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Jenni NikuView profile

Senior Lecturer

• Multivariate Statistical Modeling
• Latent Variable Models
• Statistical Method Development
• +3 more

Jenni Niku is a Senior Lecturer at the Department of Mathematics and Statistics, University of Jyväskylä, specializing in multivariate statistical modeling and computational methods for ecological data. She is actively involved in the Predictive Community Ecology Group and leads research projects on latent variable models for complex ecological structures. Research Focus: Latent variable models for community ecology Spatial-temporal pattern analysis Statistical methodology for multivariate data Joint species distribution modeling Computational tools for ecological datasets Recent Publications: Jenni has contributed to advancements in analyzing compositional count data, fungal dispersal dynamics, chronic disease modeling, and peatland restoration. Her work bridges statistical innovation with ecological applications, particularly in handling high-dimensional data. Collaborations: She works extensively with researchers from Biological and Environmental Science departments, focusing on interdisciplinary applications of her statistical models.

• Botany
• Vegetation Science
• Mountain Ecosystems
• +7 more

Prof. Dr. Jörg Ewald is a Research Professor in the Faculty of Forestry and Agriculture at the Technical University of Applied Sciences Weihenstephan-Triesdorf. He leads the Research Professorship on Diversity and Functions of Mountain Ecosystems and holds key leadership roles as Dean of the Faculty and Scientific Director of the Doctoral Center Sustainable Land Use Systems (SusLand). He is also a member of the steering committee for the BayWISS Joint College Life Sciences and Green Technologies. His research is centered on botany, vegetation science, and mountain ecosystems , with a strong emphasis on the impacts of climate change, land-use dynamics, and conservation in alpine and forest environments. His work spans plant ecology, biodiversity assessment, soil ecology (particularly Tangelhumus), and forest site evaluation. He teaches courses such as General Botany, Systematic Botany, Forest Communities, and Climate Change Management. His recent publications reveal a consistent focus on long-term ecological monitoring, species distribution shifts in response to warming, and the effects of land use on biodiversity across trophic levels. He employs methods like NMDS ordination, cluster analysis, metabarcoding, and GIS-based modeling. His work often involves large collaborative networks across Germany and integrates citizen science data with historical records. Prof. Ewald is actively involved in applied research for sustainable forest management and conservation policy, contributing to projects like StratALP for assessing forest degradation sensitivity. His leadership in doctoral education and interdisciplinary research initiatives underscores his central role in advancing sustainable land use science.

Varouchakis EmmanuelView profile

Assistant Professor

• Geostatistics
• Environmental Mining
• Sustainable Development
• +3 more

Varouchakis Emmanuel is an Assistant Professor at the Department of Mining Technology within the School of Mineral Resources Engineering at the Technical University of Crete . His research focuses on Geostatistics , Environmental Mining , and Sustainable Development , with expertise in Risk Analysis , Stochastic Processes , and Machine Learning . He contributes to the Research Units of Geostatistics and Environmental Mining and Sustainable Development ( https://www.envi-stat.tuc.gr/en/home ). Research Trends: His recent publications address critical challenges in hydrogeological modeling, environmental risk assessment, and sustainable mining practices. Key themes include groundwater level analysis , geostatistical inversion , pit lake hazard frameworks , and multi-criteria decision-making for SDG implementation in mining contexts. Grants & Projects: He leads the HFRI-funded project "Advanced geostatistical modeling for natural resources evaluation" (2023-2025) and participates in EU initiatives like IMMERSE (2023-2025) for critical mineral education and REECOL (2023-2026) focused on ecological rehabilitation of post-mining areas, employing machine learning and remote sensing. Teaching: He teaches undergraduate courses in Statistics and Probability for Engineers and Applied Geostatistics , as well as postgraduate courses on Time Series Analysis and Geostatistical Simulation Methods .

