Marcel Scharth is a Lecturer in Business Analytics at the University of Sydney Business School and affiliated with the Centre for Translational Data Science. He holds a Ph.D. from VU University Amsterdam (2012) and has conducted postdoctoral research at the University of New South Wales. His expertise spans Bayesian methods, computational statistics, machine learning, and financial econometrics. Research focuses include high-dimensional stochastic volatility models, Monte Carlo methods, and Bayesian machine learning applied to time series and longitudinal data. He teaches courses like QBUS2810 (Statistical Modelling), QBUS2820 (Predictive Analytics), and QBUS5001 (Quantitative Methods for Business), emphasizing statistical reasoning and computational skills. His work has been published in top journals such as the Journal of Econometrics and the Review of Economics and Statistics. He actively engages in AI discourse, contributing to media articles on prompt engineering for generative AI and the limitations of models like ChatGPT. Professional activities include Effective Altruism collaborations and open-source contributions via GitHub repositories like forecasting and Machine Learning for Business .
Pietro D'Antuono is a postdoctoral researcher affiliated with the Applied Mechanics Acoustics & Vibration Research Group. His research focuses on fatigue analysis, structural health monitoring, and offshore wind turbine systems. He has contributed to advancing methodologies for data-driven lifetime assessment of support structures and foundations, integrating physics-informed machine learning and probabilistic modeling. His work bridges mechanical engineering, civil engineering, and renewable energy systems. Research Interests: Offshore wind turbine structural dynamics Fatigue and damage mechanics Data-driven predictive models Structural health monitoring Materials degradation analysis Key Contributions: Developed the py-Fatigue open-source toolbox for fatigue assessment. Authored 24+ peer-reviewed publications and datasets on structural reliability and offshore energy systems. Awards: Best Paper Award (2nd place, ex-aequo) at an international conference (2022). Professional Activities: Presenter at conferences on topics like fatigue modeling, data-driven methodologies, and structural health monitoring. Contributed to international research collaborations in Europe and beyond.
Associate Professor David Lee is an academic at the University of the Sunshine Coast (UniSC), specializing in plant genetics and forest research. He leads genetic improvement programs for tree species like Acacia, Corymbia, Eucalyptus, and Santalum, focusing on disease resistance, biomass assessment, and carbon sequestration. His work has influenced hardwood plantation development in Australia, South Africa, and Brazil. He holds a PhD in Plant Genetics from James Cook University and a BAgricSc(Hons) from the University of Melbourne. Lee’s research integrates molecular technologies (e.g., resistograph, NIR spectroscopy) to enhance tree breeding efficiency. He manages over $9 million in grants from bodies like ACIAR, FWPA, and MLA. His projects include Myrtle rust resistance screening, sandalwood domestication for Indigenous communities, and carbon sequestration modeling in tropical forests. He has supervised 10+ PhD students, contributing to studies on Corymbia hybrids, pest-pathogen interactions, and forest biomass estimation. Current projects emphasize ginger disease resistance and bioenergy potential from sandalwood residues. Lee collaborates with institutions like DAF, CSIRO, and international partners, advancing sustainable forestry practices globally. His expertise spans forest genetics, agroforestry, and environmental adaptation, with over 30 peer-reviewed papers and book chapters. He is affiliated with UniSC’s Forest Industries Research Centre and Tropical Forests and People Research Centre, driving interdisciplinary research to address climate change and biodiversity challenges.
Professor Ralf Werner serves as Professor of Business Mathematics at the University of Augsburg, where he leads the Computational Statistics and Data Analysis working group within the Institute of Mathematics at the Faculty of Mathematics, Natural Sciences and Technology. His academic career spans both theoretical research and practical industry applications in quantitative finance. Werner's research interests encompass: Computational Statistics and Data Analysis Optimization under Uncertainty Financial Engineering and Risk Management Actuarial Science and Insurance Mathematics Portfolio Optimization and Asset Allocation His scholarly output demonstrates a consistent focus on robust mathematical methods applied to financial problems, particularly in replicating portfolios for insurance applications, credit risk modeling, and statistical approaches to financial risk management. Werner's publications appear in leading journals across operations research, mathematical finance, and actuarial science. Professional qualifications include his habilitation at the Karlsruhe Institute of Technology (2011) and doctorate from Friedrich-Alexander University Erlangen (2001). He maintains active industry connections through his role as Scientific Advisor for DEVnet since 2010. Werner serves as Internship Coordinator and DAV (German Actuarial Society) correspondent, supporting students pursuing actuarial careers. He is an active member of multiple professional organizations including the Society for Operations Research (GOR), German Mathematical Society (DMV), and German Society for Insurance and Financial Mathematics (DGVFM).
