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Yuexiao DongView profile

Associate Professor

• Sufficient Dimension Reduction
• High-dimensional Data Analysis
• Machine Learning
• +3 more

Yuexiao Dong is an Associate Professor in the Department of Statistics, Operations, and Data Science at Temple University's Fox School of Business and Management. He holds a PhD from Pennsylvania State University (2009) and a Bachelor's in Mathematics from Tsinghua University. His research focuses on sufficient dimension reduction, high-dimensional data analysis, and machine learning. Dr. Dong has been funded by the NSF for his work on 'New Developments in Sufficient Dimension Reduction' and serves as a Gilliland Research Fellow. He is an Associate Editor for the Journal of Systems Science and Complexity since 2015. His research is published in top journals such as the Annals of Statistics and Biometrika . Key interests include developing methodologies for efficient data analysis, particularly in handling high-dimensional and non-Euclidean data. He teaches Quantitative Foundations for Data Science at the undergraduate level. Education: Bachelor’s in Mathematics, Tsinghua University PhD in Statistics, Pennsylvania State University Awards: Gilliland Research Fellow NSF Grant: New Developments in Sufficient Dimension Reduction Dr. Dong’s work bridges statistical theory and applications, emphasizing computational efficiency and real-world relevance. Recent projects include Bayesian model averaging, real-time dimension reduction, and expectile-based regression techniques.

Edward C. RosenthalView profile

Professor

• Game Theory
• Logistics
• Supply Chain Management
• +5 more

Edward C. Rosenthal is a Professor at the Fox School of Business and Management, Temple University, specializing in the Department of Statistics, Operations, and Data Science. He holds a Ph.D. from Northwestern University and has extensive teaching experience in operations management, game theory, and supply chain logistics across undergraduate, graduate, Honors, and Executive MBA programs. His research focuses on game theory applications, logistics optimization, and behavioral decision-making. Rosenthal has received prestigious awards including the Great Teacher Award and Lindback Award for Distinguished Teaching. Education: Ph.D., Northwestern University His research interests span cooperative/noncooperative game theory, mechanism design, logistics systems, and behavioral decision theory. Key contributions include work on interdependent security games, supply chain cost allocation, and pricing models in on-demand services. He authored influential books like The Era of Choice (MIT Press, 2005) and The Complete Idiot’s Guide to Game Theory (2011). Recent articles explore topics such as network security strategies, ride-sharing pricing, and journal ranking methodologies. His work bridges theoretical frameworks with practical applications in logistics, transportation, and market dynamics. Awards: Great Teacher Award, Lindback Award Teaching: Operations Management, Game Theory, SCM Internships Rosenthal’s research also addresses real-world challenges like over-targeting in markets and optimal supply chain policies under disruptions. He collaborates on mechanisms for fair cost allocation in public transit and infrastructure networks.

Bagkavos DimitriosView profile

Associate Professor

• Mathematical Statistics
• Survival Analysis
• Probability Theory Applications
• +7 more

Bagkavos Dimitrios is an Associate Professor in the Department of Mathematics at the School of Science, University of Ioannina. His research focuses on Mathematical Statistics, Survival Analysis, and Probability Theory Applications, with expertise in nonparametric estimation, statistical inference, and kernel methods. He holds a B.Sc. in Mathematics from the University of Ioannina and an M.Sc. in Mathematics (Statistics) from the University of Birmingham, UK. His work emphasizes hypothesis testing, computational statistics, and applications of martingales and Edgeworth expansions. He has developed R packages such as NPHazardRate and asymmetry.measures , and his research has been published in top journals like Journal of Multivariate Analysis and Computational Statistics and Data Analysis . He actively contributes to the academic community by refereeing for journals including Test , Journal of Statistical Planning and Inference , and Communications in Statistics .