Daniela MarellaView profile

Professor

• Statistical matching
• Bayesian networks
• Survey sampling
• +4 more

Daniela Marella is a Professor at the Department of Social and Economic Sciences, Sapienza University of Rome. She teaches Statistics and holds office hours on Tuesdays from 10:00 AM to 12:00 PM. Her email address is daniela.marella@uniroma1.it . Teaching: Statistics (Sociology, Economics, Development Studies) Research: Statistical matching, Bayesian networks, survey sampling, uncertainty quantification Publications: 15+ articles on non-probability sampling, measurement error, and interrater agreement Her research focuses on statistical matching methodologies, Bayesian network applications in survey data, and handling selection bias in non-probability samples. She explores measurement error modeling, empirical likelihood approaches, and resampling techniques for complex survey designs. Recent publications emphasize uncertainty analysis in statistical matching, graphical models for data integration, and Bayesian structural learning. Key areas include non-ignorable sampling, ordinal categorical data agreement, and pseudo-population resampling frameworks.

Yu YaoView profile

Lecturer

• Machine Learning
• Artificial Intelligence
• Robust Machine Learning
• +4 more

Yu Yao is a Lecturer in Machine Learning at the School of Computer Science, The University of Sydney. He joined in December 2023 and focuses on developing robust and interpretable machine learning systems. His research emphasizes robustness to data noise, adaptable ML systems, and disentangled representation learning. Yao holds a PhD from The University of Sydney under Professors Tongliang Liu and Dacheng Tao, followed by postdoctoral positions at Mohamed bin Zayed University of Artificial Intelligence and Carnegie Mellon University. Education: PhD in Computer Science (University of Sydney), postdoctoral research at MBZUAI and CMU. Research interests include causal inference in ML, multimodal learning, and label noise mitigation. He has published extensively in top venues like ICML, NeurIPS, and ICLR, and served as an Area Chair for AJCAI 2023, NeurIPS 2025, and ICLR 2025. Awards: Outstanding Reviewer (NeurIPS 2023, ICLR 2023), University of Sydney Research Excellence Prize (2019) Teaching: Advanced Machine Learning (USYD), Guest Lectures on noisy label learning (MBZUAI, China University of Petroleum) Service: Action Editor for TMLR, Area Chair for ICML/ICLR/NeurIPS, reviewer for top journals and conferences His lab focuses on trustworthy AI, with ongoing projects on causal mechanisms in robust learning and interpretable multimodal systems. Current advisees include PhD candidates Ruojing Dong and Jiyang Zheng (co-advised with Prof. Liu), and master's student Kai Lian.

• Labor Law
• Private Law
• Criminal Sciences
• +8 more

Bernard Bossu is a Professor of Private Law and Criminal Sciences at the Faculty of Legal, Political and Social Sciences, University of Lille. He serves as Director of the Social Law Research Team (EREDS) and Co-director of the Institute of Labor Sciences (IST), playing a central role in labor law research and academic leadership. Professor, Private Law and Criminal Sciences, University of Lille Director, Social Law Research Team (EREDS) Co-director, Institute of Labor Sciences (IST) Responsible for Master’s programs in Social Law and Labor Law His research focuses on labor law, particularly employment contracts, workplace surveillance, information and communication technologies (ICT), staff representation, and the legal dimensions of worker dignity and rights. He explores how technological advancements challenge traditional legal frameworks and how reforms, such as the Auroux Laws, have shaped contemporary labor relations. The recent publications and conferences highlight a strong trend toward digitalization in labor law, including monitoring, ICT integration, and data privacy, alongside enduring themes like anti-discrimination, aging workforce, and disabled workers’ inclusion. His scholarly output reflects a deep commitment to social justice, legal reform, and the protection of employee rights in evolving work environments. Bernard Bossu has supervised numerous doctoral students in labor law, covering topics such as workplace violence, private life at work, and ethical dimensions of employment. While no specific grants are mentioned, his leadership in organizing major colloquia and research programs indicates active involvement in funded academic initiatives. He is affiliated with key research units including EREDS, IST, and CRDP, contributing to a collaborative ecosystem focused on labor, social law, and legal reform. His work bridges academic scholarship with practical legal challenges in contemporary employment relations.