Kehan Gao serves as a Professor in the Department of Computer Science at Eastern Connecticut State University, teaching Software Engineering, Databases and Information Management, and Data Structures and Algorithms courses. Her research focuses on: Software Engineering and Reliability Software Quality Engineering Data Mining & Machine Learning Computational Intelligence Software Metrics With over 80 refereed publications, she specializes in software defect prediction using feature selection and data sampling techniques to address class imbalance. Recent work (2014-2025) extends these methodologies to Mars image classification and COVID-19 severity assessment, demonstrating cross-domain applicability of her ensemble learning approaches. No scientific awards were documented in available materials. Information regarding student advising, research grants, and laboratory affiliations was not provided in the source text.
Prof. Dr. André Uschmajew is a full professor and holds the Chair of Mathematical Data Science at the Institute of Mathematics, Faculty of Mathematics, Natural Sciences, and Materials Engineering, University of Augsburg, Germany. He has held prominent research and academic positions at institutions including the Max Planck Institute for Mathematics in the Sciences (Leipzig), University of Bonn, and EPF Lausanne. 2022–present: Chair of Mathematical Data Science, University of Augsburg 2017–2022: Research Group Leader, Max Planck Institute MiS Leipzig 2014–2017: Bonn Junior Fellow Professorship, University of Bonn 2013: Ph.D. in Mathematics, TU Berlin His research centers on the theoretical and computational aspects of low-rank tensor and matrix approximations, with deep connections to Riemannian optimization, functional analysis, and high-dimensional scientific computing. He investigates the geometry of low-rank varieties, convergence of alternating algorithms, and applications in data science and dynamical systems. His work combines rigorous mathematical analysis with algorithmic innovation. The recent publications (2023–2025) reflect a strong focus on optimization methods for low-rank structures, dynamical low-rank approximation for PDEs like the Vlasov-Poisson equation, randomized SVD, Sinkhorn-type algorithms with overrelaxation, and Kronecker product operator approximation. Key themes include convergence analysis, algorithmic acceleration, and applications in scientific computing and signal processing. Although no specific awards are listed, his publication record in top-tier journals such as Numerische Mathematik , SIAM Journal on Optimization , and Foundations of Computational Mathematics indicates significant recognition in applied mathematics and numerical analysis. He advises students and researchers in mathematical data science and numerical analysis, though specific advisees are not named. He teaches courses such as Kernel Methods and Linear Algebra II. He has collaborated with leading researchers including Bart Vandereycken, Daniel Kressner, and Wolfgang Hackbusch. His work is supported through institutional affiliations and likely research grants, though specific grants are not listed. He is actively involved in the development of numerical methods for high-dimensional problems, particularly using tensor networks and manifold optimization. He is affiliated with research teams at the University of Augsburg and previously led a group at the Max Planck Institute MiS Leipzig, focusing on mathematical aspects of data science and tensor methods.