Myra CohenView profile

Professor

• Software Engineering
• Search-Based Software Engineering
• Highly-Configurable Software Testing
• +5 more

Myra B. Cohen is a Professor and the Lanh and Oanh Nguyen Chair in Software Engineering at Iowa State University's Department of Computer Science, part of the College of Engineering. She previously held the Susan J. Rosowski Professorship at the University of Nebraska-Lincoln. Her research focuses on software testing of highly-configurable systems, search-based software engineering, and applications in synthetic biology and bioinformatics. Cohen holds a Ph.D. from the University of Auckland, M.S. from the University of Vermont, and B.S. in Agriculture from Cornell University. Education: Ph.D., Computer Science, University of Auckland, 2004 M.S., Computer Science, University of Vermont, 1999 B.S., School of Agriculture and Life Sciences, Cornell University Research Interests: Testing of cyber-physical systems and scientific software Combinatorial design applications in software assurance Synergies between software engineering and synthetic biology Configuration-aware testing for safety-critical systems Recent Projects: OPERA: Rural broadband solutions through open-source ecosystems Dependable AI-driven computational biology tools for KBase Blockchain fault tolerance modeling and measurement Self-adaptive mechanisms for unmanned aerial systems Awards: NSF CAREER Award AFOSR Young Investigator Award ACM Distinguished Scientist ASE Conference Fellow Leadership Roles: Program Co-Chair: ESEC/FSE 2020, ISSTA 2025 General Chair: ASE 2015 Steering Committee: ASE, ESEC/FSE, ISSTA Labs & Affiliations: Director: LaVA-OPs (Variability-Aware Assurance Lab) Center for Wireless, Communities and Innovation (WiCI) Bioinformatics & Computational Biology Graduate Program

Oliver EulensteinView profile

Associate Professor

• Computational Biology
• Phylogenetics
• Bioinformatics
• +5 more

Oliver Eulenstein is an Associate Professor at Iowa State University. He holds a Ph.D. in Computer Science from the University of Bonn (1998), an M.S. and B.S. from the University of Paderborn (1991 and 1987). His primary affiliations include the Computational Biology Laboratory. His research focuses on computational complexity, discrete algorithms, and computational biology, particularly phylogenetics. He explores topics such as phylogenetic networks, gene tree reconciliation, and genomic duplication models. Key research contributions include developing algorithms for phylogenetic analysis (e.g., RF-Net 2 for virus reassortment networks) and tools like Phylo-rs. His work bridges computer science and biology, addressing challenges in evolutionary genomics and biodiversity analysis. He has authored over 60 peer-reviewed articles, with recent emphasis on machine learning applications in phylogenetics and bioinformatics. Professional service includes editorial roles in computational biology journals and conference organization. His lab actively pursues interdisciplinary projects at the intersection of algorithms, data science, and biological systems.

Holger DreesView profile

Professor

• Actuarial Mathematics
• Financial Time Series Modeling
• Extreme Value Statistics
• +3 more

Prof. Dr. Holger Drees is a Professor of Actuarial Mathematics at the University of Hamburg, affiliated with the Faculty of Mathematics, Computer Science and Natural Sciences. He holds a position in the Department of Mathematics, specializing in the ST – Mathematical Statistics and Stochastic Processes research group. His office is located at Bundesstraße 55, Room T15 in Hamburg. He earned his diploma in mathematics from the University of Dortmund (1990), his PhD from the University of Siegen (1993), and his habilitation from the University of Cologne (1998). His research focuses on extreme value theory, actuarial mathematics, financial time series modeling, and non/semiparametric statistics. He is a member of the Hamburger Zentrum für Versicherungswissenschaft (HZV) and serves as an Associate Editor for *Bernoulli* and *Extremes* journals. His recent research emphasizes statistical inference on extreme value dependence structures, cluster-based methods for time series extremes, and dimension reduction techniques for multivariate extremes. His work bridges theoretical advancements in extreme value analysis with practical applications in finance and insurance. Teaching activities include advanced courses on extreme value theory and actuarial mathematics. Professional contributions include editorial roles and collaborative projects on statistical methodologies for extremes. His research has been supported by grants such as the DFG Heisenberg grant (2000–2002). He maintains an active international research network, collaborating with institutions like the University of Cologne and the University of Heidelberg.

