جست‌وجوی استاد و پژوهشگر

• Algebraic Geometry
• Mathematical Biology
• Phylogenetics
• +۳ مورد دیگر

Jesús Fernández Sánchez is a Researcher at the Department of Mathematics, Polytechnic University of Catalonia (UPC), associated with the GEOMVAP (Geometry of Varieties and Applications) research group. He holds an affiliation with the Faculty of Mathematics and Statistics (FME) and the Institute of Mathematics of UPC-BarcelonaTech (IMTech). His work focuses on algebraic geometry, mathematical phylogenetics, and computational algebra, with applications to evolutionary biology and stochastic processes. Research Interests: Fernández Sánchez's research bridges algebraic geometry with biological applications, particularly in phylogenetics. He explores topics such as Markov matrix embeddability, phylogenetic invariants, equivariant models, and algebraic methods for evolutionary analysis. His work includes developing semi-algebraic methods for quartet reconstruction and analyzing rank conditions in phylogenetic networks. Grants & Projects: He has led or contributed to several competitive research projects, including 'GEOMETRIA DE VARIETATS I APLICACIONS' (GEOMVAP group), the María de Maeztu Excellence Program, and collaborations on phylogenetic model development. His projects emphasize multidisciplinary approaches in geometry, algebra, and topology. Advising: He has supervised doctoral students like Marina Garrote-López and Jordi Roca-Lacostena in topics such as phylogenetic reconstruction methods and Markov matrix analysis. His lab investigates geometric and algebraic tools for evolutionary biology, with a focus on model validation and computational techniques. Affiliations: Besides his primary role at UPC, he collaborates with international research networks in algebraic phylogenetics and stochastic processes. His work integrates pure mathematics with applied problems in biology and statistics.

• Innovation Research
• Innovation Management
• Entrepreneurship
• +۸ مورد دیگر

Dietmar Harhoff is a prominent academic and researcher in innovation, entrepreneurship, and economics. He serves as Director at the Max Planck Institute for Innovation and Competition and Honorary Professor at Ludwig-Maximilians-Universität München (LMU Munich). His career spans roles including Managing Director at the Max Planck Institute, Director of LMU's Entrepreneurship Center, and academic leadership at the LMU Munich School of Management. Education: Ph.D. (1991) MIT Sloan School of Management Habilitation (1996) University of Mannheim Master of Public Administration (1987) Harvard Kennedy School Diplom-Ingenieur (1984) University of Dortmund Research Interests: Focuses on innovation systems, patent policies, venture capital, and applied economics. His work bridges theoretical frameworks with empirical analysis of innovation processes, technology markets, and policy impacts. Articles Trends: Recent work addresses AI evolution, data commons, patent opposition impacts, and regional innovation dynamics. Themes include patent quality, innovation networks, and policy design for disruptive technologies. Awards: 2019 Federal Cross of Merit (Germany) 2015 Bavarian Academy of Sciences Membership 2013 Schumpeter School Award Advisory & Grants: Leads German federal innovation policy advisory (SPRIND), chairs EU innovation initiatives, and advises institutions like Nesta and the Bavarian AI Council. Extensive grants focus on innovation metrics, patent systems, and technology transfer. Labs/Teams: Co-founded octimine technologies and German Accelerator Inc., and directs research centers in entrepreneurship and innovation at LMU and Max Planck.

Cristian Felipe Jiménez-Varónمشاهده پروفایل

پژوهشگر

• Time Series Analysis
• Functional Data Analysis
• Environmental Statistics
• +۳ مورد دیگر

Dr. Cristian Felipe Jiménez-Varón is a Researcher in the Department of Mathematics at the University of York, UK, where he focuses on Network Stochastic Processes and Time Series (NeST) under the supervision of Prof. Marina Knight. His current role involves modeling time series data on dynamic networks. Previously, he earned a PhD in Statistics from KAUST (2024), supervised by Prof. Ying Sun, and a Master's in Applied Mathematics from Universidad Nacional de Colombia (UNAL). He taught at UNAL (2016-2020) and Universidad Autónoma de Manizales (2017-2020). His research interests span time series analysis, functional data analysis, and their applications in environmental, economic, and financial domains. He specializes in developing statistical methods for diverse data types, particularly in environmental monitoring and financial modeling. Recent work includes copula-based visualization techniques and high-dimensional functional forecasting. Dr. Jiménez-Varón’s publications address cutting-edge topics such as quantile coherence estimation, outlier detection in functional data, and network-based time series modeling. His work bridges theoretical statistical innovation with practical applications in sustainability and finance.