• Machine Learning
• Information Fusion
• Data Clustering
• +3 more

Julien Ah-Pine is a lecturer at Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS) under Université Clermont Auvergne , with affiliations at Institut national polytechnique Clermont Auvergne and École des Mines de Saint-Étienne . He also holds a Researcher position at CNRS. Research Interests His work spans machine learning , information fusion , aggregation functions , and multi-criteria decision support , with a focus on complex data types like graphs , functional data , and multi-view datasets . Recent publications emphasize anomaly detection in spectral data streams , online learning , and interpretable AI for industrial applications. Selected Publications 2025 work on OnlineBootKNN introduces a novel framework for real-time spectral anomaly detection, while 2024 research explores multiple kernel methods in functional data classification. Earlier studies cover graph-based clustering , relational data mining , and linguistic network models for NLP tasks. Laboratory & Collaborations Works within LIMOS laboratory at Université Clermont Auvergne, collaborating with institutions like Mines Saint-Étienne and CNRS. Key partnerships include Nicolas Rojas Varela and Engelbert Mephu Nguifo on data stream analysis projects.

Tim ChenView profile

Senior Lecturer

• Human-Computer Interaction
• Computer Graphics
• Artificial Intelligence
• +4 more

Dr. Tim Chen is a Senior Lecturer at the School of Computer Science, University of Adelaide, with research focused on augmenting human capabilities through human-centered AI systems. He leads interdisciplinary teams in projects like the Augmenting Ability CRC and the IMAGENDO Project , which received the Eureka Prize and NHMRC Idea Grant. Affiliation: University of Adelaide Collaborators: Prof. Patrick Baudisch (HPI), Prof. Takeo Igarashi (U of Tokyo), Prof CT Lin (UTS), Dr. Li-Yi Wei (Adobe Research) Research Interests: Human-AI interaction paradigms ( AI as Assistant, Collaborator, Oracle ), virtual reality training, contrastive learning, and data visualization. His work bridges AI/ML, computer graphics, and human-computer interaction to empower knowledge workers. Recent Achievements: Two CHI 2025 papers on VR learning, SIGGRAPH 2025 jury committee role, and ARC DP analysis website development. Funding includes NHMRC (~$2M) and AEA Ignite grants. Scientific Awards: Eureka Prize-winning leadership NHMRC Idea Project Grant AEA Ignite Grant (~$500k)

• Data Structures
• Algorithms
• Compressed Data
• +3 more

Dr. Sebastian Wild is a Lecturer at the University of Liverpool, specializing in algorithms and data structures. His research focuses on developing space-efficient and adaptive methods for computing over compressed data, optimizing performance based on input characteristics. He has led projects on sorting, selection, and dictionary operations, with an emphasis on exact constant-factor analysis and practical efficiency. Research Grants: Computing over Compressed Graph-Structured Data (EPSRC, 2024-2027) Decomposition techniques for graphs (Royal Society, 2023-2025) Lazy Finger Search Trees (Royal Society, 2021-2022) Recent publications highlight advancements in adaptive sorting, approximation algorithms for scheduling, and cache-oblivious data structures. Themes include implicit representations, compressed graph processing, and run-adaptive methods. His work bridges theoretical analysis with real-world applications in databases, compression, and algorithm engineering. Professional Roles: REF Impact Lead (Department, 2023-present) Outreach Co-ordinator (Department, 2020-present) Recruitment Lead (School/Institute, 2022-2024) He teaches modules like COMP335: Communicating Computer Science and supervises theses, integrating his research into pedagogy.

• Dependence Modeling
• Copula Models
• Multivariate Analysis
• +7 more

Harry Joe is a Professor in the Department of Statistics at the University of British Columbia (Vancouver Campus). His primary research focuses on dependence modeling, copula theory, multivariate analysis, and applications in biostatistics, finance, and psychometrics. He has advised students including Xiaoting Li, Xinyao Fan, and Pavel Krupskiy. Research Interests: - Advanced copula constructions (e.g., vine copulas) - Extreme value theory and tail dependence - Applications in financial risk, biomedical research, and educational measurement - Multivariate time series analysis and non-Gaussian models Publications highlight contributions to copula-based classification methods (2024), factor copula models (2015), and dynamic dependence modeling (2020). His work bridges theoretical developments with practical applications across disciplines. Teaching and advising emphasize methodological innovation. Current research explores high-dimensional dependence structures and computational methods for complex data. No lab/team affiliations explicitly noted in provided materials.

