جست‌وجوی استاد و پژوهشگر

Debby ten Hoveمشاهده پروفایل

استادیار

• Educational Measurement
• Psychometrics
• Social Network Analysis
• +۴ مورد دیگر

Debby ten Hove is an Assistant Professor at the Faculty of Behavioural and Movement Sciences, affiliated with the LEARN! - Learning Sciences department at Vrije Universiteit Amsterdam. Her research focuses on interrater reliability, psychometric modeling, and educational measurement. She is also a project researcher in the ADAPT initiative, which evaluates interventions for early academic skills. Additionally, she serves as an advisor at Stichting CITO since 2023. Her academic background includes advanced statistical methodologies applied to educational and psychological data. Key research areas include social network analysis, cluster-level modeling, and the application of generalizability theory. She teaches courses such as Statistics 2 and Designkeuzes voor Kwantitatief Onderzoek , emphasizing quantitative research methods. Her recent publications emphasize improving reliability in observational studies and rating scales, with contributions to journals like Multivariate Behavioral Research and Psychometrika . Her work bridges theoretical psychometrics with practical educational applications, particularly in early childhood interventions and policy evaluation. Current projects include the ADAPT initiative (2025-2027), focused on measuring the impact of educational interventions in primary and vocational education. This project integrates content knowledge and mathematical achievement metrics to assess intervention efficacy.

• Information Theory
• Machine Learning
• Deep Neural Networks
• +۷ مورد دیگر

Shujian Yu is an Assistant Professor at the Department of Artificial Intelligence , part of the Faculty of Science at Vrije Universiteit Amsterdam. He is also affiliated with the Network Institute . His research focuses on Information Theory , Machine Learning , and Deep Neural Networks , with applications in causal discovery, brain network analysis, and generalization bounds. Key research interests include: Information-Theoretic Methods for ML interpretability and robustness Transfer entropy and causal inference in complex systems Feature selection and dimensionality reduction techniques Brain network-based psychiatric diagnosis (e.g., schizophrenia analysis) He teaches courses on Data Mining Techniques , Deep Learning , and Introduction to Reinforcement Learning . Recent work explores hierarchical state space models, Cauchy-Schwarz divergence applications, and Granger causality in chemical processes. His collaborations span interdisciplinary fields including neuroscience and industrial engineering. Publications emphasize theoretical foundations while addressing practical challenges in ML generalization, adversarial robustness, and sequential decision-making.

• Artificial Intelligence
• Bayesian Networks
• Healthcare Informatics
• +۳ مورد دیگر

Ann Nicholson is a Professor and Dean of the Faculty of Information Technology at Monash University. She has held various senior academic roles, including Deputy Dean (Research) from 2018–2020 and Associate Dean (Education) from 2014–2016. Her work focuses on Bayesian networks, AI applications in healthcare, environmental science, and education technology. She has contributed to numerous projects addressing complex societal challenges, such as pandemic modeling, medical decision support, and sustainable development goals. Her research interests include causal modeling, Bayesian network development, and AI-driven solutions for real-world problems. She has led collaborative initiatives like the MAGNET project studying neurodevelopmental disorders and contributed to environmental modeling of Port Phillip Bay. Nicholson has also pioneered tools like iOOBN for object-oriented Bayesian networks and BARD for structured group reasoning. Her publications span over three decades, with significant contributions to AI applications in medicine, ecology, and policy analysis. She has been involved in projects ranging from wind energy assessment in Mexico to fog forecasting at Melbourne Airport.

Asim Ansariمشاهده پروفایل

استاد

• Internet Recommendation Systems
• Digital Customization
• Social Network Modeling
• +۵ مورد دیگر

Asim Ansari is the William T. Dillard Professor of Marketing at Columbia University’s Columbia Business School. His research focuses on Internet Recommendation Systems, Digital Customization, Social Network Modeling, and Bayesian Methods for Customer Data. He holds affiliations with the Data, Media and Society; Financial and Business Analytics; and Foundations of Data Science centers. Professor Ansari has been recognized with the Paul Green Award (1994) and the Dean’s Award for Teaching Excellence (2009). His work has been nominated for prestigious awards including the O’Dell Award and Long Term Impact Award. He serves as an Associate Editor for Management Science and Quantitative Marketing and Economics , and is on the editorial boards of Marketing Science and the Journal of Marketing Research . His research explores cutting-edge topics such as generative models for consumer behavior, probabilistic machine learning applications in marketing, and the dynamics of consumer choices. His work bridges statistical methods with real-world marketing challenges, emphasizing data-driven strategies for businesses and consumer insights.