Yue Zhaoمشاهده پروفایل

مدرس

• Nonparametric Statistics
• Semiparametric Multivariate Dependence Modeling
• Copula Methods
• +۵ مورد دیگر

Dr. Yue Zhao is a Lecturer in Statistics at the Department of Mathematics, University of York (equivalent to Assistant Professor in North America). She holds a PhD in experimental cosmology from Princeton University (2010) and a second PhD in statistics from Cornell University (2015). Her postdoctoral research included positions at McGill University (2015-16) and KU Leuven (2016-19). She serves as Director of the MSc in Statistics and Computational Finance program. Her research focuses on nonparametric/semiparametric multivariate dependence modeling (copulas), high-dimensional statistics, survival analysis, and statistical inference for neural networks. She actively supervises PhD students and welcomes applications in copula methods, estimating equations, and high-dimensional statistical problems. Dr. Zhao has published extensively in top journals like Annals of Statistics and Scandinavian Journal of Statistics , with work spanning theoretical advancements and applied statistical methodologies. Her research activities include peer-review for journals like Statistics and Computing and invited talks at international conferences. Professional affiliations include roles in the York University Statistics group, and she maintains an active research network through collaborations across Europe and North America.

Anita Lindmarkمشاهده پروفایل

دانشیار

• Socioeconomic Disparities
• Stroke Research
• Mediation Analysis
• +۳ مورد دیگر

Anita Lindmark is an Associate Professor in the Statistics department at the Umeå School of Business, Economics and Statistics (USBE), Umeå University. Her work focuses on understanding socioeconomic disparities in healthcare outcomes, particularly in stroke research and mediation analysis. She holds a recognized qualification as a university teacher. Education: Recognised as a university teacher (formal qualification). Research interests include the application of advanced statistical methods to analyze socioeconomic determinants of health outcomes, stroke severity, and survival rates. Her work emphasizes mediation analysis to identify pathways through which socioeconomic status influences health, and she develops novel statistical techniques to address unobserved confounding in observational studies. Her publications (2014–2025) consistently explore socioeconomic disparities in stroke care and outcomes, integrating sensitivity analysis and causal inference frameworks. This work bridges epidemiology, biostatistics, and public health, with a focus on improving healthcare equity. Awards: USBE Scientific Awards 2020. Research projects include 'Uncovering the mechanisms behind socioeconomic inequalities in stroke care and outcome through innovative statistical methods for mediation analysis' (2019–2022). She contributes to the Stat4Reg research group, emphasizing statistical methods in register-based studies. No formal advisees/students are listed, though her work involves collaborations with clinicians and epidemiologists. Lindmark’s team focuses on interdisciplinary approaches to healthcare data analysis, leveraging national registries like Riks-Stroke to address healthcare inequities.

Maria Karlssonمشاهده پروفایل

دانشیار

• Statistics
• Econometrics
• Education Technology
• +۳ مورد دیگر

Maria Karlsson is an Associate Professor at the Department of Statistics, Umeå School of Business, Economics and Statistics (USBE), Umeå University. She also serves as a Student Counsellor. Her research focuses on statistical methodology, particularly in truncated and censored regression models, as well as pedagogical innovations in statistics education. She has contributed to projects like Stat4Reg and led initiatives such as Analysis of demand equations using mixtures of Tobit models (2013–2015) and Estimation of labor market policy program effects in the presence of interference between individuals (2013–2014). Educational Background: Holds the title of Distinguished university teacher , reflecting her expertise in pedagogy. Her academic qualifications emphasize both statistical rigor and educational effectiveness. Research Interests: Her work bridges statistical theory and practical applications, including labor market policy evaluation, causal inference in observational studies, and integrating flipped classrooms with MOOCs. She also explores ethical frameworks for AI in education and innovative writing practices in statistics curricula. Publications Overview: Karlsson's publications span statistical methodology (e.g., truncSP R package development) and pedagogical advancements. Recent work highlights include addressing ethical AI challenges and advancing active learning strategies in higher education. Awards: Recognized as a Distinguished university teacher , underscoring her contributions to educational excellence. Grants & Projects: Led two major research projects on labor market policies and demand modeling, demonstrating her impact on both theoretical and applied statistics. Labs/Teams: Active member of the Stat4Reg research group, focusing on statistical methods for regression analysis and policy evaluation.