Yu ChengView profile

Professor

• Dynamic Treatment Regimes
• SMART (Sequential Multiple Assignment Randomized Trials)
• Causal Inference
• +6 more

Yu Cheng is a Professor and Chair of the Department of Statistics at the University of Pittsburgh, affiliated with the Dietrich School. She holds secondary appointments in Biostatistics and Psychiatry. Her research focuses on dynamic treatment regimes, SMART trials, causal inference, and multiple endpoints analysis. She leads a PCORI-funded method grant on SMART studies and co-investigates projects on cardiovascular disease and maternal/child health outcomes. Her awards include ASA Fellow (2024) and Pittsburgh Chapter Statistician of the Year (2020). Dr. Cheng earned her PhD in Statistics from the University of Wisconsin-Madison (2006), following MS (National University of Singapore, 2001) and BS (University of Science and Technology of China, 1999) degrees. She has held leadership roles including ASA LiDS Section Treasurer (2021–2023), Department Chair (2014–present), and Director of Graduate Studies (2016). Her research integrates methodological innovation with clinical collaboration, addressing topics like HIV, smoking cessation, lupus, depression, and cystic fibrosis. Her 150+ publications span journals like Biometrics , Journal of the American Statistical Association , and Biostatistics . Teaching includes courses in Survival Analysis, Probability, and Statistical Consulting. She actively mentors students through supervised consulting programs.

• Dutch Linguistics
• Language Acquisition
• Theoretical Linguistics
• +3 more

Dr. Caitlin Meyer is an Assistant Professor of Dutch Linguistics at the Faculty of Humanities, University of Amsterdam (UVA), specializing in language acquisition and theoretical linguistics. Her research focuses on syntactic development, numerical cognition, and pragmatic particle usage in Dutch, with significant contributions to understanding verb clusters and ordinal number acquisition. Education: PhD in Linguistics from UVA Research: Language acquisition, Dutch syntax, cognitive development, experimental linguistics Collaborations: Engaged with Leiden University Centre for Linguistics (LUCL) for interdisciplinary research Her recent publications analyze Dutch verb cluster acquisition (2015-2020), ordinal number learning strategies (2016-2021), and pragmatic particle development (2017). She employs experimental methods and cross-linguistic comparisons in her work, leveraging LUCL's Phonetics, EEG, and Eye-tracking labs. While no formal awards are listed in this dataset, her research has been published in key academic outlets. She contributes to teaching through formal programs at University of Amsterdam and participates in LUCL's educational initiatives.

Bryan E. ShepherdView profile

Professor

• Biostatistics
• Causal Inference
• Measurement Error
• +3 more

Bryan E. Shepherd serves as Professor of Biostatistics and Biomedical Informatics and Vice Chair of Faculty Affairs in the Department of Biostatistics at Vanderbilt University. As a primary faculty member, he leads methodological research while overseeing departmental academic operations within the Vanderbilt University Medical Center ecosystem. His foundational education includes a PhD in Biostatistics from the University of Washington, establishing his technical expertise in advanced statistical methodologies. This training underpins his dual focus on theoretical innovation and real-world health applications. Dr. Shepherd's research centers on solving complex data challenges through novel biostatistical frameworks, particularly causal inference under measurement error, two-phase sampling optimization, and longitudinal analysis of error-prone clinical data. His work bridges theoretical statistics and urgent public health needs, with HIV research forming a major application domain where he addresses disparities in treatment access, care continuum outcomes, and comorbid conditions across global populations. Recent methodological breakthroughs include R packages for semiparametric likelihood estimation and rank correlation analysis. Analysis of his 2023-2025 publications reveals a dominant trend toward methodological solutions for data imperfections (measurement error, selective sampling) combined with high-impact applications in HIV epidemiology. His Latin American and African cohort studies consistently examine sex disparities, treatment efficacy, and social determinants of health, while his statistical innovations focus on efficiency gains in causal estimation and correlation modeling for clustered data. No specific scientific awards are documented in the available institutional profile. Though student advising details are unlisted, his extensive publication record—particularly collaborative international studies—implies active mentorship of junior researchers. His leadership as Vice Chair of Faculty Affairs suggests significant administrative responsibilities alongside research. Grant activity is inferred from multi-country HIV studies involving Vanderbilt's Center for Quantitative Sciences and Data Coordinating Center. Dr. Shepherd operates within Vanderbilt's collaborative research infrastructure, notably the Center for Quantitative Sciences (CQS) and Biostatistics Data Coordinating Center (VBDCC). His work leverages these resources for large-scale analyses of HIV cohorts across Latin America and Nigeria, focusing on treatment outcomes, genetic risk factors, and health system barriers. The Vanderbilt Nigeria Biostatistics Training Program (VN-BioStat) reflects his commitment to global capacity building in biostatistics.

