annotation tool for film analysis) and Terrender (web-based terrain visualization). His Erdős number is 3, reflecting interdisciplinary research connections in mathematics and computer science.

• Quantitative Finance
• Portfolio Optimization
• Covariance Matrix Estimation
• +3 more

Gianluca De Nard serves as a Senior Research Associate at the University of Zurich's Department of Economics, Research Fellow at New York University's Volatility and Risk Institute, and Head of Quantitative Research at OLZ AG in Zurich. His career bridges academic research and practical finance applications, with significant contributions across multiple institutions. Dr. De Nard specializes in quantitative finance with expertise in portfolio optimization, covariance matrix estimation, and financial econometrics. His research focuses on developing robust statistical methods for handling large-dimensional financial data, with particular emphasis on shrinkage techniques and factor models. His work addresses fundamental challenges in modern portfolio theory, including nonstandard errors in financial research, climate risk integration, and AI applications in investment management. Analysis of his recent publications reveals a clear evolution toward integrating machine learning with traditional financial econometrics. His 2024-2025 work shows increasing sophistication in applying AI techniques to portfolio construction while maintaining rigorous statistical foundations. The recurring themes across his research include improving covariance matrix estimation, developing factor models for portfolio selection, and addressing methodological challenges in financial research. Dr. De Nard has established collaborations with leading researchers including Nobel laureate Robert F. Engle, Olivier Ledoit, and Michael Wolf. His work appears in top finance journals including the Journal of Finance, Journal of Financial Econometrics, and Journal of Banking and Finance, with his 2024 paper 'Nonstandard Errors' accumulating over 17,000 downloads. At OLZ AG, he applies his academic research to practical investment challenges as Head of Quantitative Research, demonstrating his ability to translate theoretical advances into real-world solutions. His dual academic-industry roles position him at the forefront of quantitative finance research and application.

Shahin TavakoliView profile

Senior Lecturer

• Statistics
• Functional Data Analysis
• Neuro-imaging
• +4 more

Shahin Tavakoli is a Senior Lecturer in the Research Institute for Statistics and Information Science at the Geneva School of Economics and Management (University of Geneva). He holds a PhD in Mathematical Statistics from EPFL and has held positions as a University Research Fellow at the University of Cambridge and a tenure-track Assistant Professor at the University of Warwick. His research focuses on functional data analysis with applications in neuroimaging, phonetics, biophysics, econometrics, and genomics. Education: BSc/MSc in Mathematics (EPFL), PhD in Mathematical Statistics (EPFL). Key roles include Associate Editor for the Journal of Statistical Planning and Inference and proposer for a JRSS B discussion paper. Teaching includes Applied Bayesian Statistics, Multivariate Analysis, and Mathematics courses at the University of Geneva. Research interests emphasize statistical methodologies for complex data structures, including high-dimensional functional time series and spatial modeling of linguistic data. Notable recent publications address phonetic analysis, brain imaging, and econometric factor models. Collaborations span institutions like the University of Cambridge, University of Warwick, and LMU Munich. Advising includes PhD students Marco Palma and Beatrice Matteo, with contributions to projects such as functional regression clustering and normative brain mapping. His work bridges theoretical statistics with applied domains, reflecting interdisciplinary impact across natural and social sciences.

Hoda AllahbakhshiView profile

Researcher

• GIScience
• Spatio-temporal data analysis
• Sensor-based human mobility analysis

Hoda Allahbakhshi is a Researcher at the Digital Society Initiative of the University of Zurich , affiliated with the Faculty of Science and Department of Geography . Her work focuses on GIScience , spatio-temporal data analysis , and sensor-based human mobility analysis , particularly addressing accessibility challenges for mobility-restricted individuals. Research Themes include: Development of spatial accessibility analytics frameworks Integration of multi-sensor tracking data (GPS, accelerometers) Age-specific physical activity detection modeling Participatory approaches to urban accessibility mapping Her scientific contributions (2017-2024) analyze mobility patterns through big data and urban infrastructure interactions , with recent emphasis on microscale street elements and inclusive urban environments . Current affiliations: Department of Geography , Digital Society Initiative (University of Zurich).

• Multiresolution Methods
• Numerical Analysis
• Computational Mathematics
• +4 more

Michael Multerer is an Associate Professor at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on multiresolution methods, scattered data analysis, and numerical analysis with applications in computational mathematics and engineering. He leads projects such as the SNSF Starting Grant on multiresolution methods for unstructured data, emphasizing nonlinear approximation and kernel-based techniques. Research Interests: Development of fully discrete multiresolution methods for unstructured data Wavelet theory and kernel matrix algebra Uncertainty quantification in partial differential equations Scattered data compression and approximation Key Software Contributions: FMCA: Fast multiresolution covariance analysis for scattered data Bembel: Boundary element library for solving Laplace and Helmholtz equations SPQR: Anisotropic sparse grid quadrature in MATLAB Funding: Holder of the SNSF Starting Grant (2025) for advancing multiresolution techniques in unstructured data processing. Labs/Teams: Active in the research group at USI’s Faculty of Informatics, collaborating with institutions like TU Darmstadt and University of Basel on numerical methods and engineering applications.

