جستجوی استاد راهنمای دانشگاهی

S. Yaser Samadi is an Associate Professor in the Department of Mathematics at the School of Mathematical and Statistical Sciences, Southern Illinois University Carbondale. He holds a Ph.D. in Statistics from the University of Georgia (2014) and maintains an active research program in advanced statistical methodologies. Education: Ph.D. in Statistics, University of Georgia, 2014 Research Interests: Dr. Samadi specializes in multivariate time series analysis, high-dimensional statistical inference, and tensor data analysis. His work addresses critical challenges in big data, symbolic data, and dimension reduction for time series through Bayesian analysis and sequential methods for dependent and independent data, yielding robust models for complex data structures. Publication Trends: His recent publications (2014-2023) emphasize time series analysis, dimension reduction, and innovative approaches for interval-valued and matrix-valued data. Key contributions include envelope models for vector autoregression, copula-based count data modeling, and sequential analysis techniques, bridging theoretical statistics with econometrics and data science applications. Scientific Awards: Outstanding Teacher of the Year, School of Mathematical and Statistical Sciences (2021) Advising: Dr. Samadi has mentored four Ph.D. students to completion: Rukayya Ibrahim (Assistant Professor, Penn State Harrisburg), Wiranthe Herath (Assistant Professor, Drake University), Tharindu De Alwis (Postdoctoral Fellow, WPI), and Hadi Safari Katesari (Teaching Assistant Professor, Stevens Institute of Technology). His Master's students Samira Zaroudi (CUNY) and Reginald Ziedzor (Amplify) have also achieved notable career placements.

• Multivariate Time Series Analysis
• High Dimensional Statistical Inference
• Tensor Data Analysis
• +۵ مورد دیگر

Dr. Mohamad Khaled is a Senior Lecturer in the School of Economics at The University of Queensland (UQ), affiliated with the Centre for Efficiency and Productivity Analysis within the Faculty of Business, Economics and Law. His research focuses on Applied Health Economics , Health Econometrics , and broader Econometrics methodologies. He specializes in analyzing health inequality, socioeconomic determinants of health, and statistical modeling techniques for economic data. His work addresses critical issues such as child malnutrition, health concentration curves, and income-related health disparities. Dr. Khaled has contributed to peer-reviewed journals including European Journal of Health Economics , Journal of Business and Economic Statistics , and Health Economics . His methodologies often involve advanced econometric frameworks and Bayesian approaches. He holds an academic position in a leading economics department and collaborates with institutions like Edward Elgar Publishing. His research emphasizes bridging theoretical econometric models with real-world health policy applications.

• Applied Health Economics
• Health Econometrics
• Econometrics
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Dr. Mawuli Kouami Segnon is a researcher at the Chair of Empirical Economics, School of Business and Economics, University of Münster. His work focuses on econometric modeling, financial time series analysis, and volatility forecasting across various domains including cryptocurrencies, energy markets, and macroeconomic indicators. Research interests include: Development of advanced volatility models (GARCH, multifractal, regime-switching) Applications to financial markets, energy economics, and macroeconomic policy High-frequency data analysis and mixed-frequency forecasting Count data modeling with conditional heteroscedasticity Portfolio risk management using copula and multifractal approaches Recent publications demonstrate expertise in: Geopolitical risk impacts on stock volatility Comparative analysis of realized variance measures Inflation uncertainty modeling in G7 countries Electricity price volatility in Australian markets Bitcoin market forecasting Historical economic data analysis Current projects (since 2020) involve: Innovative economic/financial time series forecasting Financial market volatility modeling Applications of multifractal structures in econometrics

• Econometrics
• Time Series Analysis
• Financial Forecasting
• +۵ مورد دیگر

Elisa Perrone is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. Her research focuses on dependence modeling, copula theory, and their applications in fields such as public transport analysis, environmental risk assessment, and renewable energy forecasting. Academic Rank: Assistant Professor University: Eindhoven University of Technology (TU/e) Department: Mathematics and Computer Science Elisa’s work explores discrete copulas, zero-inflated data, optimal experimental design, and uncertainty quantification. She has contributed to modeling dependence structures in complex datasets, particularly in transportation systems and climate science. Recent research outputs highlight copula-based statistical post-processing for weather forecasts, analysis of multi-way contingency tables, and uncertainty reduction in LED health management. Her publications span top-tier journals and conferences in statistics and applied mathematics. Scientific Award : Second Best Poster Presentation Award (2015) Elisa actively organizes workshops like the Eurandom Workshop on Dependence Modeling and contributes to editorial activities. She teaches courses on linear statistical models, regression models, and dependence modeling.