Danlu Guoمشاهده پروفایل

مدرس ارشد

• Hydrology
• Environmental Engineering
• Water Quality Modelling
• +۴ مورد دیگر

Dr Danlu Guo is a Senior Lecturer at the School of Engineering, Australian National University (ANU), since December 2022. She holds a PhD in Hydrology from the University of Adelaide (2017) and has held roles including Research Associate at UNSW Sydney and the University of Melbourne, where she remains an Honorary Fellow. Her research focuses on hydrology and environmental engineering, emphasizing data-driven approaches for catchment water resources, including rainfall-runoff modelling, water quality trends, and climate change impacts. She has received prestigious awards such as the 2022 GN Alexander Medal and the 2019 MSSANZ Early Career Award. Dr Guo leads over $1M in Category 2 funding and contributes to ARC Linkage Projects. Her work bridges academic and applied sectors through roles as Guest Editor for the Journal of Hydrology , Chief Editor of the Australasia Journal of Water Resources , and Chair of MSSANZ's Water Quality Modelling SIG. Education Background: PhD in Hydrology (University of Adelaide, 2017); prior roles include Research Associate at UNSW Sydney (2016–2017) and University of Melbourne (2017–2022). Research Interests include river water quality dynamics, Bayesian modelling, large-sample hydrology, and irrigation efficiency. Her studies have revealed deteriorating water quality in the Great Barrier Reef region and recovery in the Murray-Darling Basin, as well as climate-driven shifts in streamflow and salinity. She develops frameworks for benchmarking irrigation performance using satellite and on-farm data. Grants and Projects: Lead CI for >$1M Category 2 funding; involved in ARC Linkage Projects. Current projects include improving water quality modelling via solute transport studies and enhancing irrigation benchmarking methodologies. Labs/Teams: Active in the Institute for Water Futures (ANU) and collaborates with the University of Melbourne's Infrastructure Engineering. Her research network spans hydrology, environmental engineering, and climate science communities.

Ricardo Silvaمشاهده پروفایل

استاد

• Causal Inference
• Graphical Models
• Latent Variable Models
• +۴ مورد دیگر

Ricardo Silva is a Professor of Statistical Machine Learning and Data Science at University College London's Department of Statistical Science, specializing in computational causal inference and graphical models. His research bridges statistics, machine learning, and real-world applications across domains including algorithmic fairness, sports analytics, and network science. His primary research interests focus on causal inference methodologies , particularly counterfactual reasoning, latent variable models, and relational learning frameworks. Silva develops Bayesian computational approaches for causal discovery and effect estimation, with significant contributions to handling hidden confounders and unmeasured variables. His work frequently integrates graphical models with machine learning techniques to address complex inference problems in high-dimensional settings. Key research trends across his publications reveal a strong emphasis on Causal structure learning from observational and interventional data Counterfactual frameworks for algorithmic fairness Bayesian nonparametric methods for relational data Applications in sports analytics and network dynamics His methodological innovations often manifest as open-source software tools that have become standard in causal inference research. Ricardo actively contributes to the academic community through leadership roles in major conferences: Co-organized Uncertainty in Artificial Intelligence (UAI) 2018 and 2019 Chaired workshops at NIPS/NeurIPS on causal inference (2016-2017) Organized the ongoing Centre for Computational Statistics and Machine Learning Masterclasses at UCL As an educator and researcher, Silva maintains strong industry connections through software development and collaborative projects, particularly in applying causal methods to real-world decision systems. His teaching focuses on advanced statistical machine learning concepts with practical implementation components.

Wei Wuمشاهده پروفایل

استاد

• Systems Ecology
• Landscape Ecology
• Coastal Hydrology
• +۳ مورد دیگر

Dr. Wei Wu serves as Professor at the University of Southern Mississippi's Gulf Coast Research Laboratory (GCRL 111). As a systems ecologist, she employs interdisciplinary approaches to model ecosystems across spatiotemporal scales, focusing on ecological resilience under environmental change. Her research integrates landscape ecology, hydrology, and biogeochemistry to understand coastal system responses to climate change and human impacts. Current projects examine saltmarsh resilience to sea-level rise, watershed biogeochemistry in changing climates, and antibiotic resistance in coastal ecosystems. Wu's publications demonstrate advanced application of Bayesian statistics, remote sensing, and hierarchical modeling. Recent work (2018-2025) emphasizes wetland loss drivers, restoration effectiveness, species-habitat relationships, and climate impacts on hydrochemical processes. Her research supports resource management through predictive models of coastal vulnerability, habitat suitability assessments, and restoration strategy evaluations aimed at enhancing human-natural system resilience.

