جست‌وجوی استاد و پژوهشگر

• Statistics and Probability

Ersin Yılmaz serves as a Postdoctoral Researcher within the Department of Computer Science at Aalto University, conducting advanced research at the intersection of statistical theory and computational applications. His work contributes to the university's data science initiatives through methodological innovations in regression analysis. His research concentrates on statistical methodology development, particularly for complex data structures involving correlated errors and high-dimensional spaces. Key interests include shrinkage estimation techniques, kernel-based regression frameworks, and robust modeling approaches applicable to machine learning systems. This work bridges theoretical statistics with practical computational implementations in computer science contexts. Analysis of his publication record reveals a focused trajectory in advanced regression methodologies, specifically addressing estimation challenges in partially linear models with dependency structures. His work demonstrates technical rigor in mathematical statistics while maintaining relevance to contemporary data science applications, particularly in improving estimator efficiency under complex error dependencies. Scientific Awards: No scientific awards, fellowships, or honors were documented in the source material. Current information indicates no formal student supervision roles or grant-funded projects. The researcher operates within Aalto University's computer science infrastructure without specific lab or team affiliations mentioned in available records.

• Financial Econometrics
• Time Series Analysis
• Statistics
• +۳ مورد دیگر

Christian Francq is a Professor in Applied Mathematics with dual affiliations at ENSAE-CREST (École Nationale de la Statistique et de l'Administration Économique - Center for Research in Economics and Statistics) and the University of Lille (specifically University Charles-de-Gaulle Lille 3). He joined ENSAE-CREST in September 2011 on secondment while maintaining his position at the University of Lille where he has been a full Professor since September 2002. His academic career began as an Assistant Professor in Applied Mathematics at University Lille I from 1990 to September 1998, followed by a position as full Professor of Mathematics at the University Littoral-Côte d'Opale from September 1998 to September 2002. Professor Francq's research focuses on Financial Econometrics , Time Series Analysis , and Statistics , with particular expertise in GARCH models, volatility modeling, and risk measurement. His work bridges theoretical statistical developments with practical financial applications, making significant contributions to the understanding of financial time series and risk modeling. He has developed advanced methodologies for estimating and testing GARCH-type models, addressing challenges related to non-stationarity, moment conditions, and model specification. His recent publications demonstrate a strong focus on advancing the theoretical foundations of time series analysis while maintaining relevance to financial applications. Key themes include conditional heteroskedasticity modeling, systemic risk measurement, volatility estimation, and the development of robust statistical inference procedures for financial time series. His work often addresses the challenges of working with real financial data, including non-stationarity, heavy-tailed distributions, and complex dependence structures. Professor Francq has been actively involved in major research projects including the ANR project MLforRisk (ANR-21-CE26-0007) and previously completed ANR projects MultiRisk and Econom&Risks. These projects focused on econometric methods for multiple risk modeling and contributed to the development of reproducible research platforms like Exec&Share and RunMyCode. He has extensive teaching experience in advanced statistical and financial courses including GARCH and stochastic volatility models, Linear Time Series, and Risk measures. His book on GARCH modeling represents a significant contribution to the field, providing comprehensive coverage of both theoretical foundations and practical applications of GARCH models. Professor Francq collaborates extensively with researchers across Europe and has co-authored numerous papers with prominent scholars in econometrics and statistics. His work has been presented at major international conferences and published in top-tier journals in econometrics and statistics.

Barbara Verfürthمشاهده پروفایل

استاد

• Numerical Methods for Partial Differential Equations
• Multiscale Finite Element Methods
• Numerical Homogenization
• +۴ مورد دیگر

Prof. Dr. Barbara Verfürth is a Professor at the Institute for Numerical Simulation (INS) at the University of Bonn since October 2022. Previously, she served as a Junior Research Group Leader and Tenure-Track Professor at Karlsruhe Institute of Technology (KIT) from 2020-2022, was a PostDoc at the University of Augsburg (2018-2020), and completed her PhD at the University of Münster (2015-2018). Her academic journey demonstrates a strong focus on numerical analysis and computational mathematics. Prof. Verfürth's research interests span several interconnected areas in computational mathematics: Numerical methods for partial differential equations Multiscale (finite element) methods (Numerical) homogenization (Time-harmonic) wave propagation: Helmholtz and Maxwell equations Nonlinear PDEs (nonlinear diffusion, nonlinear Helmholtz) Her recent publications demonstrate a consistent focus on multiscale methods for wave propagation problems, particularly in high-contrast and time-varying media. The research shows strong connections between theoretical numerical analysis and practical applications in metamaterials and wave physics. Her work bridges pure mathematical analysis with computational implementation, often developing novel algorithms for challenging multiscale problems. Prof. Verfürth leads several significant research projects: Homogenization of time-varying metamaterials (Project B4, DFG CRC 1173) Numerical methods for nonlinear, random and dynamical multiscale problems (Project 496556642, DFG Emmy Noether) Previously completed TEEMLEAP - a testbed for exploring machine learning in atmospheric prediction (KIT Future Fields) She is actively involved in academic mentoring, currently advertising for a PhD position focusing on numerical multiscale methods for linear elasticity with high-contrast coefficients. Her teaching includes courses such as "Scientific Computing I" and "Introduction to Numerical Mathematics" at the University of Bonn.