Chuan-Fa Tangمشاهده پروفایل

استادیار

• Statistics
• Survival Analysis
• Statistical Machine Learning
• +۹ مورد دیگر

Chuan-Fa Tang is an Assistant Professor of Statistics in the Department of Mathematical Sciences at the University of Texas at Dallas, affiliated with the School of Natural Sciences and Mathematics. He joined UTD in 2019 and teaches graduate-level courses including Applied Linear Models, Linear Statistical Models, and Nonparametric Methods. Ph.D. in Statistics from University of South Carolina (2017) M.S. and B.S. in Mathematics from National Taiwan University (2010, 2007) His research focuses on order-restricted inference , shape-constrained inference , survival analysis , and statistical machine learning , with particular interest in: Nonparametric and semiparametric models Empirical processes and likelihood methods Post-selection inference Stochastic ordering and heavy-tailed distributions His work includes developing goodness-of-fit tests for Cox models and stochastic ordering frameworks, with software implementations in R . Key publications examine uniform stochastic ordering comparisons, positive quadrant dependence testing, and Taylor's laws for heavy-tailed data. Scientific awards include: NSF DMS 2311292 grant (2023-2026) for monotonic hazard trend research He has contributed computational tools like TestUSO and LLGOF_UniCoxPH packages for statistical analysis.

Theodosiadou Ouraniaمشاهده پروفایل

پژوهشگر

• Stochastic modeling
• Time series analysis
• Applied probabilities
• +۳ مورد دیگر

Ourania Theodosiadou is a Researcher at the Department of Mathematics, Aristotle University of Thessaloniki (since Oct 2024). Previously, she served as a Postdoctoral Researcher at the Institute of Information and Communication Technologies (CERTH) from 2019 to 2024 and held multiple contracted lecturer roles at Aristotle University and the University of Macedonia. She earned a PhD in Mathematics (2019) and a Master's in Statistics and Modeling (2014), both from AUTH, with a focus on stochastic processes and financial applications. Her doctoral thesis explored latent stochastic processes with jumps in finance under Prof. Georgios Tsaklidis. Her research interests span stochastic modeling, time series analysis, computational statistics, and machine learning. Recent work includes real-time threat assessment using Hidden Markov Models (2023), cryptocurrency transaction analysis for illegal activity detection (2023), and centrality-based network node identification (2022). Methodological contributions include state space modeling with constraints (2021) and Kalman filter applications for jump detection in financial markets (2017-2019). Publications reflect interdisciplinary applications in finance, security, and computational methods. Current work extends AI-driven solutions against terrorist financing and explores blockchain forensics through time series analysis.

• Statistics
• Econometrics
• Risk Management
• +۵ مورد دیگر

Ruben Seiberlich is a Lecturer at the Zurich University of Applied Sciences (ZHAW), School of Management and Law, Department of Banking, Finance, and Insurance. He holds a PhD in Econometrics from the University of Konstanz (2013) and has taught courses such as Advanced Quantitative Methods, Risk Management, and Finanzinstrumente & Portfoliotheorie. His research focuses on statistics, econometrics, and risk management, with notable work on real estate bubble risk, gender discrimination in housing, and partisan bias in inflation expectations. Education: Diplom Volkswirt (2009), Dr. rer. pol. Ökonometrie (2013) Current roles: Study Program Leader for MSc Banking and Finance, Model Performance and Backtesting Leader at Credit Suisse AG Research Interests span empirical economics, risk analysis, and applied econometric methods. His recent articles analyze real estate and gold market bubbles, gender bias in rental housing, and political bias in economic expectations. Key methodologies include semi-parametric decomposition, propensity score matching, and shrinkage estimation. Scientific Networks: Member of the German Statistical Society’s committee for Empirical Economics and Applied Econometrics; ORCID ID 0000-0002-5258-5499.

• Transportation Systems
• Discrete Choice Modeling
• Causal Inference
• +۴ مورد دیگر

