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.
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.
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 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.
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.
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 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 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 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 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.
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.
Noirrit Kiran Chandra is an Assistant Professor of Statistics at the Department of Mathematical Sciences within the School of Natural Sciences and Mathematics at The University of Texas at Dallas. His research focuses on developing novel statistical methods for high-dimensional data analysis, Bayesian computation, and applications in genomics and neuroscience. He holds a Ph.D. in Statistics from the Indian Statistical Institute, Kolkata (2019), and postdoctoral experience at Duke University (2019–2020) and The University of Texas at Austin (2020–2022). Education: Ph.D. in Statistics, Indian Statistical Institute, Kolkata (2019) M.Stat., Indian Statistical Institute, Kolkata (2013) B.Sc. in Statistics, Calcutta University (2011) Research Interests: Graphical models and covariance structure inference Clinical trial design and real-world data integration Bayesian asymptotics and scalable computation High-dimensional clustering and multiple hypothesis testing Applications in genomics, neuroscience, and public health Grants: Seed Program for Interdisciplinary Research (SPIRe) Grant ($60,000), UTD (2024) New Faculty Research Symposium (NFRS) Grant ($25,000), UTD (2023) Awards: ISBA Biostats and Pharma Junior Researcher Award (2023) Travel awards from ISBA (2022) and IBCASC (2018) Student Poster Award, International Indian Statistical Association (2017) His work includes developing open-source statistical tools such as the SUFA and CAPPMx packages for subspace factor analysis and scalable Bayesian computation. He collaborates with researchers in psychology and biomedical sciences to advance interdisciplinary research.
Phil Hayes is an Assistant Professor in the Sport Exercise and Rehabilitation Department at Northumbria University, where he has been the longest serving member of the sport science staff since joining in 1991. He currently serves as admissions tutor and has previously held the position of Programme Leader for 14 years. Dr. Hayes is a BASES accredited Sport and Exercise Scientist and Chartered Scientist with extensive experience in sports physiology. PhD in Sports Science (awarded October 9, 2015) MSc in Sports Science (Education) (awarded August 31, 1989) Dr. Hayes' research focuses on enhancing sport performance, particularly in middle and long-distance running. His primary interests include the effect of fatigue on running gait (especially the role of muscle strength), aerobic interval training, quantifying training loads, and the relative age effect. His work combines laboratory-based research with practical applications in elite sports settings. Analysis of his recent publications (2020-2025) reveals a strong focus on running biomechanics, training methodology, and coaching practices. His research increasingly incorporates advanced statistical methods like multilevel functional regression models to analyze complex movement patterns. There's a clear trend toward interdisciplinary work combining physiology, biomechanics, and coaching science to optimize athletic performance. BASES accredited Sport and Exercise Scientist Chartered Scientist UK Athletics Level 4 middle-distance running coach BASES Accredited Physiologist Former accredited anthropometrist with ISAK Dr. Hayes supervises PhD students including Sherveen Riazati (researching fatigue effects on running gait) and Arran Parmar (investigating acute responses to aerobic interval training). He has provided sport science support for Premier League football teams, professional cricket teams, and international runners, including athletes involved in Dame Kelly Holmes' 'On Camp with Kelly' scheme. His applied work extends to coaching, where he serves as a UK Athletics Level 4 middle-distance running coach for a local athletics club, having coached multiple GB age-group teams. His research group focuses on Optimising Human Performance, with particular expertise in velocity, strength, fatigue, and muscle strength applications in sports contexts.
Hamid Arian is an Assistant Professor in the School of Administrative Studies at York University, specializing in finance. His research focuses on quantitative methods in finance, including derivatives pricing, risk management, and machine learning applications in algorithmic trading. Education: PhD in Mathematical Finance, University of Toronto MSc and BSc in Pure Mathematics, Sharif University of Technology CFA charter from CFA Institute, USA FRM charter from GARP, USA Arian's research spans environment and sustainability management, derivatives securities, risk management, financial AI, algorithmic trading, and fintech. His recent publications demonstrate extensive work on portfolio optimization, neural networks for financial modeling, and advanced statistical methods for risk assessment. Articles predominantly explore machine learning innovations in finance, showing consistent focus on robust modeling techniques for derivatives pricing, risk quantification, and algorithmic trading systems.
Dr. Ewa Krzyszczyk is a Lecturer in Zoology at Bangor University's School of Environmental and Natural Sciences. She serves as the Director of Student Engagement and Director of the BSc/MSc Zoology with Marine Zoology program, while also co-managing the BSc Zoology with Foundation Year. Her academic journey began at Bangor University where she earned her BSc in Zoology with Marine Zoology (2003) and MSc in Marine Mammal Science (2005), followed by a PhD in behavioral ecology from Georgetown University (2013). Her educational background includes: PhD in Age determination, life history and juvenile behavior in bottlenose dolphins (Tursiops sp.) in Shark Bay, Australia, Georgetown University (2013) MSc in Marine Mammal Science, Bangor University (2005) BSc in Zoology with Marine Zoology, Bangor University (2003) Dr. Krzyszczyk's research is centered in behavioral ecology, with a focus on understanding how behavior changes with environment, age, disease, and anthropogenic effects. She applies novel approaches to quantify these interactions and their consequences within and between ecosystems. Her work spans marine mammal behavior, particularly bottlenose dolphins at Shark Bay and St. Johns River, as well as reef health assessment in the Turks and Caicos Islands where she studied fishing pressure, Black Spot Syndrome effects, and lionfish culling management. Her recent publications demonstrate consistent focus on bottlenose dolphin social behavior, disease transmission, and conservation. The articles reveal strong patterns in studying social networks, mortality risks, and behavioral adaptations across different environmental conditions. Her work bridges theoretical behavioral ecology with practical conservation applications, particularly in marine ecosystems. Among her scientific recognitions is a Teaching Fellowship awarded in 2023. She has published extensively on bottlenose dolphin behavior, social structures, and conservation issues. As an educator, Dr. Krzyszczyk teaches across multiple modules including Practical Skills, Field Trips, Invertebrates, and Dissertation in Biological Sciences. She is module organizer for Science Communication and Essential Biology. Her teaching philosophy emphasizes student engagement and experiential learning, believing that 'involve them and they will understand.' She has also taught Marine Resource Management, Directed Research, Tropical Marine Ecosystems, and Marine Megafauna Ecology while working with The School for Field Studies. Dr. Krzyszczyk maintains active research collaborations across institutions and her work contributes to UN Sustainable Development Goals related to marine conservation and environmental protection.
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