Paula Cristina Ribeiro Vicente is an Assistant Professor at Lusófona University in Lisbon and a member of the research unit COPELABS . Her academic career spans over three decades, focusing on advanced statistical modeling techniques. She holds a PhD in Applied Quantitative Methods (2014, ISCTE-Instituto Universitário de Lisboa) and a Master's in Statistics and Operations Research (1993, University of Lisbon). Her research specializes in: Latent Growth Curve Models Longitudinal Data Analysis Structural Equation Modeling Omissions by Design in Data Collection Recent publications (2024) examine planned missing data designs in longitudinal analysis and sensitivity of fit measures in structural equation models. Earlier works (2023-2018) analyze normality deviations' impact on model estimates and compare fit indices across statistical frameworks. She has been actively involved in methodological conferences like the Portuguese Association for Classification and Data meetings and Psychometric Society events, presenting simulation studies on missing data and model robustness. Her work primarily supports policy analysis through statistical modeling of material deprivation metrics using rotating panels and confirmatory factor analysis frameworks.
Dr. Larisa Tarasova is a tenured Research Group Leader of "Watershed Dynamics & Hydrological Extremes" at the Helmholtz Centre for Environmental Research – UFZ, Department of Catchment Hydrology, Germany. She leads projects on large-scale flood analysis, catchment regionalization and hydrological modelling uncertainty, and has published extensively on these topics since 2016. Education: PhD in Hydrology, Department of Catchment Hydrology, UFZ (2016-2020) M.Sc. Water Resources & Environmental Management, Leibniz University Hannover (2013-2015) B.Sc. Ecology & Environmental Engineering, Peoples' Friendship University of Russia (2007-2012) International exchange semesters at Nanjing Normal University and Xiamen University, China Research interests revolve around understanding and predicting hydrological extremes across large domains. She develops data-driven and process-based approaches to classify flood events, quantify their space-time dynamics, and unravel how catchment properties and climate patterns control runoff generation. Her work integrates statistical learning, large-sample datasets (e.g., CAMELS-DE) and uncertainty analysis to improve predictive models for floods and water quality under change. Her publications (≥60 peer-reviewed since 2016) exhibit a clear trajectory from event-based process studies to continental-scale modelling and machine-learning applications, culminating in recent high-impact papers on compound flood drivers, catchment descriptors and neural-network flood forecasting. Scientific awards include the Gordon Research Conference Rising Star Award, AGU and Wiley citations for excellence, and national Best Dissertation and UFZ Best PhD awards in 2021, underlining her early-career leadership. She convenes sessions on hydrometeorological extremes at EGU and AGU meetings, serves as reviewer for top journals (Science, Nature Climate Change, WRR, etc.) and acts as referee for NSF and Hungarian Academy grant programmes, underlining her active service to the community.
Dr. Mindaugas Juodis is a Researcher at the Blockchain and Quantum Technologies Group within Vilnius University's Institute of Data Science and Digital Technologies. His work centers on advanced statistical modeling of blockchain systems and decentralized networks. His research spans blockchain, cryptocurrency, quantum computing, and probability theory. Key investigations include Bitcoin price regime shifts using Bayesian methods, Ethereum transactional decentralization metrics, and wealth distribution analysis in blockchain networks. Earlier theoretical work focused on self-normalized sums, central limit theorems, and functional limit theorems for dependent processes. Publications reveal a clear trajectory from theoretical statistics (2004-2007) to applied blockchain analytics (2024-2025). Recent work bridges mathematical rigor with cryptocurrency applications, appearing in Mathematics, ICT Express, and IEEE conferences. His research demonstrates expertise in translating complex statistical frameworks to real-world decentralization problems. As part of the Blockchain and Quantum Technologies Group, he contributes to cutting-edge research in blockchain analytics and quantum computing intersections, focusing on empirical validation of decentralization metrics and network properties.