• Psychology
• Neuroscience
• Developmental Psychology
• +5 more

Andjela Markovic is a Lecturer and Research Associate at the Department of Psychology, University of Fribourg. Her work focuses on sleep neurophysiology, particularly in adolescents and children, with a strong emphasis on EEG markers, mental health, and developmental trajectories. She leads research on topics such as sleep spindles, thalamic volume, and circadian rhythms, often involving longitudinal studies and collaborations with clinical teams. Her recent work includes the COVI-GAPP study, investigating physiological responses to SARS-CoV-2 infections using wearable technology, and projects on the genetic basis of sleep homeostasis in adolescence. Her research spans interdisciplinary areas, including the interplay between social behavior, diet, and sleep in children, and the impact of confinement on pediatric sleep during the pandemic. Markovic is affiliated with multiple research groups within the Department of Psychology, including Clinical Psychology and Cognitive Neuroscience. She contributes to teaching activities in the Faculty of Humanities and maintains an ORCID profile (0000-0003-2104-9543). Key themes in her publications include adolescent mental health (e.g., major depressive disorder, schizophrenia), sleep neurophysiology biomarkers, and the developmental origins of sleep patterns. Her methodological expertise includes EEG analysis, Bayesian inference, and genetic association studies.

Andrea CiniView profile

Researcher

• Machine Learning
• Graph Deep Learning
• Time Series Forecasting
• +3 more

Andrea Cini is a postdoc researcher affiliated with the Graph Machine Learning Group and the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) at the University of Lugano (USI). He also holds a position as a SNSF postdoc fellow at the University of Oxford under Prof. Michael Bronstein, focusing on machine learning for time series forecasting and graph processing. His research integrates graph deep learning methodologies with spatiotemporal dynamics, emphasizing applications in healthcare, energy systems, and intelligent systems. Education: PhD in Computer Science and Engineering (USI, 2020), supervised by Prof. Cesare Alippi MSc and BSc in Computer Science and Engineering (Politecnico di Milano) Visiting researcher at Imperial College London (Prof. Danilo Mandic) Research interests span graph neural networks , time series forecasting , and spatiotemporal data processing . His work has introduced influential methods such as the Torch Spatiotemporal library, and has been recognized with a best paper award. Recent publications emphasize applications in relational conformal prediction, hierarchical forecasting, and energy grid optimization. Awards include the Best Paper Award for contributions to graph-based forecasting methodologies. Current projects are funded by the Swiss National Science Foundation, exploring graph-based reinforcement learning and spatiotemporal modeling at the University of Oxford. Collaborations include affiliations with the Northernmost Graph Machine Learning group at UiT the Arctic University of Norway. His research bridges theoretical advancements and industrial applications in fields like healthcare dynamics prediction and smart grid optimization.

• Social Network Analysis
• Organizational Networks
• Network Dynamics
• +2 more

Alessandro Lomi is Full Professor in the Faculty of Economics at the University of Italian Switzerland (USI), where he conducts research on organizational networks and social dynamics. As Life Member of Clare Hall College, University of Cambridge, his expertise spans social network modeling, interorganizational relations, and computational approaches to organizational behavior. His methodological contributions include: Developing relational event models for network dynamics Analyzing multilevel organizational networks Studying temporal patterns in interhospital patient transfers Examining knowledge sharing in organizational contexts Recent publications investigate role switching in financial markets, goodness-of-fit frameworks for network models, and microstructural approaches to self-organization.

• Financial Economics
• Portfolio Optimization
• Stochastic Dominance
• +5 more

Professor Wing-Keung Wong is a distinguished academic at the Department of Finance, Asia University . With over 187 scholarly papers and 638 citations, his work spans critical areas in financial economics and quantitative finance. Current affiliation: Asia University, Department of Finance Past affiliations: National University of Singapore, Chinese University of Hong Kong, Erasmus University Rotterdam Research Themes include: Portfolio optimization and stochastic dominance theory Market efficiency analysis across diverse financial instruments Behavioral finance and investor decision-making models Risk measurement with VAR and CVaR frameworks International financial market integration studies Quantitative trading system development Key Article Trends reveal consistent focus on empirical finance, mathematical modeling, and decision science applications in portfolio management and market anomalies.