David Long is Professor of Paediatric Nephrology at University College London's Great Ormond Street Institute of Child Health (GOSICH). He serves as Head of the Developmental Biology and Cancer Department, co-leads the UCL Centre of Kidney and Bladder Health, and acts as Deputy Theme Lead of the Gene, Cell and Stem Theme at the Great Ormond Street Biomedical Research Centre. With over 80 publications cited more than 6,000 times and an H-index of 42, his research focuses on understanding kidney disease mechanisms to develop novel therapies for patients. David Long earned his Doctor of Philosophy from University College London in 2003 and his Bachelor of Science (Honours) from the University of Southampton in 1999. His initial research experience was gained in the Nephro-Urology Unit at GOSICH under Professor Adrian Woolf as an MRC-funded PhD student. Professor Long's research mission centers on understanding mechanisms underlying kidney disease in children and adults to translate findings for patient benefit. His laboratory combines experimental models of kidney disease using zebrafish, transgenic mice, and patient samples with innovative technologies including three-dimensional imaging, mathematical modeling, gene editing, stem cell technology, and novel therapeutic approaches. His work is particularly important given the UK has 70,000 patients with end-stage kidney disease requiring dialysis or transplantation, with an annual cost of the UK ESKD programme conservatively estimated at £1 billion. Analysis of Professor Long's recent publications reveals a strong focus on kidney lymphatic vessels, polycystic kidney disease, and glomerular pathology. His work increasingly utilizes advanced technologies like single-cell transcriptomics, three-dimensional imaging, and microfluidic organ-on-chip platforms. A significant theme is the investigation of lymphatic vessel function in kidney health and disease, particularly in polycystic kidney disease and transplant rejection. His research also explores novel therapeutic approaches including vincristine for podocyte damage and cardiotrophin-1 for glomerular disease, demonstrating the interdisciplinary nature of his work bridging basic science with clinical applications. Wellcome Trust Investigator Award (2020-2025) Medical Research Council New Investigator Award (2012) Kidney Research UK Senior Non-Clinical Fellowship (2008-2014) UCL Bogue Research Fellowship MRC-funded PhD Professor Long has supervised seventeen PhD students (ten as primary supervisor) and managed eleven postdoctoral fellows, three research assistants, and a Wellcome Trust MD/PhD clinical fellow. His research group has expanded to over fifteen members, making him one of the few non-clinical scientists leading a renal group in the UK. His funding includes grants from the MRC, Kidney Research UK, Diabetes UK, Kids Kidney Research, and the GOSICH Children's Charity. He has also been a member of the Kidney Research UK Research Grants Committee (2014-22) and currently serves as an academic editor for PLoS One and on the Journal of the American Society of Nephrology editorial board. Professor Long leads the Kidney Development and Disease Group (KDD) at UCL Great Ormond Street Institute of Child Health, a team of clinicians and scientists with the ultimate aim to develop new therapies for patients with kidney disease. He also co-established and co-leads the UCL Centre of Kidney and Bladder Health. His laboratory has grown to over fifteen members, focusing on innovative approaches to understand and treat kidney diseases, with current work supported by a Wellcome Trust Investigator Award examining how lymphatic vessels grow, work, and communicate with other cells in growing or diseased organs.
Hammou Elbarmi is a Professor at the Paul H. Chook Department of Information Systems and Statistics within the Zicklin School of Business at Baruch College, CUNY. His academic journey includes a PhD in Statistics from the University of Iowa (1993), an MS in Statistics (1989), a DEA in Applied Mathematics from University Mohamed V, Morocco, and a BS in Applied Mathematics from the same institution. PhD, Statistics, University of Iowa (1993) MS, Statistics, University of Iowa (1989) DEA, Applied Mathematics, University Mohamed V, Morocco BS, Applied Mathematics, University Mohamed V, Morocco Elbarmi specializes in Order Restricted Inference , Survival Analysis , and Categorical Data Analysis . His research focuses on statistical methods for comparing survival and cumulative incidence functions under stochastic ordering constraints, with applications to competing risks and biased sampling. He has developed novel nonparametric estimation techniques for distributions with type I/II bias and pioneered empirical likelihood approaches for hypothesis testing under inequality constraints. His work spans over two decades of funded research projects from PSC-CUNY , including grants for "Nonparametric estimation under stochastic precedence" (2020-2022) and "Consistent estimation of survival functions under uniform stochastic ordering" (2023-2024). He has delivered over 30 presentations at institutions like Rice University, University of Iowa, and Joint Statistical Meetings. Elbarmi has received the Faculty Scholarship and Creative Achievement Award at Baruch College annually from 2003-2011 and served as a Co-Chair for the Seminar Series in Statistics & Operations Research. His editorial and peer-review roles include associate editorships for journals like Statistica Sinica and Journal of Nonparametric Statistics .
Tracy L. Kijewski-Correa is the William J. Pulte Director of the Pulte Institute for Global Development and Academic Director of the Integration Lab (i-Lab) at the University of Notre Dame's Keough School of Global Affairs. She holds a joint appointment as Professor of Civil Engineering and Global Affairs in the Department of Civil and Environmental Engineering & Earth Sciences. Her research focuses on civil infrastructure challenges in both developed and developing contexts, with particular emphasis on natural hazard assessment and mitigation. A co-founder of Engineering2Empower (E2E), she has developed comprehensive strategies for sustainable housing in Haiti, integrating community-led innovation and market-based solutions. Her work spans multiple regions including Haiti, United States, Latin America, Middle East, South Asia, and South Pacific/Oceania. Kijewski-Correa's recent publications demonstrate a clear trend toward leveraging open data and computational approaches for high-fidelity regional loss estimations, particularly for hurricane risk assessment. Her work increasingly integrates computer vision, Bayesian data integration, and component-level fragility modeling to advance the precision of disaster impact predictions while maintaining computational tractability across large building portfolios. Kellogg Institute Faculty Fellow (since 2011) Her research has been supported by multiple grants including projects focused on climate adaptation in Bangladesh and Haiti, community-driven development in Haiti, post-disaster sheltering programs, and technology incubators for sustainable development. As director of the Pulte Institute and Academic Director of the i-Lab, she leads interdisciplinary teams working at the intersection of engineering, global development, and disaster resilience.