Susan Hunter is an Associate Professor in the Edwardson School of Industrial Engineering at Purdue University, West Lafayette, with her office located in GRIS 272. She can be contacted via email at susanhunter@purdue.edu. Her research centers on Operations Research with deep specialization in Stochastic Optimization and Multi-objective Optimization . She develops theoretical frameworks and practical algorithms for simulation-based optimization under uncertainty, focusing on Pareto front approximation, adaptive sampling techniques, and parallel computing implementations. Key contributions include error quantification methods, confidence region constructions, and performance indicator analyses for stochastic multi-objective problems. Analysis of her 2020-2025 publications reveals dominant trends in multi-objective stochastic simulation optimization, particularly addressing computationally expensive functions and integer-variable constraints. Her work bridges theoretical rigor (e.g., Central Limit Theorems for confidence regions) with practical tools like the SCORE algorithm for sampling allocation and bi-PASS for parallel optimization. She also contributes methodological guidance through LaTeX submission templates for academic publishing.
Markus Bitzer serves as a Clinical Professor in the Department of Internal Medicine - Division of Nephrology at the University of Michigan Medical School. As a Physician-Scientist, he bridges clinical nephrology practice with cutting-edge research in kidney disease mechanisms and translational applications. His work spans from experimental model systems to patient care for complex renal conditions. Dr. Bitzer's research interests focus on glomerular diseases including diabetic kidney disease and focal segmental glomerulosclerosis, aging-related kidney changes , and the role of non-coding RNAs in disease progression. His laboratory employs multi-omics approaches integrating computational pathology, glycomics, and transcriptomics to uncover novel disease mechanisms and biomarkers. Recent work demonstrates expertise in spatial molecular analysis using advanced mass spectrometry imaging and computational integration of complementary multi-omics datasets. Analysis of his recent publications reveals a strong emphasis on translational applications with multiple studies identifying potential biomarkers (urinary CXCL9, clusterin) and therapeutic targets (miR-17/-20a pathway). His collaborative work through consortia like CureGN and NEPTUNE demonstrates engagement with large-scale clinical research initiatives. The research portfolio shows increasing sophistication in computational pathology methods applied to kidney disease classification and progression. Dr. Bitzer actively collaborates with both basic science and clinical research teams across multiple institutions, with frequent co-authorship on studies involving multi-omics data integration, computational pathology, and clinical nephrology. His Twitter presence indicates engagement with the broader scientific community regarding methodological approaches in multi-omics analysis.
Frederick W. Taylor is a Senior Research Scientist Emeritus at the Institute for Geophysics, Jackson School of Geosciences, The University of Texas at Austin. His research focuses on Quaternary and contemporary crustal motions in the Southwest Pacific, utilizing coral reefs to study tectonic deformation and sea-level changes. Key areas include the Solomon Islands megathrust zone and climate-driven sea-level anomalies in the tropical Pacific. His work integrates geodetic data (GPS, SAR) with coral microatoll analysis to explore tectonic processes, including earthquake cycles and long-term vertical deformation. He also investigates Holocene sea-level trends and ENSO variability through coral geochemical records. Taylor collaborates globally, particularly in the Federated States of Micronesia and Vanuatu, leveraging coral archives to reconstruct climate and tectonic histories. Recent studies highlight the interplay between short-term elastic deformation and long-term tectonic uplift in forearc regions. His publications span 40+ years, addressing topics like subduction zone segmentation, paleoseismic events, and climate model-proxy comparisons for ENSO dynamics. Taylor's affiliations include the Bureau of Economic Geology and interdisciplinary collaborations within the Jackson School. His research contributes to understanding natural hazards like earthquakes and sea-level rise, bridging geophysics, paleoclimatology, and geology.
Jialin Zhang is an Assistant Professor of Statistics at Mississippi State University. He received his Ph.D. in Statistics from the University of North Carolina at Charlotte in 2019. His research focuses on entropic statistics, including nonparametric estimation of entropy, mutual information, tail probabilities, and biodiversity indices, with applications in machine learning and data science. His work addresses high-dimensional and non-ordinal data challenges using information-theoretic frameworks, developing tools like R packages for tail classification and entropic statistics. Research emphasizes theoretical foundations and practical implementations in computational biology and healthcare analytics.
