جست‌وجوی استاد و پژوهشگر

Andrew Woodمشاهده پروفایل

استاد

• Non-Euclidean Statistics
• Directional Statistics
• Statistical Shape Analysis
• +۵ مورد دیگر

Professor Andrew Wood is a faculty member at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research spans non-Euclidean statistics, theoretical statistics, computational methods, and applied statistics in sciences and medicine. Research Interests : Non-Euclidean statistics, directional statistics, statistical shape analysis, asymptotic theory, computational statistics, stochastic differential equations, and applications in science/medicine. Grants : Funded by Engineering and Physical Sciences Research Council (UK), Biotechnology and Biological Sciences Research Council (UK), and Australian Research Council Discovery Projects. Editorial Roles : Former Joint Editor of Journal of the Royal Statistical Society, Series B and current Associate Editor of Biometrika . Research Trends : Recent publications focus on robust statistical methods for non-Euclidean data, computational approaches for complex distributions, principal component analysis for high-dimensional datasets, and geometric inference on manifolds. Applications include microbiome analysis, surface fractal dimension estimation, and spherical regression models. Supervision & Collaboration : Registered as a supervisor at ANU, with collaborations across disciplines including molecular biology, environmental science, and computational mathematics.

• Computational Statistics
• Nonparametric Regression
• Survival Analysis
• +۴ مورد دیگر

Nora M. Villanueva is an Assistant Professor at the Department of Statistics and Operations Research, University of Vigo. She works at the intersection of computational statistics, nonparametric regression, and survival analysis, with applications to cybersecurity and industry 4.0. Her academic journey includes a PhD in Statistics and Operations Research from the University of Vigo (2021) and prior roles as a Data Scientist at Optare Solutions and Ecomanagement Technology. Education: PhD in Statistics and Operations Research (2021), University of Vigo Her research focuses on predictive modeling, nonparametric curve estimation, and software development for statistical analysis. She has contributed to the creation of four R packages and participated in 17 research projects and 39 conferences. Her most recent publications explore group determination in survival curves, explainable AI methods, and genomic sequence analysis. Her work combines theoretical statistics with practical applications in telecommunications, energy, and biomedical domains. Scientific Awards: Research Award (Science and Technology), Pontevedra Provincial Council (Spain) Premio a la mejor solución del reto IN1: 'Clasificación de ataques a honeypots SSH' Villanueva has extensive teaching experience at the University of Vigo, covering courses in statistics, regression models, and data analytics. She also contributed to industry workshops on predictive modeling and R package development.

Kirsten Schorningمشاهده پروفایل

استادیار

• Design of experiments
• Biostatistics
• Model selection
• +۳ مورد دیگر

Kirsten Schorning is a Junior Professor of Mathematical Statistics at the Department of Statistics, Faculty of Statistics, Technical University of Dortmund. She maintains her office in the Mathematics Building, Room 725, and leads a research group focused on statistical methodology development with applications in biostatistics and toxicology. Education: Bachelor's and Master's degree in Mathematics, Ruhr-University Bochum (2007-2012) Doctorate in Mathematics (Dr. rer. nat.), Ruhr-University Bochum (2012-2016) Dr. Schorning's research primarily centers on Design of Experiments , Biostatistics , and Model Selection and Averaging . Her work develops innovative statistical methodologies for optimal experimental design, particularly in drug combination studies, toxicological assessments, and dose-response modeling. She has made significant contributions to addressing day-to-day variability in toxicity assessments and developing optimal concentration designs for in vitro experiments. Analysis of her recent publications (2019-2025) reveals a strong focus on optimal experimental design methodologies with applications in pharmaceutical research and toxicology. Her work bridges theoretical statistics with practical applications, particularly in determining effective drug doses, analyzing mixture toxicity, and developing robust statistical approaches for concentration-response relationships. A notable trend is her increasing focus on addressing real-world complexities such as day-to-day variability in toxicity assessments and developing guidance frameworks for toxicological dose-response experiments. Dr. Schorning leads a research group at TU Dortmund that includes team members Sabine Bell (secretary), Pauline Baur, Julia Eichhorn, Justin Lamberti, and Onur Gül. Her research has significant implications for pharmaceutical development, environmental toxicology, and clinical trial design, providing statisticians and researchers with improved methodological tools for experimental planning and analysis.