Serkan EryilmazView profile

Professor

• Reliability Engineering
• Applied Probability
• Stochastic Modeling
• +3 more

Prof. Serkan Eryilmaz is the current President of Atilim University (since 2023) and previously served as Vice President for Research (2017–2023). He holds a Ph.D. in Statistics from Ankara University (2002) and has held academic positions at Izmir University of Economics and Atilim University. His research focuses on reliability engineering, applied probability, stochastic modeling, and renewable energy systems. He is an Editorial Board member of Reliability Engineering and System Safety and an Area Editor of IISE Transactions . Education: Ph.D. in Statistics (Ankara University, 2002), M.Sc. in Statistics (Ankara University, 2000), B.Sc. in Statistics (Ankara University, 1999). Honors include TUBITAK Science Incentive Award (2017) and inclusion in 'The Most Influential Scientists in the World' (2021, 2020). Research Interests: Reliability analysis of systems, stochastic processes, probabilistic models in renewable energy, actuarial risk analysis, and applied probability. Recent work includes publications on δ-shock models, preventive replacement policies, and wind energy systems. Awards: TUBITAK grants (2017, 2006), top-ranked graduate (Ankara University, 1999), and international recognition for impactful research in reliability engineering. Advising & Grants: Supervised 7 PhD and 8 Master’s theses. Active in editorial roles and professional service, including co-chair of the System Reliability Technical Committee at the European Safety and Reliability Association. Labs/Teams: Leads research groups in reliability engineering and stochastic modeling at Atilim University, collaborating on projects involving renewable energy systems and probabilistic risk analysis.

Laura MagazziniView profile

Associate Professor

• industrial organization
• economics of innovation
• microeconometrics
• +2 more

Laura Magazzini is an Associate Professor of Econometrics at the Institute of Economics, Sant'Anna School of Advanced Studies, specializing in microeconomic data analysis with emphasis on innovation dynamics and competition in the Life Science industry. Her methodological expertise spans linear and nonlinear panel data models, limited dependent variable frameworks, and simulation-based estimation techniques. Her academic credentials include: PhD from Sant'Anna School of Advanced Studies (2004) Graduate degree in Statistics for Economics from the University of Florence Research visits at Carnegie Mellon University (Pittsburgh) and University of Sydney during academic training Magazzini's research integrates industrial organization, innovation economics, and advanced econometrics to examine real-world economic phenomena. She develops and applies sophisticated quantitative methods to analyze micro-level data, particularly focusing on pharmaceutical innovation, migration economics, and household labor dynamics. Her interdisciplinary approach bridges theoretical econometrics with empirical industrial organization. Recent publications reveal a consistent focus on methodological innovation applied to substantive economic questions, particularly in health economics and industrial dynamics. Her work frequently employs endogenous-switching models and dynamic panel frameworks to address identification challenges in microeconomic datasets, with strong representation in top journals like The Economic Journal and Management Science . No scientific awards, student advising records, grant details, or laboratory affiliations were documented in the provided materials.

• Statistical Learning
• Machine Learning
• Probability
• +6 more

Stephan Clémençon is a Professor at Télécom Paris, working within the Information Processing and Communication Laboratory (LTCI) where he leads the Signal, Statistics and Learning (S2A) research team. His academic career spans multiple prestigious institutions including University of Paris X (2000-2005) and INRA Met@risk research unit (2005-07), with membership in the LPMA (Stochastic Modeling and Probability) laboratory of Paris 6 and Paris 7. Clémençon earned his PhD in Applied Mathematics from University of Paris 7 Denis Diderot with a visiting period at Stanford University's Department of Statistics (1997-1998). He currently serves as the responsible for the Specialized Master's in 'Big Data' at Télécom Paris and previously held the 'Machine Learning for Big Data' industrial chair (2013-2018), now actively involved in the 'Data Science and AI for Digitalized Industry and Services' chair. His research interests focus on statistical learning, probability, statistics, machine learning, stochastic processes, and nonparametric statistics, with applications spanning quantitative finance, biosciences, and signal/image processing. Clémençon has developed an 'immoderate taste for stochastic modeling and statistics applied in various application areas.' His extensive publication record (270 documents in HAL) demonstrates consistent research productivity with 15 recent publications (2020-2024) primarily focused on anomaly detection, ranking algorithms, statistical learning theory, and bias correction in machine learning. His work bridges theoretical statistics with practical applications in computer vision, telecommunications, and ethical AI considerations. Clémençon teaches advanced courses including Martingale Theory, Machine Learning, Advanced Nonparametric Statistics, and Big Data Projects at Télécom Paris, while also contributing to programs at Ensae Paris, Paris Diderot University, ENS Paris Saclay, and other institutions. His research team (S2A) operates within the Image, Data, Signal (IDS) department at Télécom Paris, focusing on the intersection of signal processing, data science, and statistical learning methodologies.

