Find Academic Supervisors

• Statistics
• Ecological Modeling
• Sports Analytics
• +4 more

Prof. Dr. Roland Langrock holds the Chair of Statistics and Data Analysis at the Faculty of Economics, University of Bielefeld . He is a spokesperson for the Center for Statistics and a subproject manager in the Transregio 212 NC³ collaboration. His research spans ecological statistics, sports analytics, and time series modeling. 2026–present: Principal investigator for "Data-based indication of fraud in live betting" (DFG) 2025–present: Subproject manager D06 in TRR 212 NC³ 2021–present: ERASMUS representative for Master of Statistical Sciences Research Interests: His work focuses on hidden Markov models for analyzing animal movement, sports performance, and commercial data. Key applications include marine predator behavior , football match dynamics , and fraud detection in betting . He develops flexible statistical frameworks for state-switching processes across domains. Scientific Awards: Multiple German Research Foundation grants (2017–2026) and participation in EU-funded projects. Notable publications in Journal of the Royal Statistical Society , Ecology Letters , and Science . Additional Roles: Member of the Bielefeld Graduate School in Theoretical Sciences, organizer of advanced statistical methods courses, and contributor to software packages like moveHMM . His collaborations extend to marine biology (blue whales), subterranean rodent studies, and retail demand forecasting.

• Quantitative Finance
• Financial Econometrics
• Risk Management
• +3 more

Sung Kim is an Assistant Professor in the Department of Economics & Finance at the College of Business , Louisiana State University in Shreveport (LSUS), where he has been since 2018. His academic expertise bridges applied mathematics and financial economics.

• Bayesian inference
• Biostatistics
• Clinical trials
• +7 more

Mauro Gasparini is a Full Professor at the Department of Mathematical Sciences (DISMA) of Polytechnic University of Turin. He serves as Director of DISMA since 2019, Member of Academic Senate, and co-leader in the SmartData@PoliTO Big Data Laboratory. His career spans academia and industry, including roles at Purdue University (Assistant Professor 1992-1996) and Novartis (Senior Statistician 1996-1998). He has been Editor of Biometrical Journal (2012-2015) and maintains referee activities across international journals. PhD from University of Michigan (1992, Dirichlet process applications) Academic leadership: Department Director, Editorial boards, ISTAT Advisor Research spans Bayesian methodology with biomedical applications Maintains collaborations with Novartis, Chiesi, and research centers His research interests focus on Bayesian inference , Biostatistics , and Clinical trials methodology, particularly addressing issues in pharmaceutical development, genomic data analysis, and medical decision-making. Recent work includes vaccine efficacy modeling, optimal imaging timing for cancer diagnostics, and adaptive trial designs. Key publication trends show interdisciplinary applications in Statistics in Medicine , Biometrics , and Statistical Methods in Medical Research , with emphasis on biomedical data science, Bayesian adaptive methods, and clinical decision support systems. Scientific contributions include: Editor, Biometrical Journal (2012-2015) Advisor, Italian National Institute of Statistics (2020-2024) Leadership in multiple research projects (NODES, SORGENTE, IDEAS) As PhD advisor, he supervises students in: Shaoshi Tang (Clinical trial modeling) Saeed Sani (Biomedical data analysis) Marco Ratta (Genomic statistics) Luca Rondano (Bayesian methods) Vittorio Zampinetti (Tumor DNA sequencing) Fulvio Di Stefano (Evidence-based decision statistical methods) He leads research projects in pharmaceutical statistics, genomic surveillance, and spatial risk assessment frameworks, with recent emphasis on SARS-CoV-2 analysis and cancer progression modeling.