Prateek Bansal serves as an Academic Visitor and postdoctoral research associate at the Transport Strategy Centre (TSC) within the Centre for Transport Studies (CTS) at Imperial College London's Department of Civil and Environmental Engineering. He has been affiliated with Professor Dan Graham's research group since August 2019, contributing to transportation systems analysis through advanced statistical methodologies. Education: PhD in Transportation from Cornell University MS in Transportation Engineering from University of Texas at Austin B.Tech in Civil Engineering from Indian Institute of Technology (IIT) Delhi His research focuses on advancing discrete choice modeling with applications in transportation behavior. Key innovations include variational Bayes for fast inference, causal frameworks for transit systems with structural correlations, and entropy-based solutions for endogeneity. His doctoral work pioneered semi-parametric heterogeneity modeling, t-distributed error kernels, minorization-maximization algorithms, and quadrature methods for multi-dimensional integrals in choice experiments. Analysis of his 15 most recent publications (2019-2022) reveals a strong emphasis on Indian transportation contexts, including electric vehicle adoption, two-wheeler markets, and vaccine hesitancy. Methodologically, he integrates spatial statistics, Bayesian computation, and causal inference to address metro system vulnerability, crowd dynamics, and traffic flow relationships. His work consistently bridges econometric theory with real-world transit data from London, Hong Kong, and Indian cities. As part of the Centre for Transport Studies, Bansal collaborates within Imperial's Transport Strategy Centre on projects involving large-scale transit data analysis. His research outputs appear in leading journals including Transportation Research Part B, Journal of the Royal Statistical Society, and Transportation.

Rebecca A. Hutchinsonمشاهده پروفایل

دانشیار

• Machine Learning
• Ecology
• Computational Sustainability
• +۵ مورد دیگر

Rebecca A. Hutchinson is an Associate Professor at Oregon State University, affiliated with both the School of Electrical Engineering and Computer Science and the Department of Fisheries, Wildlife, and Conservation Sciences. Her research focuses on computational sustainability, integrating machine learning with ecological challenges to enhance ecosystem health and inform computer science. Key areas include species distribution modeling, occupancy models, and leveraging citizen science data. She has collaborated extensively with mentors like Thomas G. Dietterich and Matthew G. Betts, and contributed to the unmarked R package for ecological analysis. PhD in Computer Science from Carnegie Mellon University (2009), advised by Tom M. Mitchell. Bachelor’s in Computer Science & Engineering from Bucknell University (2002). Her work bridges statistical ecology and machine learning, addressing issues like habitat modeling, semi-parametric techniques in probabilistic models, and species interaction networks. Recent projects include improving species distribution models using spatial clustering and neural networks (StatEcoNet). Her research has been recognized with awards like the Best Paper (proposals track) at the 2021 ICML workshop on Climate Change. Grants include National Science Foundation SEES Fellowships and collaborations with organizations like the Fred Hutchinson Cancer Research Center. She advises on computational ecology tools and has mentored students in interdisciplinary projects combining AI and environmental science.

• Actuarial Science
• Statistics
• Mortality Modeling
• +۳ مورد دیگر

Professor Erengul Dodd is a Professor of Actuarial Math & Statistics at the University of Southampton, actively engaged in research and accepting PhD students. She is affiliated with the Faculty of Social Sciences and the Department of Actuarial and Financial Mathematics. Her research interests span Actuarial Science, Statistics, Mortality Modeling, Fertility Projections, Demographic Analysis, and Cancer Insurance Pricing. Dodd is a member of several research groups including the Statistics Statistical Sciences Research Institute (S3RI), Centre for Population Change, and Demographic and Migration Modelling. Dodd's research portfolio reveals strong trends in demographic modeling and actuarial applications, with publications spanning mortality modeling, fertility projections, and cancer risk assessment. Her work frequently employs advanced statistical techniques including Bayesian methods, generalized additive models, and stochastic modeling approaches. The research demonstrates interdisciplinary applications connecting actuarial science with public health, demography, and social policy. Dodd has secured significant research funding from organizations including the Economic and Social Research Council (ESRC) and the Society of Actuaries. Her current projects focus on generational connections, demographic modeling, and cancer mortality analysis. She has supervised multiple PhD students across Mathematical Sciences and Social Statistics & Demography. Dodd previously served as Chair at The Alan Turing Institute from 2021-2023, demonstrating leadership in the broader academic community.