• Probability Theory
• Multivariate Statistics
• Gamma Distributions
• +2 more

Philippe Bernardoff is an Associate Professor at the University of Pau and the Pays de l'Adour. His research focuses on advanced probabilistic models, including multivariate gamma distributions, negative multinomial laws, and Laplace transform applications in statistical dependence structures. His work bridges theoretical probability and practical applications in areas like polarimetric image processing. Bernardoff's research interests center on developing statistical methodologies for complex multivariate systems, with emphases on distribution theory, simulation algorithms, and copula-based dependence modeling. His publications consistently explore the mathematical frontiers of infinitely divisible distributions and their computational implementations. His recent articles demonstrate a strong focus on advancing simulation techniques for gamma distributions and expanding the theoretical understanding of Laplace transforms in multivariate contexts. This work has implications for high-dimensional data analysis and stochastic modeling.

Yuri GoegebeurView profile

Associate Professor

• Extreme Value Theory
• Statistical Modeling
• Actuarial Science
• +5 more

Yuri Goegebeur is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, affiliated with the SDU Climate Cluster. His research focuses on extreme value theory, statistical modeling, and actuarial science, particularly in areas like reinsurance pricing, tail risk analysis, and censoring data methods. He has contributed extensively to methodologies for estimating extreme quantiles, tail dependence functions, and risk measures under various conditions. His work integrates advanced statistical techniques with applications in finance, insurance, and environmental risk assessment. Key research interests include extreme value analysis, robust estimation techniques, and the development of risk measures for time series and censored datasets. He has published over 80 articles in peer-reviewed journals and serves on editorial boards for journals like Extremes and Psychometrika . His collaborative projects involve analyzing extreme weather impacts on healthcare systems and advancing multivariate regression models for extreme values. Recent publications emphasize conditional tail moment estimation, dependent risk measures, and applications in reinsurance. His research bridges theoretical statistics with practical challenges in actuarial science and environmental modeling. He has co-led projects funded by institutions like Villum Fonden, focusing on extreme value methodologies for real-world problems such as climate-related health risks.

Pujee TuvaandorjView profile

Assistant Professor

• Econometrics
• Statistical Inference
• Microeconometrics
• +3 more

Pujee Tuvaandorj is an Assistant Professor in the Department of Economics at York University, part of the Faculty of Liberal Arts & Professional Studies. His research focuses on econometric methods, particularly robust inference under weak identification and randomization techniques. He holds a Ph.D. from McGill University (2015), an M.A. from Hitotsubashi University (2009), and a B.A. from Kyoto University (2007). His work bridges theoretical econometrics and applied microeconometric models. Research interests span econometric theory, including permutation tests for linear models, instrumental variables analysis, and robust statistical methods. He also explores structural breaks, time series econometrics, and asymptotic theory. His recent work emphasizes methodological advancements in handling weak identification and serial dependence in econometric models. Teaching responsibilities include courses such as Introductory Statistics for Economists II , Financial Econometrics , and Econometric Theory . His publications in top journals like Quantitative Economics and Journal of Econometrics reflect his expertise in econometric inference and model development. No scientific awards are explicitly mentioned in the provided texts. His research portfolio includes contributions to regression discontinuity designs, generalized method of moments (GMM), and invariant tests. Current projects address digital adoption, cyber security, and labor market impacts on homelessness, leveraging Canadian administrative data.