Yi Yin is an Associate Professor of Environmental Studies at New York University, specializing in the interactions between terrestrial ecosystems, atmospheric processes, and anthropogenic impacts. Her work focuses on climate change feedback mechanisms, extreme events (heatwaves, droughts, wildfires, floods), and equitable climate adaptation strategies. She holds a Ph.D. in Ecology from Peking University (2012) and a joint Ph.D. in Geobotany from Leibniz Universität Hannover (2009), alongside a B.S. in Physical Geography from Beijing Normal University (2006). Prior to NYU, she served as a research scientist at Caltech, and held postdoctoral roles at NASA's Jet Propulsion Laboratory and France's LSCE. Her current research explores urban heat disparities, ecosystem resilience to climate extremes, and methane mitigation strategies. She leads projects addressing environmental justice in climate adaptation, fire-driven carbon cycles, and global methane budgets. Her publications analyze urban heatwave impacts, agricultural radiative transfer modeling, and wetland methane dynamics. She serves on NYU's Steering Committee for Foundations of Scientific Inquiry. While no formal awards are listed, her work demonstrates impactful contributions to climate science and policy-relevant research.
Dr. Michael Förster is the Head of the Vegetation Remote Sensing Research Department and a Postdoctoral researcher at the Technical University of Berlin since 2009. His work focuses on developing remote sensing methods to analyze ecosystem dynamics, vegetation structure interactions, and environmental indicators using satellite and drone data. He holds expertise in integrating optical and SAR time series to study forest health, drought impacts, and soil moisture dynamics. Education & Positions: PhD in Geoecology (Potsdam, 2003) Visiting Scientist positions at JRC (2018), Utrecht University (2012), and EURAC (2010) Research roles at TU Berlin since 2003 Research Interests: Analyzing ecosystem degradation via remote sensing time series Linking spectral/plant trait data to biophysical variables Deriving actionable environmental indicators for policy frameworks (e.g., NATURA 2000) Integrating LiDAR/SAR with optical data for forest evaluation Drone-satellite synergy for ecohydrological studies Key Contributions: Pioneered drought impact analysis using XAI and Sentinel-2 data Developed methods for bark beetle infestation detection Advanced soil moisture modeling via cosmic-ray neutron sensing and remote sensing fusion Published extensively in journals like Ecological Indicators, Remote Sensing of Environment, and Forest Ecology and Management Labs & Teams: Leads the Vegetation Remote Sensing Research Department, coordinating interdisciplinary projects on environmental monitoring and geoinformatics.
Haziq Jamil is an Assistant Professor in Statistics at Universiti Brunei Darussalam (UBD) within the Faculty of Science, Department of Mathematics. He concurrently serves as a Visiting Fellow at the London School of Economics and Political Science (LSE) Department of Statistics (2024-2027) and will transition to King Abdullah University of Science and Technology (KAUST) as a Research Specialist in August 2025, taking leave from UBD. His academic journey includes a PhD in Statistics (2018) and MSc in Statistics (2014) from LSE, and a BSc & Master in Mathematics, Statistics, Operational Research and Economics (2010) from Warwick University. His research spans statistical theory, methods, and computation with strong social science applications. Core interests include latent variable models, Gaussian processes, Bayesian statistics, and spatio-temporal modeling, particularly applied to Brunei's housing market and psychometric testing. He pioneered I-prior regression methodology using Fisher information kernels and developed bias-reduction techniques for Item Response Theory models. His publication record shows consistent output in high-impact journals with increasing focus on Brunei-specific applications since 2022. Haziq's 15 most recent publications (2022-2025) demonstrate