جست‌وجوی استاد و پژوهشگر

Pascal Frossardمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Graph Signal Processing
• +۶ مورد دیگر

Pascal Frossard is a Full Professor at the Department of Electrical Engineering in the School of Engineering (STI) at EPFL, with a courtesy appointment in the School of Computer and Communication Sciences. He founded and directs the LTS4 laboratory since 2003, co-leads the EPFL AI Center and Swiss Data Science Center, and serves as Associate Dean for Research at STI. Research Focus: Machine Learning, Graph Signal Processing, AI Applications in Healthcare, Computer Vision Academic Leadership: IEEE Fellow, ELLIS Fellow, Conference Chair roles Key Projects: Digital Pathology for Oncology, Cardiac Digital Twins, Robust Machine Learning Research Interests: His work bridges signal processing, machine learning, and applied mathematics, emphasizing biomedical applications. Recent research includes adversarial robustness in classifiers, network representation learning, and 360-degree video analysis. Scientific Awards: IEEE Fellow ELLIS Fellow Leadership in IEEE technical committees Advising & Grants: Supervised 20+ PhD students and postdocs. Secured major grants from PHRT, Hasler Foundation, FNS-Sinergia, Armasuisse, Google, and Cisco.

Kjell Jornerمشاهده پروفایل

استادیار

• Digital Chemistry
• Computational Chemistry
• Machine Learning
• +۵ مورد دیگر

Kjell Jorner is an Assistant Professor of Digital Chemistry in the Institute for Chemical and Bioengineering at ETH Zurich's Department of Chemistry and Applied Biosciences. His research group focuses on integrating computational methods and machine learning to address challenges in chemical synthesis, materials design, and reaction prediction. Education: PhD from Uppsala University (Photochemistry of aromatic compounds) Postdoctoral studies at AstraZeneca UK (Reaction prediction using computational chemistry and ML) Postdoctoral studies at University of Toronto (Molecular design of catalysts and organic electronic materials) Research Interests: Professor Jorner's work bridges computational chemistry, machine learning, and experimental design. Key areas include: Development of quantum mechanics-machine learning hybrid approaches for reaction feasibility prediction Inverse molecular design of functional materials (e.g., singlet-fission systems) Computational catalyst optimization and high-throughput screening methods Digital tools for chemical education and cheminformatics Publication Trends (2023-2025): Recent articles demonstrate a strong focus on machine learning applications in chemistry, including reaction prediction algorithms, catalyst design frameworks, and automated molecular generation. A recurring theme is the development of computational tools to accelerate materials discovery and optimize chemical processes. Laboratory & Team: Leads the Digital Chemistry research group at ETH Zurich (HCI E 137) exploring computational approaches to chemical challenges.

Johanna Ziegelمشاهده پروفایل

استاد

• Statistical Forecasting
• Probability Theory
• Financial Risk Management
• +۳ مورد دیگر

Johanna Ziegel is a Professor of Statistics at ETH Zurich, Switzerland, since 2024, and a Visiting Scientist at the Heidelberg Institute for Theoretical Studies (HITS). Previously, she held positions at the University of Bern, where she was promoted to Full Professor in 2023. Her research focuses on decision-theoretically sound methods for forecast evaluation, probabilistic forecasting, risk measures in finance, and applications in meteorology, medicine, and climate science. She is actively involved in editorial roles for journals like Bernoulli , JASA: Theory & Methods , and SIAM Journal on Financial Mathematics . Education: PhD in Stereological Analysis of Spatial Structures from ETH Zurich (2010), supervised by Paul Embrechts and Eva B. Vedel Jensen. Postdoctoral research at the University of Melbourne and Heidelberg University. Research Interests: Forecast evaluation, elicitable functionals, risk measures, isotonic regression, statistical calibration, and applications in finance, climate science, and biostatistics. Her work bridges theoretical statistics with practical challenges in uncertainty quantification and decision-making under uncertainty. Advising & Collaborations: Supervised 7 PhD students and mentored several postdocs. Collaborates with the Computational Statistics group at HITS and the Oeschger Centre for Climate Change Research. Her group explores distributional regression under order constraints and novel methods for forecast comparison. Recognition: Credit Suisse Award for Best Teaching (2022), H.I.T. Program for Academic Leadership (2021–2022). Active in professional service, including the Bernoulli Society Council and editorial boards.