• Cosmology
• Quantum Field Theory
• String Theory
• +۴ مورد دیگر

Enrico Pajer is a Lecturer at the University of Cambridge , affiliated with the Department of Applied Mathematics and Theoretical Physics and part of the General Relativity research group . His academic career includes previous roles as Assistant Professor at Utrecht University, Research Associate at Princeton University, and Postdoc at Cornell and Princeton. Education : PhD from Ludwig Maximilian University of Munich (2005-2008), Bachelor and Master from Scuola Normale Superiore and University of Pisa (2000-2005) Research Interests span theoretical cosmology, quantum field theory, and string theory. Key areas include: Inflationary cosmology Large-scale structure formation Cosmic microwave background Quantum field theory in curved spacetime String theory applications General relativity Publications focus on topics like inflationary unitarity bounds, open effective field theories, cosmological cutting rules, and graviton interactions, with a strong emphasis on quantum gravity and early universe dynamics. Contact : Email: enrico.pajer@gmail.com Room: B1.02, University of Cambridge Phone: 01223 766918 Personal Homepage: http://www.damtp.cam.ac.uk/user/ep551/index.html

Matúš Maciakمشاهده پروفایل

دانشیار

• nonparametric regression
• change-points
• structural breaks
• +۵ مورد دیگر

Matúš Maciak is an Associate Professor in the Department of Probability and Mathematical Statistics at the Faculty of Mathematics and Physics, Charles University . His research spans nonparametric regression , change-point detection , and statistical modeling in financial and biological contexts . Key research areas: Nonparametric smoothing and robustness Structural break analysis Shape-constrained inference Sparsity and LASSO-based methods Bootstrapping dependent data Recent publications focus on exogenous market changes , longitudinal data modeling , and biological applications like diel movement patterns in fish. Technical reports include contributions to ecological assessment methods and intercalibration processes for water quality. Contact: Email: Matus.Maciak@mff.cuni.cz , maciak@karlin.mff.cuni.cz Office: K151, Sokolovská 83, Prague 8

• Enumerative Geometry
• Quantum Algebra
• Quantum Topology
• +۷ مورد دیگر

Professor Gaëtan Borot is a faculty member at the Institute of Mathematics, Faculty of Mathematics and Natural Sciences, Humboldt-Universität zu Berlin. He leads a research group in Mathematical Physics with connections to quantum field theory, string theory, and geometric analysis. His work is supported by multiple Research Training Groups including Rethinking QFT (2019-2029), From geometry to numbers (2024-2029), and the College of Mathematics and Physics Berlin (2020-2029). Professor Borot's research centers around topological recursion and its applications across mathematical physics. His work connects enumerative geometry, quantum algebra, moduli spaces, and random matrix theory through a unifying framework that reveals deep connections between seemingly disparate fields. He investigates how topological recursion provides methods to study quantization of geometric objects and field theories, with applications ranging from Gromov-Witten theory to statistical physics models on random surfaces. Analysis of his recent publications (2021-2024) shows a strong focus on the mathematical structures underlying conformal field theories, particularly Liouville CFT, where he explores Virasoro representations, scattering matrices, and equations of motion. His work on topological recursion continues to expand into new domains including cohomological field theories, Hurwitz theory, and spectral curves, demonstrating the versatility of this algebraic structure across mathematical physics. Professor Borot actively supervises PhD students including Giacomo Umer (who successfully defended in May 2025), Davide Scazzuso, and Niklas Martensen, along with numerous master's and bachelor's students. He organizes the Mathematical Physics Seminar held weekly in Adlershof and has taught courses on integrable systems, random matrix theory, and differential geometry. Despite currently being on leave until September 2025, he continues to supervise thesis projects with availability starting October 2025.