David JobstView profile

Researcher

• Statistical Meteorology
• Distributional Regression
• Copula Modeling
• +3 more

David Jobst is a Researcher at the Institute for Mathematics and Applied Informatics within the Faculty of Mathematics, Natural Sciences, Economics and Computer Science at the University of Hildesheim, where he has been employed since August 2020. He teaches undergraduate mathematics courses including Introduction to Analysis, Stochastics, and Advanced Seminars on Series and Infinite Products. Dr. Jobst holds multiple degrees from the Technical University of Munich: a Master of Education and First State Examination in Mathematics and Sports Education (2017-2020), a Bachelor of Science in Mathematics with a minor in Electrical and Information Technology (2015-2019), and a Bachelor of Education in Mathematics and Sports Education (2014-2017). His research focuses on advanced statistical methods for weather forecasting, specializing in distributional regression, copula modeling (particularly Vine Copulas), and probabilistic weather prediction. He develops innovative approaches to postprocess ensemble weather forecasts using machine learning techniques, with particular attention to spatio-temporal dependencies in meteorological data. Jobst's publication record demonstrates a clear progression in developing sophisticated statistical frameworks for weather forecast postprocessing, moving from traditional methods like Ensemble Model Output Statistics (EMOS) to advanced machine learning approaches including gradient-boosted models and vine copula structures. His work bridges theoretical statistics with practical meteorological applications, addressing challenges in temperature, wind speed, and cloud cover forecasting. While no specific awards are documented in the available materials, his research has been presented at numerous international conferences including the European Geosciences Union General Assembly, CMStatistics conferences, and specialized workshops on ensemble postprocessing across Europe. As a university researcher, Jobst participates in academic service through conference organization and peer review activities. His consultation hours are conducted via virtual meeting platforms, reflecting modern academic practices. He maintains a private academic website (jobstdavid.org) where additional research materials and contact information are available.

Zhan GaoView profile

Assistant Professor

• Econometrics
• Machine Learning
• High-Dimensional Time Series
• +4 more

Dr. Zhan Gao is an Assistant Professor of Economics at Southern Methodist University, where he has been a faculty member since 2025. His core expertise lies in econometric theory and its intersection with machine learning, particularly in high-dimensional time-series and panel-data settings. Education: Ph.D. in Economics, University of Southern California Research Interests: Dr. Gao’s research spans econometrics , machine learning , biostatistics , and health economics . Methodologically, he focuses on robust estimation under endogeneity, high-dimensional inference, and convex optimization techniques that scale to large datasets. His recent publications advance robust regression methods that withstand outlier contamination, develop new identification strategies for categorical random-coefficient models, and create survival-analysis tools for dependent censoring in oncology trials. Collectively, this body of work reflects a commitment to rigorous theory paired with reproducible computational tools. Software & Reproducibility: All major papers are accompanied by open-source R and Python packages hosted on GitHub, ensuring full reproducibility. Notable repositories include implementations of penalized least squares, copula-graphic estimators, and high-dimensional GMM routines. Teaching: In Fall 2025 Dr. Gao will teach ECO 4370 / 6370 Computing for Economics , introducing graduate and advanced undergraduate students to modern computational methods and convex optimization in econometrics.

Henryk ZähleView profile

Professor

• Stochastics
• Quantitative Risk Management
• Statistical Robustness
• +4 more

Henryk Zähle is a Full Professor of Stochastics at Saarland University's Department of Mathematics, where he has held a W3 position since 2014. He previously served as a W2 Professor (2013-2014) and W1 Junior Professor (2010-2012) at Saarland, and earlier at TU Dortmund University (2007-2010). He earned his Ph.D. in Mathematics from Technical University Berlin (2004) and a Diploma in Mathematics from University of Göttingen (2000). His research focuses on statistical robustness of risk measures asymptotic theory for empirical processes quantitative risk management Markov decision models insurance and financial mathematics with methodological contributions to bootstrapping, quasi-Hadamard differentiability, and sensitivity analysis. Article trends show sustained engagement with stochastic process theory nonparametric estimation robust statistical functionals applications to insurance and finance asymptotic error distributions time series analysis spanning both theoretical and applied domains. Scientific awards include Marie Curie Fellowship (University of Warwick, 2001) DFG Fellowship (2000-2003) He has supervised numerous Ph.D., Master's, and Bachelor's theses on topics like risk measure asymptotics empirical process convergence copula robustness Markov decision sensitivity nonparametric risk estimation statistical bootstrap methods and serves as Associate Editor for Metrika .