Valentina Boevaمشاهده پروفایل

استادیار

• Machine Learning
• Bioinformatics
• Genomics
• +۳ مورد دیگر

Prof. Valentina Boeva is an Assistant Professor at the Department of Computer Science, ETH Zürich, specializing in biomedical informatics. Her research focuses on integrating machine learning and computational methods to address challenges in genomics, oncology, and precision medicine. She holds a position in the Professur für Biomedizininformatik (Biomedical Informatics) and is based at CAB G32.2, Universitätstrasse 6, Zürich, Switzerland. Her work emphasizes applications such as cancer biomarker discovery, tumor heterogeneity analysis, and epigenetic profiling. She teaches courses including Machine Learning Seminar, Data Science Lab, and Machine Learning for Genomics. Her research group develops computational tools like CDState and UniversalEPI to decode complex biological systems. She actively publishes in top-tier journals, with recent work on exosome-driven diagnostics and chromatin interaction modeling. Her scientific contributions span methodologies for single-cell data analysis, survival modeling, and drug response prediction. She collaborates across disciplines to bridge computational science with clinical applications in cancer research.

Niklas Lindeمشاهده پروفایل

استاد

• Geophysics
• Environmental Geophysics
• Probabilistic Inversion
• +۳ مورد دیگر

Niklas Linde is a full professor at the University of Lausanne's Faculty of Geosciences and Environment, leading the Department of Earth Sciences. He holds a PhD in Geophysics from Uppsala University (2005) and has held roles including Assistant Professor (2008), Associate Professor (2013), and Full Professor (2019). His research focuses on transforming geophysical signals into realistic hydrogeological models with rigorous uncertainty quantification. Key areas include probabilistic inversion, Bayesian methods, and geostatistical modeling applied to environmental and subsurface processes. Education: PhD in Geophysics (Uppsala University, 2005), postdoctoral positions at Lawrence Berkeley National Lab (USA), CNRS-CEREGE (France), and ETH Zurich (Switzerland). He joined UNIL in 2008 as an Assistant Professor in Environmental Geophysics. Research interests span geophysical inversion techniques, subsurface heterogeneity characterization, and the integration of geophysical and hydrological data. Current projects emphasize Bayesian approaches for model selection and rare event estimation, supported by grants from the European Commission and Swiss National Science Foundation. Collaborations involve international teams addressing challenges in hydrogeology, rock fracture dynamics, and 4D hydrogeology. Publications reflect advancements in inverse problem solving, stochastic simulation, and machine learning applications. His work bridges theory and practice, with field studies in alpine environments, fractured media, and environmental monitoring. Students under his supervision have explored topics like deep generative networks and Bayesian hydrogeological inversion. Advising: Supervised over a dozen PhD students, including recent works on variational Bayesian methods and geophysical data fusion. Grants include projects on uncertainty quantification and experimental design. Active in scientific societies and editorial roles, contributing to methodological advancements in Earth sciences.

Marc Paolellaمشاهده پروفایل

استاد

• Financial Econometrics
• Time Series Analysis
• Portfolio Optimization
• +۴ مورد دیگر

Marc Paolella is a Full Professor of Empirical Finance at the University of Zurich's Department of Finance. His academic journey includes roles as Associate Professor (2003–2006) and Assistant Professor (1999–2003) at Kiel University and visiting positions at the University of Geneva. He holds a Dr. sc. pol. (1998) and Habilitation (2002) from Kiel University, alongside degrees from SUNY Stony Brook and Colorado State University. His research focuses on financial econometrics, emphasizing portfolio optimization, risk prediction, and saddlepoint approximation theory. He has authored four textbooks, including works on probability theory, statistical inference, and GARCH models. Over 50 journal articles span leading venues like the Journal of Econometrics and Journal of Financial Econometrics. Paolella's contributions include developing methods like CHICAGO for portfolio risk calculation and the COMFORT model for non-Gaussian returns. He serves as Editor-in-Chief of the journal Econometrics and holds editorial roles at multiple journals. Awards include the Elected Fellowship at the IETI and recognition for impactful research. His teaching spans advanced courses in financial econometrics, probability theory, and mathematical finance. He has supervised numerous theses and leads the Quantitative Finance program at the Science ETH UZH. Research grants include Swiss National Science Foundation funding for portfolio optimization under non-elliptical distributions.