• Bayesian Statistics
• Biostatistics
• Epidemiology
• +۵ مورد دیگر

Stefano Cabras is a Full Professor in the Department of Statistics at University Carlos III de Madrid (UC3M), where he serves as Director of the Flores de Lemus Institute and Deputy Director of the Master's Degree in Statistics for Data Science. His office is located at 10.1.10 - Campomanes (Getafe). Professor Cabras maintains broad interdisciplinary research interests spanning: Bayesian statistics and computational methods Biostatistical applications in nutrition, epidemiology, and medicine Causal inference and policy impact evaluation Machine learning applications in transportation and sports science Statistical modeling of complex biological systems His publication trends show strong emphasis on Bayesian methodologies applied to healthcare, public policy, and urban systems. Recent work demonstrates increasing integration of deep learning with traditional statistical approaches for problems in causal inference, pandemic forecasting, and transportation optimization. Professor Cabras leads several major research initiatives: Principal researcher for 'Statistical modelling for smart cities' (Beijing Metro Group, 2020-2023) Director of 'Contrato marco para servicios de consultoría estadística' (KROLL Advisory, 2022-2024) Co-researcher for 'Predicción de edad biológica mediante IA' (Fundación Mutualidad Abogacía, 2020-2021) He has supervised graduate research including theses on Bayesian metro occupancy modeling and maintains active collaborations through the Flores de Lemus Institute. His research has been applied in policy analysis, healthcare diagnostics, and urban planning internationally.

Pierre Dutilleulمشاهده پروفایل

استاد

• Environment
• Ecology
• Agriculture
• +۷ مورد دیگر

Pierre Dutilleul is a Professor at McGill University's Macdonald Campus, associated with the McGill School of Environment and the Department of Mathematics and Statistics. His research focuses on spatio-temporal heterogeneity, plant and soil imaging, and fractal analysis of natural structures. Current affiliations: Department of Plant Science, McGill School of Environment Specializations: CT scanning for root and wood imaging, multifractal analysis, soil aggregate dynamics Research interests span environmental sustainability , soil science , and plant production , with recent work on 3D root phenotyping, biochar characterization, and space-time modeling of agricultural and seismic events. Articles demonstrate expertise in applied statistics and computational methods for ecological and agricultural systems. Key methodological contributions include: Development of R packages for spatio-temporal analysis Innovative CT scanning applications in plant and soil research Statistical tests for separable variance-covariance structures

Nezamoddini-Kachouie Nezamoddinمشاهده پروفایل

دانشیار

• Statistical Modeling
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Nezamoddini-Kachouie Nezamoddin is an Associate Professor in the Department of Mathematics and Systems Engineering at Florida Institute of Technology , with an affiliate appointment in the Department of Electrical Engineering and Computer Science , both within the College of Engineering and Science . Since joining Florida Tech in 2012, he has built an interdisciplinary research program bridging engineering, medicine, and biology. Education & Training BASc – Electrical & Computer Engineering MASc – Systems Design Engineering, University of Waterloo, 2008 PhD – Biomedical Engineering Postdoctoral Fellow – Harvard Medical School & Harvard School of Public Health, 2010-2012 Research Interests Dr. Kachouie’s work sits at the confluence of statistical modeling , machine learning , artificial intelligence , image processing , pattern recognition , and biostatistics . He leverages these tools to tackle grand challenges in cancer research , public health , and climate change , producing actionable insights from complex, high-dimensional data. Publication Trends With more than 120 peer-reviewed papers spanning 2003-2025, his recent output emphasizes deep learning (U-Net, CNNs), Bayesian spatiotemporal models , and generalized additive models applied to glacier recession , wildlife population monitoring , sea-level rise , and cancer genomics . These works collectively advance both methodological innovation and high-impact societal applications. Student Mentorship & Grants He has mentored 80+ undergraduates who presented at venues such as NCUR, JSM, and AMS. Seven PhD students have graduated under his supervision and now hold academic positions. He has served as PI of the NSF REU Site “Statistical Models with Applications to Geoscience” (2020-2024) and as mentor for the NSF Biomath REU Site (2015-2017). Currently, his group comprises 2 post-docs, 6 PhD, 2 Master’s, and 7 undergraduate researchers. Contact & Resources Email: nezamoddin@fit.edu Office: Crawford 335 (inside 328) Phone: (321) 674-7485 Profiles: Google Scholar , Research Website