Christopher Frank ParmeterView profile

Associate Professor

• Econometrics
• Stochastic Frontier Analysis
• Measurement Error
• +7 more

Christopher Frank Parmeter is an Associate Professor in the Economics department at the Miami Herbert Business School, University of Miami. His research focuses on econometric methodology and applied economic analysis. Role: Assoc. Professor Email: c.parmeter@miami.edu ORCID: 0000-0001-6123-0107 His research spans econometrics, stochastic frontier analysis, and measurement error correction. Recent work explores robotics' economic impact, bank efficiency under exchange rate volatility, and robust nonparametric techniques. Key publication trends include Bayesian stochastic frontier models, nonparametric inference, and empirical applications in tourism and finance. While specific awards are not detailed, his work contributes significantly to econometric theory and applied economics.

• Stochastic Models
• Machine Learning
• Artificial Intelligence
• +4 more

Fabrizio Durante is a Full Professor of Probability and Mathematical Statistics at the University of Salento, Department of Mathematics and Physics "Ennio De Giorgi" in Lecce, Italy. Previously, he served as Full Professor of Mathematical Methods for Economics, Finance, and Actuarial Sciences from December 2016 to October 2024. Durante earned his PhD in Mathematics from the University of Lecce and completed his Habilitation in Mathematics at Johannes Kepler University of Linz in 2010. His academic career includes positions as Assistant Professor (2010-2014) and Associate Professor (2015-2016) of Statistics at the Free University of Bozen-Bolzano. His research focuses on stochastic methods and models for complex systems and machine learning, with significant applications in quantitative risk management across hydrology, environmental sciences, economics, and finance. Durante is particularly renowned for his contributions to copula theory, co-authoring the monograph "Principles of Copula Theory" with Carlo Sempi. Durante currently serves as national coordinator of the Italian MIUR project "Stochastic Modeling of Compound Events" (2023-25) and is affiliated with the ICSC National Research Center in High Performance Computing, Big Data and Quantum Computing. STAHY Best Paper Award 2015 from the International Commission on Statistical Hydrology (jointly with G. Salvadori and C. De Michele) Durante maintains significant editorial responsibilities as associate editor of "Dependence Modeling" and area editor of "Fuzzy Sets & Systems" and the "International Journal of Approximate Reasoning." He is co-chair of the "Dependence Models and Copulas" team of the ERCIM Working Group on Computational and Methodological Statistics. His expertise is regularly sought as an invited plenary speaker at international conferences in stochastic methods and dependence modeling.

• Copulas
• Extreme Value Theory
• Stochastic Modeling
• +4 more

Gianfausto SALVADORI is a University Researcher in the Department of Mathematics and Physics "Ennio De Giorgi" at the University of Salento, specializing in Probability and Mathematical Statistics (SSD MAT06). His office is located on the ground floor, room 325, at the Former Fiorini College - Via per Arnesano - Lecce. Dr. SALVADORI is an applied mathematician with research interests spanning Copulas, Extreme Value Theory, and stochastic modeling applications to environmental phenomena. His work focuses on modeling multivariate dependent random variables and analyzing extreme environmental events including rainfall, floods, droughts, and sea storms. He has been involved in environmental research since 1989, initially working on Chernobyl radioactive pollution and Universal Multifractals modeling, and since 2001 has specialized in Copulas methodology. His research activities include collaboration with hydrological engineers at the Polytechnic of Milan, participation in national and European projects related to extreme environmental phenomena, and academic contributions including co-authoring the book "Extremes and Copulas" published by Springer-Verlag in 2007. He has developed course materials on Extreme Value Theory, indicating active teaching responsibilities in this specialized area of statistics. Office hours are available by prior agreement via email. His contact information includes telephone +39 0832 29 7584 and email gianfausto.salvadori@unisalento.it. His professional activities demonstrate ongoing engagement in both theoretical statistical research and practical applications to environmental challenges.