• Statistics
• Mathematical Statistics
• Dependence Modeling
• +۴ مورد دیگر

Prof. Gregorio Iglesias is a Professor of Marine Renewable Energy at University College Cork (UCC) and Honorary Professor of Coastal Engineering at the University of Plymouth. His expertise lies in Marine Renewable Energy and Coastal Engineering, with a focus on wave and tidal energy systems, offshore wind integration, and coastal protection strategies. He has secured over €12M in research funding as Principal Investigator and authored/edited key texts such as Wave and Tidal Energy (Wiley) and Ocean Energy and Coastal Protection (Springer). Education: BEng (Civil Engineering, 1992), MEng (Civil Engineering, 1993), PhD (Engineering, 2001). Research Interests: Advanced modeling of wave energy converters, floating offshore wind turbines, coastal erosion mitigation, and climate change impacts on marine energy resources. His work bridges theoretical advancements and practical applications, including coasts like the Port of Gijón (Spain) and the Shannon Estuary. Recent publications highlight climate-driven renewable energy transitions, multi-hazard coastal resilience frameworks, and techno-economic assessments of offshore systems. He chairs the IEC Standards panel for wave energy device testing and serves as Subject Editor for Energy (Elsevier) . Professional Activities: Lead of Marine Renewable Energy research at MaREI (Ireland), former Head of the COAST Engineering Group at Plymouth (2012–2018), and member of PIANC’s Universities Consortium. His work has generated 5,459 citations with an h-index of 41. Teaching: Delivers modules on Ocean Energy (NE4003/NE6005) and Hydraulics (CE3007).

• Marine Renewable Energy
• Coastal Engineering
• Wave Energy Conversion
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Hugo Georges Victor Lavenant serves as Assistant Professor in the Department of Decision Sciences at Bocconi University, Milan, where he has held a faculty position since 2020. Previously, he completed a postdoctoral fellowship at the University of British Columbia (2019-2020) under the Pacific Institute of Mathematical Sciences and earned his PhD in Mathematics from Université Paris-Sud (2016-2019) under Filippo Santambrogio's supervision. His academic foundation includes: PhD in Mathematics, Université Paris-Sud (2016-2019) Studies at École Normale Supérieure (2012-2016) covering mathematics, physics, history, and philosophy of science Classes préparatoires in mathematics and physics (2010-2012) Lavenant's research centers on optimal transport theory and its applications across mathematical disciplines. He investigates geometric structures in Wasserstein spaces, develops numerical methods for dynamical optimal transport, and bridges theoretical advances with Bayesian statistics. His work demonstrates particular innovation in trajectory inference for biological data and dependence measures for random measures, connecting pure mathematics with computational statistics. Recent publications reveal accelerating interdisciplinary impact, with 2024-2025 works extending optimal transport to machine learning (kernel methods, variational inference) and data science (opinion dynamics, single-cell analysis). This trajectory shows increasing methodological sophistication in handling measure-valued mappings and non-smooth geometries while maintaining computational tractability. Award recognition includes: Pacific Institute of Mathematical Sciences Postdoctoral Fellowship Lavenant actively mentors early-career researchers through formal advising relationships and collaborative projects. He currently supervises two PhD candidates (George Kanchaveli and Francesco Mascari, co-advised with Marta Catalano) and has guided Master's students including Mathis Hardion and Niccolò Bargellini. His teaching portfolio spans advanced analysis, optimization, and real analysis courses at Bocconi, reflecting his commitment to mathematical rigor in education. He operates within Bocconi's Decision Sciences ecosystem while maintaining international collaborations with researchers at UBC, Université Paris-Sud, and statistical groups worldwide. Current projects focus on entropy-based transport methods and geometric approaches to nonparametric statistics, positioning his work at the intersection of theoretical mathematics and data-driven applications.

• Optimal Transport
• Calculus of Variations
• Convex Analysis
• +۳ مورد دیگر

David Frazier is a Professor in the Department of Econometrics & Business Statistics at Monash University, specializing in simulation-based inference, financial econometrics, and nonparametric/semiparametric modeling. He teaches ETC 1010: Data Modeling and Computing. His research focuses on robust statistical methods, Bayesian computation, and model misspecification. Key projects include 'Consequences of Model Misspecification in Approximate Bayesian Computation' (2020-2025) and 'Loss-based Bayesian Prediction' (2020-2025). Recent work addresses forecasting in misspecified models, weak identification in econometric frameworks, and robust variational Bayes techniques. His contributions align with UN Sustainable Development Goals related to economic and environmental sustainability. Projects: 4 active/funded projects with ARC, Brown University, and international collaborators. Publications: Over 37 peer-reviewed articles in journals like the Journal of the American Statistical Association and Journal of Econometrics. Research interests include advancing Bayesian methodologies for complex models, with applications in asset pricing and economic forecasting. His work emphasizes reliability in statistical inference under model uncertainty and computational efficiency.