Professor Li-Chun Zhang is a faculty member in Social Statistics at the University of Southampton. His research focuses on graph sampling, analysis of integrated data, statistical uses of administrative sources, population size estimation, and machine learning. Research Interests Graph sampling Analysis of integrated data Statistical uses of administrative sources Population size estimation Machine learning Recent Research Trends Recent publications highlight his work on improving road safety data in Oman, census methodologies, graph spatial sampling, categorical linkage-data analysis, and robust capture-recapture techniques using administrative data. His research emphasizes statistical innovation in multisource data integration and machine learning applications. Supervision Jillian Delaney (PhD Social Stats & Demo PT) Luciano Perfetti Villa (PhD Social Stats & Demo) Dadi Liu (PhD Social Stats & Demo) Contact Email: L.Zhang@soton.ac.uk
Hervé Cardot is a Professor of Statistics and team leader of the Statistics, Probability, Optimization and Control (SPOC) group at the Institut de Mathématiques de Bourgogne (IMB) , University of Burgundy. His research spans functional data analysis, stochastic algorithms for robust estimation, survey sampling methodology, and applied statistics across diverse fields. Research Interests : Functional data analysis, online principal component analysis, zero-inflated regression, survey sampling, nonparametric estimation Key Applications : Agriculture, climatology, energy consumption, food science, remote sensing, medical imaging Recent Work includes: Developing statistical frameworks for analyzing categorical trajectories using multivariate functional PCA (2025) Advancing bias-robust estimation for functional data in survey sampling (2020) Creating online stochastic algorithms for high-dimensional robust estimation (2017) Applying semi-Markov models to sensory analysis (2018) Teaching at the University of Burgundy includes courses on statistical modeling, big data analysis, and applied statistics for economics and psychology. He supervises PhD students in statistics and co-edits Statistics and Probability Letters .
Hillel J. Bavli serves as Associate Professor of Law at Southern Methodist University's Dedman School of Law and maintains affiliation with the Institute for Quantitative Social Science. His academic focus bridges legal theory and quantitative methodology, specializing in evidence law, torts, complex litigation, and empirical legal studies with particular emphasis on statistical applications in judicial contexts. His educational trajectory includes a B.A. in Economics from Boston University, J.D. from Fordham University School of Law, LL.M. and A.M. in Statistics from Harvard University, and Ph.D. in Statistics in Law and Government from Harvard. Additional distinctions include a Fulbright Fellowship for game theory research in Jerusalem, clerkships at the Supreme Courts of India and Rwanda, and fellowships at Harvard's Institute for Quantitative Social Science and Yale Law School's Center for Private Law. Professor Bavli's research program centers on redefining evidence law through statistical frameworks, notably developing the "Incongruence Principle of Evidence" and "Aggregation Theory of Character Evidence." His scholarship critically examines how implicit bias permeates character evidence usage while advancing methodological rigor in legal statistics. Current work includes a Cambridge University Press book contract on character evidence and empirical studies challenging traditional evidence rules. Analysis of his 15 most recent publications reveals consistent thematic evolution: early work established statistical foundations for damages assessment (2015-2017), mid-career research expanded into causation theory and antidiscrimination law (2018-2021), and recent scholarship pioneers character evidence reform through behavioral insights and rule reinterpretation (2022-2025). The interdisciplinary nature spans evidence law, torts, civil procedure, and statistical methodology with increasing focus on bias mitigation. Scientific recognition includes: Fulbright Fellowship for game theory research Harvard Institute for Quantitative Social Science fellowship Yale Law School Center for Private Law visiting fellowship Numerous teaching awards for Harvard's economic analysis of law seminar Professor Bavli actively shapes legal practice through committee leadership on the American Statistical Association's Forensic Science Advisory Committee and AALS Evidence Section Executive Committee. His government consultation for DNA testimony standards reform demonstrates real-world impact. As an experienced litigator at Akin Gump and Boies Schiller Flexner, he brings practical insights to classroom instruction in evidence, torts, and law/statistics. Though specific advisees aren't documented, his role inherently involves mentoring law students in empirical research methods and evidence theory. His academic ecosystem includes collaboration with statistical associations, government forensic science initiatives, and interdisciplinary research teams advancing evidence-based legal reforms. Current projects focus on character evidence modernization and empirical validation of judicial procedures through randomized controlled trials.