• Statistics
• Meteorology
• Econometrics
• +4 more

Dr. Annette Christine Möller is a Professor at Bielefeld University's Faculty of Economics, where she leads the Chair of Data Science and is affiliated with the Department of Empirical Methods. She serves as a key researcher at the Bielefeld Center for Data Science (BiCDaS). Her contact information includes email (annette.moeller@uni-bielefeld.de) and phone (+49 521 106-4877). Her research focuses on: Developing statistical methods for weather forecast post-processing Copula-based modeling of multivariate dependencies Ensemble forecasting for climate risk mitigation Applications in renewable energy prediction Statistical computing and open-source tool development Her publication record (2019-2025) demonstrates strong emphasis on: Probabilistic weather forecasting techniques Vine copula applications in meteorology Statistical calibration of ensemble models Interdisciplinary work spanning climatology, epidemiology and education She leads significant research grants including: DFG project: 'Statistical post-processing of ensemble forecasts' (2018-2024) DFG project: 'Mitigating climate risks via copula-based forecast improvement' (2024-2026) She collaborates with the Technical University of Munich and maintains active research teams focused on statistical meteorology and climate informatics.

• Dynamic Classification
• Copula Functions
• Probability Distributions
• +4 more

Francesca Condino serves as Associate Professor of Statistics (SECS-S/01) at the University of Calabria's Department of Economics, Statistics and Finance since 2020, following her tenure as Researcher from 2012. She teaches graduate courses including Multivariate Data Analysis and Statistical Methods for Business Strategies within the Statistics for Data Science and Data Science for Business Analytics programs. Her academic qualifications include: Bachelor's degree with honors in Statistical and Actuarial Sciences from University of Calabria (2002) Master's in Economy and Statistics of Territory from G. Tagliacarne Institute, Rome (2003) Ph.D. in Statistics from University 'Federico II' of Naples (2010) Condino's research centers on dynamic classification algorithms, copula-based dependence modeling, and novel probability distributions. She develops statistical frameworks for analyzing income/consumption data, hydrological phenomena, and medical diagnostics, with particular expertise in density-valued symbolic data classification and spectral biomarker analysis. Her methodological innovations bridge theoretical statistics with applications in economic policy and clinical neuroscience. Analysis of her recent publications reveals three dominant research streams: economic inequality studies using Lorenz curves and share-density clustering; medical diagnostics through FTIR spectroscopy and multivariate analysis for multiple sclerosis/epilepsy; and theoretical contributions to unit interval distributions and copula-based modeling. Her work demonstrates consistent interdisciplinary collaboration between statisticians, economists, and medical researchers. No scientific awards were documented in the provided materials. Her research has been supported by CNR research grants (2004-2010), Calabria Region funding (2008), and University of Calabria research assignments (2012). Teaching experience spans 23 years across multiple institutions, complemented by national scientific qualification for Associate Professorship (2017). She contributes to the 'Statistica & Demografia' research group and utilizes the departmental Statistical Informatics Laboratory for computational work.

• Economics
• Finance
• Actuarial Science
• +7 more

Domenico De Giovanni is an Associate Professor at the Department of Economics, Statistics and Finance 'Giovanni Anania' (DESF) at the University of Calabria. His research spans economics, finance, and actuarial science, focusing on quantitative methods and strategic decision-making. He teaches Computer Lab for Finance and Mathematical Methods for Economics in the university's graduate programs. Email: ddegiovanni@unical.it Research Groups: Quantitative Methods for Economics, Finance, and Management His work explores dynamic harvesting, capacity investment under uncertainty, tax evasion dynamics, and energy market modeling. Recent publications analyze mortality dependencies, spread options in commodity markets, and semi-Markov disability transitions.