• Simulation-based inference
• Financial Econometrics
• Nonparametric and semiparametric econometric modeling
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Vicky Fasen-Hartmann is a Professor at the Karlsruhe Institute of Technology (KIT) within the Department of Mathematics, specifically affiliated with the Institute of Stochastics. She has held her W3 Professor position since October 2012, with two periods of parental leave (August 2016-August 2017 and October 2018-October 2019). Prior to her current position, she held postdoctoral research positions at ETH Zurich (RiskLab), TU Munich, Université Pierre et Marie Curie, and Cornell University. Her educational background includes: Habilitation (2010) in Heavy Tails in Finance, Insurance and Telecommunication from TU Munich Ph.D. (2004) in Extremes of Lévy Driven Moving Average Processes with Applications in Finance from TU Munich Diploma in Mathematics (2002) from Karlsruhe Institute of Technology Professor Fasen-Hartmann's research spans multiple areas of theoretical and applied statistics with a focus on extreme value theory, heavy-tailed distributions, and their applications in finance and risk management. Her work bridges theoretical probability with practical financial applications, particularly in modeling rare events and systemic risks. She has made significant contributions to the understanding of Lévy processes, continuous-time ARMA models, and multivariate extremes. Her research combines rigorous mathematical theory with practical applications in financial mathematics, insurance, and telecommunications networks. The trends in her recent publications (2020-2025) show a clear evolution toward high-dimensional extreme value theory, financial network risk contagion, and advanced modeling of continuous-time processes. Her work increasingly addresses the challenges of modern financial systems, including systemic risk measurement, high-dimensional dependency structures, and the statistical properties of extreme events in complex systems. She has developed innovative methodologies for analyzing multivariate extremes, risk contagion, and continuous-time state space models. Professor Fasen-Hartmann has served in significant editorial roles including Associate Editor for the Scandinavian Journal of Statistics since 2014, Managing Editor of Lévy Matters (2008-2014), and Editor of Bernoulli News (2009-2011). She has also been active in academic service through committee work, including the Steering Committee of the Probability and Statistics Group in Germany (2014-2016) and the Examination Board of the Department of Mathematics at KIT (since 2017). She has supervised numerous doctoral and master's students, with current PhD candidates including Lucas Butsch (since 2021) and previously Lea Schenk, Celeste Mayer, Markus Scholz, and Sebastian Kimmig. Her teaching portfolio includes advanced courses in Time Series Analysis, Continuous Time Finance, Extreme Value Theory, and Asymptotic Stochastics. She regularly organizes workshops and conferences on specialized topics in probability and statistics, demonstrating her leadership in the academic community.

• Extreme Value Theory
• Lévy Processes
• Risk Management
• +۶ مورد دیگر

Dimitris Karlis is a Professor in the Department of Statistics at the Athens University of Economics and Business (AUEB), within the School of Information Sciences and Technology. He has been a key academic figure since earning his BSc and PhD in Statistics from AUEB in 1992 and 1999, respectively, and was promoted to Associate Professor in 2012 before advancing to full Professor. Education: BSc in Statistics, AUEB (1992) PhD in Applied Statistics, AUEB (1999) His research spans computational statistics, mixture models, EM algorithms, copulas, multivariate discrete data, and applications in sports, insurance, and seismicity. He has published extensively in top-tier statistical journals such as the Journal of the Royal Statistical Society and Statistics in Medicine . The 15 most recent publications reveal a strong focus on multivariate count data, integer-valued time series, model-based clustering using copulas, and applications in actuarial science and health. His work frequently involves mixture models, Bayesian inference, and innovative extensions of Poisson-based frameworks. Scientific Service and Recognition: Associate Editor: Metron, Communications in Statistics, IMA Journal of Management Mathematics, Stochastic Environmental Research and Risk Assessment Editor: Biometrics Bulletin of IBS Member: American Statistical Society, International Statistical Institute, International Association of Statistical Computing, Hellenic Statistical Institute Publicity Officer: Eastern Mediterranean Region, International Biometric Society Advising and Grants: He has supervised 4 completed PhDs and 18 Master’s theses, with several more in progress. He has led and participated in research projects funded by the European Union and EUROSTAT, particularly in official statistics. His advising spans methodological and applied topics in statistics. Labs and Teams: While no formal lab is named, he collaborates extensively with researchers in actuarial science, transportation, biostatistics, and environmental risk, often through joint projects and publications.