Charles L. Bérubéمشاهده پروفایل

استادیار

• Applied geophysics
• Mineral physics
• Mathematical modeling
• +۷ مورد دیگر

Charles L. Bérubé is an Assistant Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal since 2021. His research couples applied geophysics, mineral physics and machine learning to advance mineral exploration and resource characterisation. Education: B.Sc. in Physics, Université de Montréal Ph.D. in Mineral Engineering, Polytechnique Montréal (2018) Research Interests: Dr. Bérubé focuses on electromagnetic geophysics , induced polarization and machine-learning-assisted inversion . Key themes include: Spectral Induced Polarization (SIP) tomography and modelling Neural-network-based rock-physics inversion Mineral prospectivity mapping with variational autoencoders Quantitative mineralogy and computer vision for outcrop analysis Uncertainty quantification in geophysical imaging Publications Overview: His recent work (2022-2024) demonstrates a clear pivot toward data-driven and AI-enhanced geoscience , integrating Bayesian inversion, generative models and deep learning with traditional exploration geophysics. Earlier studies (2017-2020) concentrated on petrophysical characterisation of the world-class Canadian Malartic gold deposit and non-destructive evaluation of concrete degradation. Graduate Supervision & Funding: Dr. Bérubé has supervised one completed M.Sc. thesis (Benzetta, 2024) on graphite geoelectrics. In May 2023 he received a research grant from the Fonds de recherche du Québec as part of a campus-wide allocation exceeding CAD 1.57 million. Contact: Local B-632, Department of Civil, Geological and Mining Engineering Tel: +1 (514) 340-4711 ext. 4991 | Email: charles.berube@polymtl.ca

• Econometrics
• Theoretical Econometrics
• Semi-nonparametric Estimation
• +۴ مورد دیگر

Elia Lapenta is an Assistant Professor in the Economics Department at National School of Statistics and Economics (ENSAE-CREST), where he joined in September 2020 after completing his PhD at Toulouse School of Economics. Education: PhD in Economics, Toulouse School of Economics, 2020 Dr. Lapenta specializes in theoretical econometrics with research focusing on semi-nonparametric estimation and testing methodologies. His work explores machine learning applications for causal inference, nonparametric instrumental variables regressions, and high-dimensional econometric models. He has developed significant expertise in empirical games with incomplete information, contributing methodological advances to this specialized field. At ENSAE-CREST, Lapenta teaches advanced econometrics courses including Bootstrap and Resampling Methods, Econometrics 1, and Econometrics 2, demonstrating his commitment to both research and education in quantitative economic methods.

Maksim Zhukovskiiمشاهده پروفایل

مدرس ارشد

• Combinatorics
• Probability
• Model Theory
• +۵ مورد دیگر

Dr. Maksim Zhukovskii is a Senior Lecturer in the School of Computer Science at the University of Sheffield. His research spans combinatorics, probability, and model theory, with a focus on random graphs and algorithmic complexity. Email: M.Zhukovskii@sheffield.ac.uk Location: Regent Court (DCS), 211 Portobello, Sheffield S1 4DP Research Group: Foundations of Computation Research Interests: He investigates structural and probabilistic properties of random graphs, including saturation phenomena, Ramsey-type problems, and logical zero-one laws. His work bridges discrete mathematics with theoretical computer science, particularly in descriptive complexity and graph isomorphism. Notable Trends: Recent publications emphasize extremal graph theory, adjacency labeling schemes, and the interplay between logic and random graph properties. Collaborations include institutions like Tel Aviv University and the Weizmann Institute. Grants: Principal Investigator for the Royal Society-funded project Bootstrap percolation in random graphs (2024-2026, £12,000). Education: PhD in Mathematics from Moscow State University (2012). Previously held positions at Moscow Institute of Physics and Technology and research institutions in Israel.