Takashi OwadaView profile

Associate Professor

• Extreme Value Theory
• Heavy Tail Probability
• Hyperbolic Geometry
• +8 more

Takashi Owada is an Associate Professor in the Department of Statistics at Purdue University , with a courtesy appointment in the College of Science and Mathematics. His research bridges probability theory and topological data analysis , focusing on understanding the structure of complex stochastic systems through geometric and topological methods. Education Ph.D. , Operations Research (Applied Probability & Statistics concentration), Cornell University, 2013 M.A. , Economics, University of Tokyo, 2004 B.A. , Economics, University of Tokyo, 2002 Research Interests Owada’s work lies at the intersection of extreme value theory , heavy-tailed processes , and random topology , with applications to topological data analysis and random geometric graphs . He investigates limit theorems, large deviation principles, and persistent homology in systems with hyperbolic geometry and long-range dependence . His research also extends to infinitely divisible processes and stable stochastic models in high-dimensional settings. The trends in Owada’s recent work emphasize the interplay between algebraic topology and probability theory to analyze the structure of complex networks using topological invariants and geometric functionals . His studies often incorporate extreme-value behavior and heavy-tailed distributions in random simplicial complexes , hyperbolic graphs , and stochastic point processes . Scientific Awards Regina and Norman F. Carroll (Col. USAF) Research Award, 2021, 2023 Department of Statistics Outstanding Assistant Professor Teaching Award, 2021 Excellent Research Paper Award, 2018 Korean Mathematical Society Best Paper Award, 2018 Institute of Mathematical Statistics (IMS) Travel Award, 2014 Grants and Teaching Owada has secured significant funding, including the Air Force Office of Scientific Research (AFOSR) grant for studying complex stochastic networks (2022–2025) and a National Science Foundation (NSF) grant on Random Algebraic Topology (2018–2022). His teaching portfolio includes graduate and undergraduate courses such as Probability Theory , Elements of Stochastic Processes , and Basic Probability and Applications , reflecting his expertise in probability and stochastic modeling.

Alexandra DiasView profile

Professor

• Quantitative Risk Management
• Insurance Finance
• Copula Models
• +4 more

Alexandra Dias is a Professor of Finance and Actuarial Science at the University of York, affiliated with the School for Business and Society and the Department of Accounting and Finance. She previously held roles at the University of Leicester and University of Warwick. She earned her PhD from ETH Zurich, with additional degrees from Universidade de Lisboa and Universidade Nova de Lisboa. Roles: Co-programme Leader for BSc Actuarial Science, Fellow of Advance HE, Member of the Pensions Gap Working Party (Institute and Faculty of Actuaries). Educations: PhD in Finance from ETH Zurich, MSc from Universidade de Lisboa, Licenciatura from Universidade Nova de Lisboa. Her research focuses on quantitative risk management, insurance finance, copula models for multivariate dependence, and extreme events analysis. Recent work includes studies on pension gaps, reinsurance impacts, and digital accessibility in academia. She has contributed to journals like Risks, Journal of Banking and Finance, and Quantitative Finance . Key articles explore topics such as copula-based risk aggregation (2025), pensions policy (2024), and stop-loss reinsurance effects (2022). Her work bridges theoretical finance with practical applications in insurance and policy. Awards: Fellow of Advance HE. Grants/Advising: Editor for European Journal of Finance , guest editor for Risks , and active in interdisciplinary research collaborations. Labs/Teams: Part of the York Management School’s actuarial science and finance research groups.

• Formal Verification
• Model Checking
• Autonomous Systems
• +7 more

Professor Alice A Miller is a faculty member at the University of Glasgow's School of Computing Science, where she has held roles including Postdoc, Daphne Jackson Fellow, Lecturer, and Senior Lecturer since 1997. Prior to this, she worked at the Universities of East Anglia, Western Australia, and Stirling. She currently holds a Leverhulme Research Fellowship and is a Chartered Engineer with the IET. Her membership in the London Mathematical Society reflects her interdisciplinary background. Education: Alice Miller holds a PhD in Number Theory, though her research career has primarily focused on Formal Verification and Model Checking. Her academic trajectory includes leveraging mathematical techniques to analyze systems across engineering and computer science disciplines. Research interests: Her work centers on applying formal verification methods to ensure safety and reliability in complex systems such as autonomous vehicles, robots, and telecommunications. Key techniques include model checking and symmetry reduction. Specific areas include: Modelling and Verification of Concurrent Systems Abstraction and Symmetry Reduction Methods Graph Theory Applications in System Analysis Combinatorics and Group Theory for Optimization Recent publications highlight advancements in overtake planning, sensor network protocols, and probabilistic system analysis. Scientific Awards: Daphne Jackson Fellowship, Leverhulme Research Fellowship, and Chartered Engineer status from the IET. Grants and Advising: Alice has led or co-investigated multiple grants totaling over £3 million, including projects on digital twins, autonomous systems governance (£2.7M), and secure memory management (M4Secure). She has advised on student-led initiatives like UAV demonstrators and overtaking planning simulations, though specific advisee names are not listed in the provided text. Labs/Teams: Collaborates with interdisciplinary teams on projects involving robotics, sensor networks, and formal verification tools such as PRISM and SPIN. Her work frequently intersects with engineering and mathematics groups within the university and industry partners like D-RisQ Ltd.