methodological innovation in Bayesian computation, latent variable modeling, and spatial statistics, with growing emphasis on real-world applications in Brunei's housing market and educational assessment. Key trends include development of sparse Gaussian process models for property valuation, spatio-temporal analysis of Brunei's real estate, and bias-adjustment methods for psychometric models – reflecting his dual expertise in theoretical statistics and practical implementation. Teaching Excellence Award in Sciences (Universiti Brunei Darussalam, 2023) Arnold Zellner Thesis Award Honourable Mention (American Statistical Association, 2020) In-Service Training Scheme Scholarship (Brunei Public Service Commission, 2013-2018) Supreme Commander of Royal Brunei Armed Forces Scholarship (2006-2010) Best Student Award 2005 (Persekutuan Guru-Guru Melayu Brunei) Haziq serves as Graduate Programme Coordinator for Mathematics (2021-2025) and Faculty Liaison Committee member at UBD. His consultancy includes defense-related data analysis for Brunei's Ministry of Defence (2021-2022). He leads the Brunei R User Group (2024-2026) and maintains active research collaborations through the Bayesian Computational Statistics and Modelling (BAYESCOMP) group at KAUST. Current projects include open-source statistical tables, I-prior methodology R packages, and quantitative text analysis of Brunei's legislative council meetings. He directs multiple ongoing research initiatives including the Brunei housing market dataset covering 30,000+ transactions across three decades, Hamiltonian Monte Carlo educational tools, and quantitative analysis of Brunei's legislative proceedings. His work bridges theoretical statistics with practical applications in urban planning, defense analytics, and educational assessment within Brunei's unique socio-economic context.
Hunter J. Bennett is an Associate Professor at Old Dominion University's Darden College of Education & Professional Studies, specializing in biomechanics and kinesiology with emphasis on musculoskeletal modeling and movement analysis in special populations including autism spectrum disorder. His educational background includes: Ph.D. in Kinesiology and Sport Studies, University of Tennessee, Knoxville (2016) M.Sc. in Exercise and Sport Science, East Carolina University (2013) B.Sc. in Physical Education, University of South Carolina Upstate (2010) Dr. Bennett's research spans musculoskeletal simulations of daily/athletic activities, biomechanics in autism spectrum disorder , and resistance training mechanics . His work employs computational modeling, neural networking, and advanced statistics to analyze movement efficiency and injury prevention mechanisms, with particular focus on lower extremity function during gait and resistance exercises. His 2021-2023 publications reveal strong interdisciplinary trends: 60% examine autism-related movement biomechanics (gait, coordination, physical activity), 30% analyze resistance exercise mechanics (squats, deadlifts), and 10% develop methodological approaches (ultrasound reliability, joint modeling). Key themes include sex differences, movement modifications, and computational validation techniques. His honors include: Assistant Professor/Post-Doctoral Award, SEACSM (2020) Most Collaborative Grants and Contracts, Darden College (2020) Shining Star, ODU (2019) Chancellor’s Fellowship, UT (2016) Dr. Bennett secured $620,000 in research funding including a $120,000 state grant for autism movement efficiency studies (2019-2021) and $500,000 federal funding for cyber-physical fitness monitoring systems (2019-). His publications indicate active mentorship of graduate researchers in biomechanics though specific advisees aren't listed. He collaborates extensively with engineering and health science faculty. He directs the ODU Neuromechanics Lab in the Student Recreation Center, conducting experimental and computational research using motion capture and force plate technology. Current projects involve interdisciplinary teams examining autism movement patterns and cyber-physical fitness systems.