Olivier Scailletمشاهده پروفایل

استاد

• Finance
• Statistics
• Econometrics
• +۳ مورد دیگر

Olivier Scaillet is a Full Professor at the Geneva Finance Research Institute (GFRI) of the University of Geneva and holds a senior chair at the Swiss Finance Institute . His academic focus spans Finance , Statistics , and Econometrics , with expertise in Derivatives Pricing , Asset Pricing , Econometric Theory , and Quantitative Risk Management . Education : PhD in Applied Mathematics from University Paris IX Dauphine Leadership : Director of GFRI and Deputy Director (Education) His research includes nonparametric methods , instrumental variable regression , stochastic volatility models , and systemic risk measures . Recent work explores dynamic portfolio optimization , high-frequency jump analysis , and time-varying risk premia in equity and cryptocurrency markets. Professor Scaillet has been recognized with awards for best papers in Journal of Empirical Finance and Banque Privée Espirito Santo , and is a Fellow of the Society of Financial Econometrics . He serves as an associate editor for journals in Econometrics , Statistics , and Finance , and advises BNPParibas research teams in Paris and London.

Fabio Trojaniمشاهده پروفایل

استاد

• Asset Pricing
• Quantitative Finance
• Financial Econometrics
• +۲ مورد دیگر

Fabio Trojani is a Full Professor of Finance at the University of Geneva since 2015, holding the AXA Chair in Socioeconomic Risk of Financial Markets at the University of Turin and serving as a Senior Chair of the Swiss Finance Institute (SFI). He previously held Full Professorships at the University of Lugano (Statistics), University of St Gallen (Finance), and Bocconi University (Adjunct Professor of Finance). Currently, he directs the SFI PhD program and serves as Editor of the Journal of Financial Econometrics since 2019. His research focuses on Asset Pricing , Quantitative Finance , Financial Econometrics , and Statistical Methods in finance. His work bridges theoretical finance with empirical validation , addressing model robustness , volatility timing , and international SDF frameworks . Articles like ‘ Tradable Factor Risk Premia ’ (2024) and ‘ Smart Stochastic Discount Factors ’ (2019) highlight his contributions to factor risk analysis and model-free pricing . Scientific Awards and honors are not explicitly mentioned in the provided text. However, his frequent invitations to speak at conferences like the Financial Econometrics Conference (2024) and ESEM Annual Meeting underscore his influence in the field. As a director of the SFI PhD program and a former faculty member at multiple institutions, he has shaped academic and professional finance education globally.

• Transportation Engineering
• Infrastructure
• Traffic Management
• +۶ مورد دیگر

José María del Castillo Granados is a Professor at the University of Seville, working within the Higher Technical School of Engineering. He belongs to the Department of Materials and Transportation Engineering and Science, with a specialization in Transportation Engineering and Infrastructure. His academic career spans multiple decades with significant contributions to transportation research, mechanical engineering, and statistical modeling. Professor Castillo Granados specializes in Transportation Engineering with research interests spanning multiple domains: Traffic flow theory and modeling, including development of flow-density relationships Transportation demand modeling and forecasting Railway engineering and infrastructure Logistics and freight transportation systems Planetary gear train mechanics and applications Application of statistical methods to transportation problems His research output shows a clear progression from fundamental traffic flow theory to more complex transportation systems analysis, with recent work incorporating modern data sources like mobile phone data for traffic estimation. His publications demonstrate expertise in both theoretical statistical modeling and practical transportation engineering applications, particularly in the areas of traffic flow modeling, transportation demand analysis, and mechanical systems design. Professor Castillo Granados has received research funding for numerous projects including: DESARROLLO DE SISTEMAS AUTORRETENIBLES PARA SU UTILIZACIÓN EN TRANSMISIONES CONTINUAS VARIABLES RED ESPAÑOLA DE PLATAFORMAS LOGÍSTICAS INTERMODALES SIMETRIA: MODELOS DE SIMULACIÓN PARA LA EVALUACIÓN DE ESCENARIOS MULTIMODALES DE TRANSPORTE DISEÑO, CONSTRUCCIÓN Y ENSAYO DE UNA TRANSMISIÓN MECÁNICA CONTINUAMENTE VARIABLE DIURMÓVIL. Dinámica de una ciudad. Caracterización de la movilidad Urbana mediante datos de telefonía móvil He has been actively involved in the academic community, presenting his research at numerous conferences including the EURO Working Group on Transportation meetings, World Conference on Transport Research, and Transportation Research Board Annual Meetings.