• Financial Econometrics
• Volatility Modeling
• Time Series Analysis
• +۴ مورد دیگر

Andrew J. Patton is a Professor in the Department of Economics at Duke University's Trinity College of Arts & Sciences, with a secondary affiliation at Singapore Management University's School of Economics. His office is located at 213 Social Sciences Building, Box 90097, Durham, NC 27708-0204, United States, and his professional website is available at http://econ.duke.edu/~ap172/. Dr. Patton's research focuses on financial econometrics, with particular expertise in volatility modeling, high-frequency data analysis, and dependence structures in financial markets. His work spans several key areas including realized variance methods, copula modeling for financial time series, risk management techniques, and forecast evaluation methodologies. He has made significant contributions to understanding market microstructure, hedge fund risk exposures, and the dynamics of financial correlations. His publication record demonstrates consistent output in top finance and econometrics journals, with recent work exploring machine learning applications in volatility forecasting, granular risk measures, and the impact of high-frequency data on asset pricing models. The research shows a clear trajectory toward increasingly sophisticated modeling of financial dependencies and risk structures. Dr. Patton has received recognition for his scholarly contributions, ranking among SSRN's top 1,427 authors by total paper downloads and top 1,090 by total paper citations. His work is frequently cited in the field of financial econometrics, indicating substantial influence on contemporary research. He maintains active research collaborations with leading economists including Tim Bollerslev, Kevin Sheppard, Tarun Ramadorai, and Robert Engle, among others. These collaborations span multiple institutions and have produced influential work on volatility modeling, risk measurement, and financial market dynamics.

Ben Jannمشاهده پروفایل

استاد

• Statistical Methods
• Data Visualization
• Social Inequality
• +۲ مورد دیگر

Ben Jann is a Professor and current Department Head at the University of Bern in the Department of Social Sciences . His methodological expertise spans statistical software development, decomposition analysis, and data visualization. Role: Department Head Institution: University of Bern Department: Department of Social Sciences Research Interests : Jann specializes in creating Stata modules for advanced statistical analysis, including geospatial mapping (geoplot), robust regression (robreg10), and inequality decomposition (oaxaca-blinder methods). His substantive work examines social inequality through economic and sociological lenses, analyzing how living costs and taxation systems impact income distribution in Switzerland. He also investigates educational transitions and gender disparities in STEM occupational choices through longitudinal studies like TREE2. Recent Publications Trends : Jann’s 15 most recent articles (2025-2023) demonstrate his focus on statistical software development for social science applications. Key contributions include methods for marginal odds ratios (2023), two-level model variance decomposition (2025), and list experiment analysis (2025). His empirical work connects statistical methodology to pressing social issues like gender wage gaps (2021) and teacher content knowledge in developing countries (2021).

Davide La Vecchiaمشاهده پروفایل

استاد

• Time series analysis
• Robust and semiparametric inference
• Financial econometrics
• +۳ مورد دیگر

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 Spinnerمشاهده پروفایل

مدرس

• Medical Image Analysis
• Bayesian Networks
• Computational Biology
• +۳ مورد دیگر

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.

Renato Pajarolaمشاهده پروفایل

استاد

• Computer Graphics
• Scientific Visualization
• 3D Modeling
• +۵ مورد دیگر

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
• +۳ مورد دیگر

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 Tavakoliمشاهده پروفایل

مدرس ارشد

• Statistics
• Functional Data Analysis
• Neuro-imaging
• +۴ مورد دیگر

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.

• Multiresolution Methods
• Numerical Analysis
• Computational Mathematics
• +۴ مورد دیگر

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
• +۵ مورد دیگر

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.