Dr. Ben Tscharke is a Senior Research Fellow at the Queensland Alliance for Environmental Health Sciences (QAEHS), part of the University of Queensland's Faculty of Health, Medicine and Behavioural Sciences. He joined QAEHS in February 2017 after completing his PhD at the University of South Australia. His primary research focus involves wastewater-based epidemiology to monitor community consumption and exposure to illicit drugs, pharmaceuticals, and personal care products across Australia. Dr. Tscharke leads the Australian Criminal Intelligence Commission's National Wastewater Drug Monitoring program at UQ, collaborating with the University of South Australia. His work has established Australia as a leader in wastewater-based epidemiology, providing critical data for public health and policy decisions. PhD in Analytical Chemistry, University of South Australia Dr. Tscharke's research centers on wastewater-based epidemiology, where he analyzes wastewater to estimate population-level consumption of licit and illicit substances. His work extends the utility of wastewater data by combining it with other data sources to improve understanding of drug use patterns and chemical exposure in communities. He has particular expertise in developing correction factors for pharmaceuticals, analyzing temporal and spatial trends in drug consumption, and evaluating the impact of policy changes on substance use. His recent publications demonstrate a strong focus on expanding wastewater-based epidemiology to monitor antidepressants, tobacco products, alcohol, and novel psychoactive substances. His work increasingly incorporates socioeconomic factors and geographical analysis to understand how remoteness and community characteristics influence substance use patterns. Dr. Tscharke has also been expanding into microplastic research, examining plastic deposition in sediments and the release of micro- and nanoparticles from everyday products. Dr. Tscharke actively supervises PhD students on projects related to wastewater-based epidemiology, contaminants of emerging concern, and substance use monitoring. His current research grants include projects funded by the Australian Research Council, Australian Criminal Intelligence Commission, and University of the South Pacific, focusing on identifying contaminant sources, analytical testing, and understanding substance use through multiple data sources. As part of QAEHS, Dr. Tscharke collaborates with a multidisciplinary team including Professor Jochen Mueller, Associate Professor Phong Thai, Dr. Jake O'Brien, and Professor Kevin Thomas. His work contributes significantly to the Minderoo Centre for Environmental Health at UQ, particularly through the National Wastewater Drug Monitoring Program.
Paula Diehr is a Professor of Biostatistics and Health Services at the University of Washington's School of Public Health and Community Medicine. With a distinguished career spanning several decades, Dr. Diehr has established herself as a leading expert in biostatistics and health services research. Dr. Diehr's research focuses on critical methodological issues in public health, including small area statistics, health care utilization analysis, and statistical methods for population health research. Her work on the Diehr Rule for diagnosing pneumonia represents an important clinical decision tool, while her more recent work on the Healthy Life Calculator has contributed significantly to aging and longevity research. Her publications demonstrate consistent contributions to methodological advancements in public health research, particularly in statistical approaches for analyzing health care data and evaluating community-based interventions. Dr. Diehr has published extensively in top public health journals including Annual Review of Public Health, American Journal of Public Health, and Medical Care. UCLA Alumni Hall of Fame recipient Dr. Diehr has been instrumental in developing statistical methodologies that address real-world challenges in health services research, with particular emphasis on the proper application of statistical assumptions in large public health datasets and innovative approaches to analyzing health care utilization patterns. Her work bridges theoretical statistical methods with practical public health applications.
Øyvind Wiig Petersen is an Associate Professor at the Department of Structural Engineering, Norwegian University of Science and Technology (NTNU). His research focuses on bridge dynamics, wind and wave loading, inverse force identification, structural monitoring, and machine learning applications in structural mechanics. He works extensively with long-span suspension bridges and floating bridge systems. Current research areas include vortex-induced vibrations, Kalman filter applications, wind tunnel testing, and finite element model updating. He has published in leading journals like Journal of Wind Engineering, Mechanical Systems and Signal Processing, and Engineering Structures. His work integrates experimental data with computational models for structural condition assessment and load estimation.