• Machine Learning in Biomedicine
• Statistical Genetics
• Systems Genetics
• +5 more

Dr. Francesco Paolo Casale serves as Principal Investigator in Machine Learning in Biomedicine at the Helmholtz Munich Institute AI for Health, part of Helmholtz Zentrum München and affiliated with Ludwig-Maximilians-Universität München's Biomedical Center. His research develops machine learning and statistical tools to analyze genetic cohorts with deep molecular and phenotypic data, addressing fundamental biomedical questions about disease mechanisms and progression. His academic foundation includes: PhD in Statistical Genetics from University of Cambridge & EMBL-EBI (2012-2016) M.Sc. in Physics of Complex Systems from Università di Napoli Federico II (2009-2012) B.Sc. in Physics from Università di Napoli Federico II (2009) Casale's research integrates machine learning, statistical inference, and systems genetics to develop scalable tools for genetic association studies, deep learning models for imaging genetics, and computational methods examining gene-environment interactions. His work emphasizes model robustness and interpretability while investigating molecular and cellular traits associated with disease severity. Current projects focus on rare variant analysis, aberrant gene expression prediction, and longitudinal omics data integration. His publication record shows a clear progression from foundational statistical genetics methods toward increasingly sophisticated integration of machine learning with multi-omics data. Recent work emphasizes practical biomedical applications including disease risk prediction through Mendelian randomization frameworks, advanced single-cell analysis techniques, and histopathology image classification. The research demonstrates consistent methodology development focused on scalability for large datasets while maintaining biological interpretability. Key recognitions include: Highly Recognized article in PloS Genetics Research Prize (2018) Microsoft Research New England Postdoctoral Fellowship (2017) EMBL studentship (2012) Honors for MSc and BSc degrees from Università di Napoli Federico II Throughout his career at Microsoft Research, Insitro, and Helmholtz Munich, Casale has led research teams developing computational approaches at the intersection of human genetics and machine learning. His work contributes to landmark projects including the 1000 Genomes Project and Blueprint initiative, with conference presentations at major venues including NeurIPS, ASHG, and EASL. Current grant support likely stems from Helmholtz Association funding mechanisms and collaborative biomedical research programs. He directs the Systems Genetics and Machine Learning Research team at Helmholtz Munich, which operates within the Biomedical Center ecosystem of LMU Munich. The team focuses on leveraging large-scale genetic datasets with machine learning to understand disease biology, with particular emphasis on target identification and characterization for therapeutic development. Current research directions include multi-timepoint omics analysis, disease subtyping, and developing interpretable models for clinical translation.

Tommaso DorigoView profile

Professor

• Particle Physics
• High Energy Physics
• Statistics
• +4 more

Tommaso Dorigo is a Professor at the University of Padova, affiliated with the Department of Physics and Astronomy 'G. Galilei'. He serves as First Researcher at INFN-Padova and contributes to international collaborations including CMS, CDF, MODE, DarkMachines, and SWGO. His roles span research, teaching, and leadership in interdisciplinary initiatives. 2022-2025: President of USERN Organization 2020-Present: Coordinator of MODE Collaboration 2014-2019: Scientific Coordinator of AMVA4NewPhysics ETN 2016-2020: Board member of INFN CSN1 Research Interests: His work bridges Particle Physics (Higgs boson physics, top quark studies, new physics searches) and Statistics (parameter estimation, frequentist methods, nuisance parameters). In Computer Science , he develops machine learning tools for detector optimization, anomaly detection, and differentiable programming. Recent articles focus on end-to-end detector design, extended Higgs models, and systematic uncertainty-aware neural networks. Scientific Contributions: He has authored over 1600 publications (H-index 223) and pioneered algorithms like MuScleFit, INVERSE BAGGING, and INFERNO. His outreach includes a popular science blog with 15M+ visits and books like Anomaly! Collider Physics and the Quest for New Phenomena at Fermilab . Grants: Marie Curie ITN grants (AMVA4NewPhysics 2015; INSIGHTS 2017) Editorial: Editor for Elsevier journals Reviews in Physics and Physics Open Advising: Supervised 44 undergraduate/masters students and 11 PhD students Labs/Teams: Member of CMS experiment (CERN), former CDF (Fermilab), and leader of MODE collaboration

• Neuroscience
• Psychiatry
• Cognitive Neuroscience
• +3 more

Thomas Koenig is a Group Leader at the University of Bern's Translational Research Center and a Visiting Professor at the University of Geneva's Campus Biotech. His primary affiliation is the University of Bern, where he contributes to the Medical Faculty's Department of Psychiatry. Koenig's research focuses on EEG microstate analysis, particularly in understanding brain dynamics during health and psychiatric disorders. His work integrates neurophysiology with philosophical inquiries into the mind-brain relationship, including a Master's in Philosophy of Mind. Key contributions include developing the MICROSTATELAB toolbox and advancing microstate methodology. His research spans schizophrenia, Alzheimer's, sleep neurophysiology, and the neural correlates of self-experience. Education: Holds a Master in Philosophy (emphasis on Philosophy of Mind) alongside his natural sciences background. His academic background bridges neuroscience and philosophy, enhancing interdisciplinary research. Research interests include EEG/ERP microstates, resting-state networks, neuroimaging biomarkers, and translational applications in psychiatry. His articles emphasize EEG methodology, clinical applications, and cognitive neuroscience. Collaborations span institutions globally, addressing topics like psychosis biomarkers, sleep modulation, and neurodegenerative markers. Notable contributions include identifying EEG microstate D as a psychosis correlate and investigating sleep's functional networks. His work on closed-loop sleep stimulation highlights translational potential for memory and neurodegenerative interventions.