• Computational Statistics
• Mixture Models
• EM Algorithm
• +۱۲ مورد دیگر

Krithika Suresh is a Research Assistant Professor in the Department of Biostatistics at the University of Michigan, with a secondary appointment in Radiation Oncology. Her research focuses on biostatistical methodology development for survival analysis, longitudinal data, and prediction models with direct applications in cancer research and health outcomes. Her educational background includes: PhD in Biostatistics, University of Michigan, 2018 MMath in Biostatistics, University of Waterloo, 2013 BS in Honours Mathematics, University of Waterloo, 2012 Dr. Suresh's research centers on advanced statistical techniques including survival analysis, joint modeling of longitudinal and time-to-event data, and dynamic prediction systems. She applies these methods to cancer prognostic modeling (particularly prostate cancer), personalized medicine frameworks, and pragmatic clinical trial design for weight management interventions in primary care settings. Her work bridges theoretical biostatistics with practical healthcare applications. Analysis of her 2020-2022 publications reveals consistent methodological innovation in survival analysis, featuring copula-based dynamic prediction models for time-dependent covariates and longitudinal biomarkers. She has also contributed significantly to machine learning applications in medical imaging through explainable AI pipelines and pragmatic trial design for public health interventions. Her research demonstrates strong integration of statistical theory with oncology and population health applications. Based on available information, details regarding Dr. Suresh's student advising, research grants, laboratory affiliations, or scientific awards are not provided in the source materials.

• Survival Analysis
• Longitudinal Data Analysis
• Joint Modeling
• +۴ مورد دیگر

Professor Dennis Kristensen is a faculty member at the Department of Economics, University College London (UCL). He holds affiliations with prominent institutions including CeMMAP, the Institute for Fiscal Studies, Aarhus Center for Econometrics (ACE), and the Centre for Macro and Financial Econometrics at Essex University. His research focuses on econometric theory, applied microeconomics, and quantitative finance. Key areas include structural dynamic models, nonlinear econometrics, and financial econometrics. His work integrates advanced computational methods and nonparametric techniques to address complex economic problems. Research interests span stochastic volatility models, demand inversion in consumer behavior, and indirect estimation methods. He has contributed to methodologies for handling unobserved heterogeneity and time-varying parameters in economic models. Prof. Kristensen's publications emphasize methodological innovation, with recent work addressing continuous-time Markov models, diffusion copulas, and dynamic discrete choice frameworks. His articles often bridge theoretical econometrics with applied contexts, such as corporate defaults and financial market analysis. He is actively engaged in the academic community, contributing to journal editorials and interdisciplinary collaborations. His affiliations reflect a commitment to advancing econometric theory and its applications in policy and finance.

• Econometrics
• Applied Economics
• Quantitative Finance
• +۳ مورد دیگر

Daniel Kowal is an Associate Professor in the Department of Statistics and Data Science at Cornell University, effective July 2024. Previously, he served as the Dobelman Chair Assistant Professor at Rice University. His research focuses on Bayesian methodology for complex dependent data, including functional, time series, and spatial datasets, with applications in environmental health, epidemiology, finance, and astronomy. He develops scalable algorithms for high-dimensional data and interpretable uncertainty quantification. His work has been recognized with the Blackwell-Rosenbluth Award (2021) and ARO Young Investigator Award (2020). Education: Ph.D. in Statistics (Cornell University), M.S. in Statistics (Cornell University), B.A. in Mathematics (Washington University in St. Louis). Research interests include Bayesian models for prediction/inference, decision theory, discrete data analysis, and scalable approximations. Key areas of application: environmental health policy, wearable devices, economics, biomedical engineering, and astronomy. Recent grants include NSF-funded projects on adaptive dependent data models and Army Research Office initiatives on Bayesian approximations. He has supervised multiple Ph.D. students, including Yunan Gao, Thomas Sun, and Brian King. His software contributions include R packages like countSTAR, SeBR, and lmabc for Bayesian regression and data synthesis. Awards include Lindley Prize Honorable Mention (2024), Arnold Zellner Thesis Award (2018), and numerous student paper awards from ASA sections.