Wei WuView profile

Professor

• Clinical Psychology
• Quantitative Psychology
• Longitudinal Data Analysis
• +3 more

Wei Wu is a Professor in the Department of Psychology at Indiana University, specializing in quantitative methodology for psychological research. His work bridges clinical applications with advanced statistical techniques, particularly in longitudinal study designs. His academic credentials include: Ph.D. in Quantitative Psychology from Arizona State University (2008) M.S. in Personality Psychology from East China Normal University (2003) B.Ed. in Special Education from East China Normal University (1997) Professor Wu's research focuses on developing and refining statistical methods for psychological inquiry, with emphasis on missing data handling, growth curve modeling, and model fit evaluation. His clinical psychology background informs applications in educational and developmental contexts, particularly regarding grade retention effects. Methodological rigor defines his contributions, advancing best practices for longitudinal data analysis in social sciences. Analysis of his publication record reveals consistent innovation in missing data techniques and experimental design optimization. His work demonstrates increasing sophistication in planned missing data approaches, with growing emphasis on computational efficiency and real-world applicability across psychology subfields. Recent publications integrate SEM and multilevel frameworks to solve complex modeling challenges. Professor Wu has secured substantial research funding through competitive grants: NSF grant ($422,900, 2011-2016) as PI for planned missing data designs in longitudinal research NIH grant ($1.7M, 2010-2014) as Statistical Consultant for resilience determinants study PCORI grant ($1.5M, 2013-2016) as Statistical Consultant for burnout impact research New Faculty General Fund ($8,000, 2010-2013) for model fit evaluation methods NICHD grant (2004-2008) as Research Assistant for grade retention study He actively mentors graduate researchers, with documented advisees including A. I. Carroll, P-Y. Chen, F. Jia, R. Kinai, F. Gu, P. R. Miller, and P. Pornprasermanit across multiple publications. While no formal lab structure is specified, his extensive grant collaborations indicate active participation in interdisciplinary research networks focused on methodological advancement.

• Education in Technology-Enabled Environments
• Interprofessional Education
• Educational Technology and Health Care

Kerry Johnson is a retired Associate Teaching Professor and Health Information Management (HIM) Instructional Co-ordinator at Ontario Tech University's Faculty of Health Sciences. Her work focuses on enhancing education for Canadian HIM professionals through technology-enabled and interprofessional approaches. She holds a Doctorate in Educational Leadership with a focus on Educational Technologies from the University of Phoenix, alongside a Master in Adult Education and Distance Learning and an undergraduate degree from the University of Toronto. Education: Undergraduate degree, University of Toronto Master in Adult Education and Distance Learning, University of Phoenix Doctorate in Educational Leadership - Educational Technologies Her research emphasizes improving education quality through technology integration, interprofessional collaboration, and healthcare-specific educational tools. She has contributed to foundational HIM textbooks addressing decision support systems, ethical practices, and interprofessional care dynamics. While her recent articles focus on data analytics and ethical frameworks in healthcare, she has no listed scientific awards but maintains an active role in curriculum development. Dr. Johnson has taught courses such as Health Data Analytics, HIM Capstone, and Interprofessional Care Strategies. Her work reflects a commitment to bridging educational theory with practical healthcare challenges, particularly in technology-driven learning environments.