• Statistics
• Computer Science
• Economics
• +۶ مورد دیگر

Pedro Galeano San Miguel is an Associate Professor at the Department of Statistics, Universidad Carlos III de Madrid. He is affiliated with the Nonparametric Inference for Complex Data and its Applications (NICDA) research group and contributes to the Flores de Lemus Institute and UC3M-Santander Big Data Institute. His work bridges statistics, computer science, and economics with a focus on financial and high-dimensional data. Research Interests: Functional data analysis and outlier detection Bayesian nonparametric methods and stochastic volatility models Copula models for systemic risk and portfolio selection High-dimensional statistical inference and dynamic correlation Big data applications in economics and finance Publication Trends: His recent work (2024–2016) emphasizes copula models for financial risk, functional regression techniques with missing data, and Bayesian inference for high-dimensional time series. He explores systemic banking risks, volatility prediction, and correlation structure changes across economic and financial domains. Grants & Projects: He leads or contributes to projects on computational statistics for complex dependencies, big data customer network analysis, and multivariate asymmetric GARCH modeling, funded by institutions like the State Research Agency (AEI) and Banco Santander.

Günter Schneckenreitherمشاهده پروفایل

پژوهشگر

• Computational Statistics
• Epidemiological Modeling
• Agent-Based Simulation
• +۳ مورد دیگر

Günter Schneckenreither is a researcher affiliated with the Computational Statistics research area at Technische Universität Wien (TU Wien). His work spans interdisciplinary applications of computational methodologies to epidemiology, public health, and biomedical modeling, with a focus on agent-based simulations and cellular automata. Research Interests: Heterogeneity in disease transmission, age-dependent SIR models, synthetic data generation for pandemics, and fractional diffusion processes in structured populations. Publications: Recent research includes epidemiological modeling of SARS-CoV-2 immunization levels, melanocytic proliferation simulations, and comparative studies of epidemic modeling frameworks. Projects: Active participant in ARGESIM benchmark studies, integrating cellular automata and differential equations for epidemic simulations. Methodologies: Specializes in dynamic causal modeling, fractal topological structures, and multiplex social network analysis.

Gilles Blanchardمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Statistical Learning Theory
• +۵ مورد دیگر

Gilles Blanchard is a Professor at Paris-Saclay University , affiliated with the Institute of Mathematics in Orsay. His research focuses on statistical learning theory , multiple testing , kernel methods , and high-dimensional statistics . Key Contributions: Decontamination of mutually contaminated models Novelty detection with semi-supervised learning Non-Gaussian component analysis (NGCA) for dimension reduction Scientific Awards: PhD thesis award from University of Paris 6 (2004) Google Scholar h-index of 49 with over 8,000 citations Recent Research Trends: False Discovery Rate (FDR) control in structured hypothesis testing Conformal prediction for link analysis Adaptive sampling in restless bandits Statistical learning on measure spaces His work bridges theoretical statistics with practical machine learning applications, including genome-wide association studies , persistent homology in topological data analysis , and random feature moments for compressive learning . He is involved in open-source software development like μTOSS for multiple testing standardization and has advised on projects related to flow cytometry and Hadrontherapy applications.

• Non-parametric Inference
• Regression Models
• Missing Data Analysis
• +۲ مورد دیگر

Ana Perez Gonzalez is a researcher at the Department of Statistics and Operational Research, Faculty of Business Sciences and Tourism, University of Vigo. She earned her PhD from the University of Santiago de Compostela in 2003, focusing on non-parametric inference for regression models with missing responses. Her research centers on robust statistical methods for missing data, functional regression, and non-parametric estimation. Key collaborations with Wenceslao Gonzalez Manteiga, Graciela Boente, and Ana Bianco Contributions to wild bootstrap calibration, bandwidth selection, and imputation techniques Her work addresses applications in biomedical statistics, climate modeling, and spectrometric studies. Notable methodologies include goodness-of-fit tests for missing data models, principal component-based estimators, and asymptotic variance analysis.