Stine Borgen Lund serves as Assistant Professor in the Department of Public Health and Nursing at the Norwegian University of Science and Technology's Faculty of Medicine and Health Sciences. Her clinical office is located at Øya helsehus in Trondheim, with direct contact through university email and dual Norwegian phone lines. Her research focuses on critical intersections of nursing practice, trauma care, and geriatric vulnerability. Primary expertise spans intensive care nursing , traumatic brain injury management , and elder abuse prevention in nursing homes . Recent work employs constructivist grounded theory and qualitative methodologies to expose systemic neglect mechanisms among Norwegian nursing home staff, while earlier research analyzed physiological variables in moderate TBI patients through CENTER-TBI consortium collaborations. Publication trends reveal a strategic pivot from neurotrauma clinical studies (2011-2017) toward geriatric ethics investigations (2022-2024). Her 15 most recent works demonstrate methodological consistency in qualitative health research while shifting focus from ICU survival resilience to institutionalized elder neglect. Key journals include BMC Geriatrics, Healthcare, and Journal of Clinical Nursing. Lund actively disseminates findings through international academic channels including the Judith D. Tamkin International Symposium on Elder Abuse (2022) and 11th International Gerontological Congress (2022), where she presented on Norwegian nursing home staff perspectives regarding neglect rationalization. Teaching responsibilities include intensive care nursing courses SYT3530, SYT3537, SYT3535, and SYT3536 at NTNU, emphasizing clinical competency development and shared responsibility frameworks in critical care settings. Her 2016 textbook Sykepleie ved sykdommer og skader i sentralnervesystemet remains a foundational resource in Norwegian nursing education.
Dr. Paolo Gorgi is an Associate Professor at the Department of Econometrics and Data Science in the School of Business and Economics at Vrije Universiteit Amsterdam. He holds a PhD in Statistics and Econometrics from the Joint Degree Program of the University of Padova and Vrije Universiteit Amsterdam (2017). His research focuses on score-driven time series models, stochastic processes, forecasting economic variables, and statistical inference in non-linear dynamic models. He is also a Partner at ACMetric B.V. since 2021. His work contributes to UN Sustainable Development Goals related to economic growth and sustainable cities. Key research interests include developing novel methodologies for time series analysis, particularly in handling non-linear dynamics and high-dimensional data. He has authored over 36 publications, including influential works on score-driven models, copula-based count models, and robust statistical estimation techniques. Teaching responsibilities include courses on Data Science Methods and Financial Econometrics . His research network spans collaborations with institutions globally, focusing on econometric applications in finance, macroeconomics, and sports analytics. He has supervised two PhD theses and actively contributes to the academic community through editorial roles and conference participation.
Dr. Bin Li is a postdoctoral researcher in the Institute of Biomedical Engineering at the University of Oxford . His work bridges machine learning , bioimage informatics , and computational pathology , with a focus on advancing AI applications in healthcare. Education: PhD in Biomedical Engineering (2020) from the University of Wisconsin-Madison under Professor Kevin Eliceiri. Dr. Li's research centers on developing deep learning techniques for histopathology , emphasizing weakly supervised and semi-supervised learning to analyze unlabeled/coarsely-labelled data. He also explores multimodal learning for image-based drug screening, integrating data from microscopy, genomics, and proteomics. His publication history reveals expertise in stochastic PDEs , singular SPDEs , and nonlinear dynamics , indicating cross-disciplinary foundations in mathematics and physics. Dr. Li contributes to the Professor Jens Rittscher's group , focusing on innovative computational methods for biomedical image analysis.
Lili Yu is a Lecturer at the School of Psychological Sciences, Macquarie University, and a member of the Macquarie University Centre for Reading. Her research focuses on cognitive and perceptual processes underlying reading comprehension, employing eye-tracking methodologies to investigate how visual attention interacts with linguistic processing. She holds a Ph.D. in Psychology from Tianjin Normal University (2014). Her research interests include: Eye movement patterns as indicators of reading comprehension Cross-language differences in reading strategies Misinformation retention in digital environments Multimodal integration of text, speech, and visual information Yu has led or contributed to three major projects including the Macquarie University Centre for Reading (2020–2022) and the Centre for Elite Performance Expertise and Training (2019–2021). She serves on the editorial boards of Behavior Research Methods and Scientific Reports . Her recent work explores digital reading's cognitive impacts, Chinese reading mechanisms, and predictive eye movement models. Collaborative projects include developing child-friendly eye-tracking tools for literacy research and analyzing subtitle processing in multimedia contexts.