Taha Hossein RashidiView profile

Professor

• Travel behaviour analysis
• Transportation planning
• Activity-based travel demand modelling
• +8 more

Professor Taha Hossein Rashidi is a leading expert in Transport Engineering at the School of Civil and Environmental Engineering, University of New South Wales (UNSW), and a member of the Research Centre for Integrated Transport Innovation (rCITI). His work bridges disciplines like economics, statistics, urban design, and sustainability to advance smart-city solutions. Education : PhD, University of Illinois, Chicago (2011); MS Civil Engineering, Sharif University of Technology (2005); BS Civil Engineering, Sharif University of Technology (2003). His research focuses on travel behaviour analysis, activity-based travel demand modelling, integrated land use and transportation models, and autonomous driving technologies. He leads the rCITI Travel Behaviour Modelling Team, which includes 3 Post-docs, 7 PhD, and 3 MSc students. Recent work explores shared autonomous vehicles, social media data integration for transport models, and dynamic ride-sharing systems. His publications span topics like pedestrian demand modeling, residential relocation dynamics, and pandemic-related travel restrictions. Scientific Awards Fred Burggraf Award (TRB, 2008) Dwight Eisenhower Fellow (2008) ASCE Freeman Fellowship (2009) NSERC PDF Award (2012) Industrial RAND Fellowship (2012) Vice Chancellor’s Award for Teaching Excellence (2015, Team) Award for Engineering Education Engagement (2015, Team) Outstanding Paper (TRB Analytics Contest, 2017) He has secured over $1.2 million in research funding since 2007, including ARC DECRA and Linkage Grants. His teaching includes courses on geometric design, urban transport modeling, and transport econometrics.

MEHMET İSHAK YÜCEView profile

Professor

• Hydrology
• Fluid Mechanics
• Drought Modeling
• +4 more

Mehmet İshak Yüce is a Professor in the Department of Hydraulics within the Faculty of Engineering at Gaziantep University. His career spans over 30 years, starting as a Research Assistant in 1993 and achieving professorship in 2021. He holds a PhD in Civil Engineering from the University of Manchester (2005), an MSc in Water Engineering from Istanbul Technical University (1995), and a BSc in Civil Engineering from Middle East Technical University (1992). Research Focus: Dr. Yüce specializes in hydrology, fluid mechanics, and climate impacts on water systems. His work integrates computational modeling, statistical hydrology, and sustainable resource management. Key themes include: Drought prediction using copula models and machine learning Hydrokinetic turbine design for renewable energy Hydraulic transients and pollutant transport Climate-driven hydrological variability in Mediterranean basins Awards & Recognition: Gaziantep University 2015 Second Best Doctoral Thesis Award Academic Leadership: He has supervised 6 doctoral and 32 master's students, focusing on hydrology, energy systems, and hydraulic infrastructure. His grants include projects on hydrokinetic turbines (2012-2022) and drought analysis (2015-2019), funded by national agencies. Administrative Roles: Former Dean of Engineering (2021), Institute Director (2020-2023), and multiple terms as Department Deputy Head.

• Civil Engineering
• Water Resources
• Hydrology
• +3 more

Prof. Dr. Bihrat Önöz serves as Head of the Civil Engineering Department and full-time faculty at Işık University's Faculty of Engineering and Natural Sciences. She specializes in water resources engineering with extensive contributions to hydrological modeling and drought analysis across Turkish river basins. Her academic credentials include: 1974-1977: B.Sc. in Civil Engineering, Ege University 1984-1986: M.Sc. in Hydraulics, Istanbul Technical University 1988-1992: Ph.D. in Water Engineering, Istanbul Technical University Research focuses on hydrological extremes, featuring pioneering work in statistical streamflow estimation for ungauged basins, multivariate drought indexing, and climate change impacts on water resources. Her methodologies integrate wavelet analysis, copula-based modeling, and machine learning to address water scarcity challenges in Mediterranean ecosystems. She actively supervises graduate research and leads hydrological projects, with recent work emphasizing seasonal drought prediction and reservoir management under changing climate conditions. Notable supervision includes 58 graduate students across doctoral and master's programs, with thesis topics spanning low-flow analysis, flood frequency modeling, and renewable energy-water nexus studies. Current projects include hydrostatistical flow estimation models for ungauged basins and drought characterization in Eastern Black Sea watersheds.