Alfonso Valdesogo Roblesمشاهده پروفایل

دانشیار

• Econometrics
• Financial Econometrics
• Dependency Models
• +۲ مورد دیگر

Alfonso Valdesogo Robles is an Associate Professor in the Department of Applied Economics at the University of the Balearic Islands (UIB), holding a PhD from Université Catholique de Louvain with visiting appointments at University of Luxembourg, Universidad Carlos III de Madrid, Universidade Federal Fluminense, and Université de Cergy-Pontoise. His academic credentials include: PhD in Economics, Université Catholique de Louvain His research specializes in econometric modeling of financial systems, with emphasis on dependency structures and time-series analysis in volatile markets. He develops advanced statistical frameworks for financial risk assessment and economic forecasting, bridging theoretical econometrics with real-world financial applications. A member of UIB's Econometrics and Data Science (ECD) Consolidated R+D+I Group, he teaches core quantitative courses including Econometrics and Macroeconometrics across Business Administration, Economics, and Tourism programs. His pedagogical approach integrates computational methods with empirical economic analysis, supervising master's theses on tourism economics and financial modeling.

PABLO VAZQUEZ REGUEIROمشاهده پروفایل

استاد

• Particle Physics
• High Energy Physics
• Atomic, Molecular and Nuclear Physics
• +۵ مورد دیگر

Pablo Vazquez Regueiro is a Professor at the Faculty of Physics, Department of Particle Physics, University of Santiago de Compostela. He is affiliated with the Galician Institute of High Energy Physics (IGFAE) and the GAES High Energy Physics Group. Education: Ph.D. from University of Santiago de Compostela (2000) - Thesis: Development and construction of electronics for microstrip detector readout Research Interests: His work focuses on experimental particle physics, including detector technology, microstrip electronics, and high-energy collision analysis. Key contributions involve CP violation studies, baryon spectroscopy, and trigger system development. Scientific Contributions: His publications highlight expertise in LHCb collaborations, detector simulation, and data analysis techniques. Research spans Higgs boson properties, dark matter searches, and hadronization mechanisms. Labs & Teams: Actively participates in IGFAE and GAES High Energy Physics Group activities, contributing to international collaborations like LHCb.

Lâmân LELÉGARDمشاهده پروفایل

پژوهشگر

• Image Processing
• Photogrammetry
• Remote Sensing
• +۳ مورد دیگر

Lâmân LELÉGARD serves as a Researcher at the French National Institute of Geographic and Forest Information (IGN), currently assigned to the MATIS laboratory since 2018 after 10 years at LASTIG. As a Civil Servant Engineer and member of the GEOVIS research team, he contributes to geospatial research while occasionally teaching image processing, DTM manipulation, and colorimetry courses in the PPMD Master's program at the National School of Geographic Sciences (ENSG). His academic foundation includes an engineering degree (2004-2007) and Specialized Master in Geodesy (2006-2007) from ENSG, preceded by Mathematics and Physics preparatory classes at Charlemagne High School (2001-2004). Early career internships involved gravitational field analysis at Belgium's Royal Observatory (2006) and gravito-elastic equations at Paris Institute of Earth Physics (2007). LELÉGARD's research centers on image quality enhancement in photogrammetry, with sustained contributions to motion blur correction, radiometric processing, and colorimetry. His work addresses practical challenges in aerial/terrestrial imagery, including nighttime scanning systems, channel-dependent exposure artifacts, and scanned campaign inhomogeneities. Methodologies leverage adaptive filtering, principal component analysis, and multiscale transforms for real-world geospatial applications. Publication analysis reveals consistent focus on operational image processing solutions between 2010-2022, with recent work emphasizing radiometric correction for analogue airborne data and real-time vignetting estimation. His output bridges theoretical image science and field-deployable techniques for national mapping agencies. He has advised two significant internships: Emeric DELAYGUE's 2009 research on aerial motion blur deconvolution and Vincent DAVAL's 2012 work on nighttime mobile imaging restoration. These projects reflect his expertise in image degradation correction within ENSG's PPMD program framework. As an active GEOVIS team member, LELÉGARD continues advancing image quality methodologies at MATIS laboratory, focusing on radiometric processing innovations for IGN's geospatial data pipelines while maintaining selective teaching engagements at ENSG.

Jochen Liskeمشاهده پروفایل

استاد

• Extragalactic Astronomy
• Observational Cosmology
• Galaxy Evolution
• +۳ مورد دیگر

Jochen Liske is a Professor of Observational Astronomy at the University of Hamburg , affiliated with the Hamburger Sternwarte (Hamburg Observatory) and the Quantum Universe Cluster of Excellence . He leads the Observational Astronomy Research Group and contributes to major international collaborations like GAMA , WAVES , and 4MOST . Research Focus: Extragalactic astronomy, observational cosmology, galaxy evolution, dark energy, and real-time cosmology via redshift drift measurements. Instrumentation: Key roles in the 4MOST and ELT-ANDES spectrograph projects, including calibration and software development. Outreach: Creator of the Hubblecast and ESOcast video podcasts, presenter of documentaries like "Eyes on the Skies" and "The Eye 3D" , and frequent public speaker. His recent publications span galaxy surveys, gravitational wave detection methods, and instrumentation design. Notable awards include the MEDEA Special Jury Award 2009 for science communication and an ESO Fellowship . He teaches astrophysics courses and mentors students through projects like the Quantum Universe Cluster.