• Bayesian Statistics
• Environmental Health and Policy
• Public Health
• +۹ مورد دیگر

Roles and Affiliations: Alexander Herbertsson is a Senior Lecturer in Statistics and Quantitative Finance at the University of Gothenburg, affiliated with the CFF-Centre for Finance. He is based in the Department of Economics with Statistics, located at Vasagatan 1, Gothenburg. His work focuses on financial risk management, credit risk modeling, and quantitative finance methodologies. Education: Ph.D. in Economics: Quantitative Finance (2007), University of Gothenburg Licentiate of Engineering in Industrial Mathematics (2005), Chalmers University of Technology M.Sc. in Engineering Physics (Applied Mathematics specialty) (2001), Chalmers University of Technology Research Interests: Herbertsson's research emphasizes applied financial mathematics and statistical methods in finance. Key areas include credit risk modeling (default contagion, systemic risks), financial engineering, and the development of dynamic models for portfolio credit risk. His work often integrates Markov chain models, phase-type distributions, and stochastic processes to analyze dependency structures and pricing of credit derivatives. Recent studies explore saddlepoint approximations for portfolio risk analysis and risk management under exogenous shocks. Teaching: He teaches advanced courses in credit risk modeling, quantitative finance, and applied probability theory, emphasizing practical applications in risk management and financial markets. Grants and Labs: While specific grants are not detailed in the text, his affiliation with the CFF-Centre for Finance suggests involvement in collaborative financial research projects. His work often addresses real-world applications of theoretical models in systemic risk and portfolio hedging.

• Quantitative Finance
• Credit Risk
• Financial Engineering
• +۳ مورد دیگر

Peter Moffatt is a Professor of Econometrics in the School of Economics at the University of East Anglia. He is affiliated with the Norwich Institute for Healthy Aging, Centre for Behavioural and Experimental Social Science, Applied Econometrics and Finance, and Behavioural Economics research groups. His work focuses on Experimetrics—the econometric analysis of experimental economic data—and spans risk attitude modeling, financial econometrics, and consumer behavior. Dr. Moffatt has authored the 2015 textbook Experimetrics and delivered global Masterclasses on the subject. His research interests include risk preference analysis, bank borrower repayment dynamics, and consumer theory. Notable projects include a 2002–2005 Leverhulme Trust-funded study on experimental methods in economics. He advises PhD students on topics like econometric modeling of experimental data and discrete choice models. Key collaborations span international institutions, with recent work addressing volatility in energy and stock markets, pandemic impacts on financial systems, and gender differences in gambling behavior. Awards and fellowships are not explicitly noted, but his contributions to econometric methodology are widely recognized. Dr. Moffatt’s expertise includes advanced statistical techniques such as GARCH modeling, copula methods, and hurdle models, applied to diverse fields like credit risk assessment, public policy evaluation, and behavioral experiments.

• Experimetrics
• Econometrics
• Risk Attitude Analysis
• +۵ مورد دیگر

Dr. Christine Eckert serves as an Adjunct Professor in the Marketing Discipline at the University of Technology Sydney's Business School. Her research program bridges quantitative modeling with practical applications across financial decision making, strategic governance, and consumer behavior. Dr. Eckert's scholarly work demonstrates methodological sophistication with a focus on understanding how market participants make choices across diverse contexts. She has established herself as a leading researcher in tobacco control, using discrete choice methodologies to examine how packaging, warnings, and product design influence smoking behaviors among different population segments. Her publication record spans premier journals including Journal of Marketing Research, Management Science, Journal of Management, Review of Finance, and Tobacco Control, reflecting the interdisciplinary nature of her research interests. Dr. Eckert frequently employs experimental designs and advanced statistical techniques to uncover the mechanisms driving consumer choices in complex decision environments. Dr. Eckert's research portfolio reveals a consistent pattern of applying rigorous quantitative methods to address real-world problems from retirement planning to tobacco regulation. Her tobacco control studies have directly informed policy debates around standardized packaging and warning labels, while her financial decision-making research offers insights for improving retirement income products. CFP Board Center for Financial Planning Best Paper Award (2021) Dr. Eckert has secured significant research funding including projects with the Health Research Council of New Zealand and Australian Research Council. She has served on professional committees including the ANZMAC Executive Committee and PBRF Quality Evaluation Committee. Her teaching encompasses undergraduate and postgraduate courses in Marketing Research, Pricing Analysis, and Product Innovation Management, where she likely integrates her research expertise in quantitative methods and consumer behavior.

• Quantitative modeling
• Financial decision making
• Strategic governance decisions
• +۷ مورد دیگر