• Economic Inequality
• Lorenz Curves
• Distribution Theory
• +۴ مورد دیگر

Professor José María Sarabia Alegría serves as Professor of Quantitative Methods for Economics and Business at the University of Cantabria since 2001 and Professor of Statistics and Operations Research at the Complutense University of Madrid since 2000 (currently on leave). He directs the research group "Quantitative Methods in Economics and Business" at the University of Cantabria and has been an associate researcher in the Risk, Insurance, and Finance Research Group (IREA-UB) at the University of Barcelona since 2012. Dr. Sarabia earned his degree in Mathematical Sciences (specializing in Statistics and Operations Research) from the University of Valladolid in 1986 and completed his PhD in Mathematical Sciences at the University of Cantabria in 1989, with postgraduate studies at the Polytechnic University of Valencia. He has held visiting professorships at the University of Reading, London School of Economics, and Imperial College London. His research focuses on distribution theory and multivariate analysis applied to economics, risk analysis, actuarial statistics, and economic inequality measurement. His seminal 1999 work "An ordered family of Lorenz curves" published in the Journal of Econometrics has been highly influential in inequality studies, and his collaborative book "Conditional Specification of Statistical Models" introduced significant advances in multivariate distributions for economic applications. Dr. Sarabia has received numerous scientific awards including the prestigious Julio Castelo Matrán International Insurance Research Award (2008) and the University of Cantabria Social Council Research Award (2011), along with international mobility scholarships from the Salvador de Madariaga program and Erudite Program. He has supervised five doctoral theses (one receiving the Extraordinary Doctoral Award and two receiving the Social Council Award) and has secured research funding from the National Research Plan, Institute of Fiscal Studies, Mapfre Foundation, and corporate contracts with EON Spain, Nuclenor, and Cedex. Currently serving as Principal Investigator for the project "Parametric and Semi-Parametric Modeling of Multivariate Risks," he leads the Quantitative Methods research group at University of Cantabria while maintaining his association with the Risk, Insurance, and Finance Research Group at University of Barcelona, continuing to advance methodological approaches to economic and financial risk analysis.

• Econometrics
• Labour Economics
• Wage Dynamics
• +۷ مورد دیگر

Costanza Naguib is an Assistant Professor in Econometrics at the University of Bern and a Lecturer at the Faculty of Economics, Università della Svizzera italiana (USI). Her academic work bridges quantitative econometric modeling and labor economics, with a strong focus on wage mobility and labor market dynamics. Research Interests: Her research explores wage dynamics using advanced econometric tools such as functional copula models, nonlinear autoregressive processes, and semi-nonparametric estimation techniques. She investigates how education, labor market institutions, and skill mismatches influence individual wage trajectories and job satisfaction. Scientific Contributions: Specializes in non-parametric and semi-nonparametric econometric methods Focuses on labor economics with empirical applications to wage mobility Utilizes copula-based models to capture complex dependencies in labor data She completed her PhD in Economics at the Institute for Economic Research (IRE) in March 2018. During her doctoral studies, she participated in the Swiss Program for Beginning Doctoral Students in Economics (Study Center Gerzensee) in January 2017 and was a visiting PhD student at CREST (Centre de recherche en économie et statistique), Paris, in April 2017. Her PhD thesis developed a novel semi-nonparametric model to analyze wage mobility, emphasizing the role of education. There is no public information available about doctoral students she may advise or specific research grants she leads. She is actively contributing to the fields of econometrics and labor economics through methodological innovation and empirical analysis.

Michael V. Pakمشاهده پروفایل

استادیار

• Topological Quantum Computing
• Quantum Theory of Multi-Component Systems
• Matter-Antimatter Interactions
• +۴ مورد دیگر

Dr. Michael V. Pak is an Assistant Professor at the Air Force Institute of Technology's Graduate School of Engineering, Department of Engineering Physics, and affiliated with the Center for Directed Energy (CDE). His research bridges theoretical physics and quantum chemistry, focusing on quantization theory, topological quantum computing, and matter-antimatter interactions. He holds dual PhDs in Quantum Chemistry (Iowa State University, 2002) and Theoretical Physics (St. Petersburg State University, 1996), alongside an MS in Theoretical Chemistry (St. Petersburg State University, 1992). PhD, Quantum Chemistry, Iowa State University (2002) PhD, Theoretical Physics, St. Petersburg State University, Russia (1996) MS, Theoretical Chemistry, St. Petersburg State University, Russia (1992) Dr. Pak's research explores the quantum theory of multi-component systems, particularly methods to describe positron annihilation in complex environments and temperature-dependent beta decay modeling for nuclear explosion scenarios. His work on topological quantum computing emphasizes Majorana states and fault-tolerant gate operations, while nanowire architectures and Kohn-Sham systems under finite-temperature DFT are also key topics. His scientific awards include the Department of Energy Research Fellowship (2002), Nobel Foundation Scholarships (1992, 1995), and the V.A. Steklov Mathematical Institute Award (1992). He has published extensively on electron-positron correlation, hydrogen transfer, and quantum computational methods. At AFIT, Dr. Pak contributes to the Center for Directed Energy, advancing research in quantum theory and computational physics. His teaching interests align with theoretical physics and quantum mechanics education.