Marek Omelkaمشاهده پروفایل

دانشیار

• Asymptotic Statistics
• Nonparametric Statistics
• Copula Models
• +۱۰ مورد دیگر

Assoc. Prof. Marek Omelka is a faculty member at the Department of Probability and Mathematical Statistics , Faculty of Mathematics and Physics, Charles University in Prague. His research focuses on copula models, dependence modeling, and nonparametric statistical methods with applications in multivariate analysis, permutation tests, and conditional copula structures. Current teaching: NMST424 Mathematical Statistics 3 and JEM019 Statistical Methods for Data Analysis Research interests: Asymptotic statistics, copula estimation, permutation methods, multivariate association, and functional data depth His recent publications analyze copula parameter estimation, covariate effects in conditional models, and multivariate tail coefficients, with implementations in R. The PMSE program he guarantees connects theoretical statistics with real-world applications in data science, finance, and medical studies.

Martin Eigelمشاهده پروفایل

پژوهشگر

• Numerical Mathematics
• Uncertainty Quantification
• Stochastic Partial Differential Equations
• +۵ مورد دیگر

Martin Eigel is a Researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) , specializing in numerical methods for stochastic partial differential equations, uncertainty quantification, and machine learning applications in computational mathematics. His work bridges tensor networks, Bayesian inversion, and quantum simulations. Research Interests : Adaptive stochastic Galerkin finite element methods Low-rank tensor approximations for high-dimensional problems Machine learning integration with PDE solvers Quantum circuit simulation techniques Bayesian inverse problems and error control Key Article Trends : His recent publications focus on merging deep learning architectures (e.g., ResNet, CNNs) with stochastic and tensor-based numerical methods for solving parametric PDEs, Bayesian inversion, and quantum systems. Topics include Hamilton-Jacobi-Bellman equations, Langevin dynamics, and risk-averse optimization under uncertainty.

Andrey I. Lyakhovمشاهده پروفایل

استاد

• Wireless Networks
• IEEE 802.11 Protocol
• Network Performance Analysis
• +۳ مورد دیگر

Andrey I. Lyakhov is a Professor and Doctor of Computer Science at the Institute for Information Transmission Problems of the Russian Academy of Sciences. He serves as the Head of Laboratory №18 and has been active since 1959. His research focuses on wireless networks, particularly IEEE 802.11 and 802.16 protocols. Position: Laboratory Chief Affiliation: Institute for Information Transmission Problems, Russian Academy of Sciences Research Interests: Wireless Networks, MAC Protocol Analysis, Network Performance, Distributed Control, Multicast QoS, Channel Assignment His work includes analytical modeling of data transmission, beaconing in mesh networks, and studies on network unfairness and congestion. He has contributed to patents in wireless sensor networks and piconet beacon management. Notable publications span wireless LANs, WiMAX, and sensor network optimization. Lyakhov's research has involved collaborations with international conferences and journals, focusing on throughput estimation, channel contention, and quality of service in wireless systems. Recent publications include studies on bandwidth piggybacking (2010), intra-flow interference in mesh networks (2010), and multicast QoS support in WLANs (2007). Earlier works from 1983-2008 cover foundational topics in queueing theory, cache efficiency, and distributed control systems.

• Econometrics
• Time Series Analysis
• Macroeconomic Forecasting
• +۴ مورد دیگر

Mark W. Watson is the Howard Harrison and Gabrielle Snyder Beck Professor of Economics and Public Affairs at the Princeton University Department of Economics . His research focuses on macroeconomic time series analysis , with particular emphasis on dynamic factor models , inflation forecasting , and structural vector autoregression methods. Research Interests : Watson's work addresses fundamental econometric challenges in Time series modeling under structural instability Robust inference for nonstationary data Macroeconomic forecasting techniques Analysis of business cycle dynamics Cointegration and unit root testing Monetary policy and inflation relationships Publication Trends show expertise in high-dimensional forecasting , factor models , and robust statistical methods applied to macroeconomic data. His collaborative work with James H. Stock has become foundational in modern econometric practice. Technical Contributions include development of New index construction for economic indicators Software tools for cointegration analysis Replication frameworks for empirical research Available through Princeton University servers.