Roee Gutmanمشاهده پروفایل

استاد

• Bayesian Data Analysis
• Missing Data
• Record Linkage
• +۴ مورد دیگر

Roee Gutman is a tenured Professor of Biostatistics at Brown University's School of Public Health. His research focuses on Bayesian data analysis , causal inference , record linkage , and missing data techniques , with applications to health policy, geriatrics, and medical decision-making. Ph.D. in Statistics, Harvard University (2011) Advisor: Professor Donald Rubin , member of Professor Jun Liu's Lab BS (1999) and MS (2004) in Statistics/Optimization from Tel Aviv University His methodological work advances Bayesian causal inference and privacy-preserving data linkage , enabling valid comparisons of non-randomized interventions through multiple imputation and propensity score modeling . Applications include: Linking HIV+ prisoner records with clinical data Comparing opioid vs. NSAID effectiveness Estimating effects of erythropoietin-stimulating agents Nursing home bed-hold policy analysis Recent publications emphasize AI-driven healthcare monitoring (2024), DASH diet adherence interventions (2023), and Bayesian singular value decomposition (2023). Awards include: 2023 Best Paper Award, Statistics in Biopharmaceutical Research 2022 Methodology Award, ISPOR 2022 ASA/NSF/BLS Senior Research Fellow He collaborates with researchers across health services , geriatrics , and epidemiology , with funding from PCORI, NSF, and NIA. Teaching includes courses on Bayesian Methods and Statistical Inference .

Nick Graetzمشاهده پروفایل

پژوهشگر ارشد

• Sociological Methods
• Bayesian Hierarchical Modeling
• Data Visualization
• +۴ مورد دیگر

Nick Graetz is a Postdoctoral Research Associate at the Eviction Lab at Princeton University. He holds a BA from the University of Wisconsin, an MPH from the University of Washington, and a PhD in Demography from the University of Pennsylvania. Research interests include sociological methods, Bayesian hierarchical modeling, data visualization, racial segregation, urban life, education policy, and health policy. His work has been published in Nature , New England Journal of Medicine , The Lancet , and other high-impact journals. Recent research focuses on eviction consequences, mortality analysis, causal inference, and spatial disparities in health and housing. His methodological expertise bridges demographic modeling with policy evaluation. He contributes to projects requiring multidimensional well-being assessments and small-area estimation techniques, often intersecting housing justice and public health equity.

Santu Ghoshمشاهده پروفایل

دانشیار

• Biostatistics
• Clinical Trials
• Bayesian Statistics
• +۷ مورد دیگر

Dr. Santu Ghosh is an Associate Professor at Augusta University's School of Public Health, holding joint appointments in the Department of Biostatistics, Data Science, and Epidemiology, and the Department of Population Health Science. He serves as Graduate Program Director for the MS Data Science and MS/PhD Biostatistics programs. Dr. Ghosh earned his PhD from Northern Illinois University in 2013 and completed a postdoctoral fellowship at Wayne State University. His research focuses on Bayesian methodologies for clinical trials, meta-analysis, network data analysis, and ranked set sampling. He has authored over 30 peer-reviewed articles and serves as a journal reviewer for multiple high-impact publications. Dr. Ghosh teaches advanced biostatistics courses and advises PhD students in biostatistics and data science. Education PhD in Mathematical Statistics & Probability, Northern Illinois University, 2013 Research Interests Dr. Ghosh specializes in applying Bayesian statistical methods to biomedical problems, including non-inferiority trials, enrichment trial designs, and meta-analysis. He develops novel statistical techniques for analyzing high-dimensional genomic data and network structures. His work bridges theoretical statistics with practical applications in healthcare, such as optimizing pediatric blood pressure screening and evaluating disinfection protocols in radiology. Professional Contributions Statistical consultant for multiple research initiatives, including KIRSO Project and Rajan Kapoor's clinical studies Reviewer for Journal of Biopharmaceutical Statistics , Journal of Applied Statistics , and others Recipient of the 2018 Southern Region Council Travel Award and 2013 NIU Certificate of Merit Teaching & Mentoring Dr. Ghosh instructs courses such as Biostatistics I/II, Generalized Linear Models, and Dissertation Research. He mentors PhD students in their thesis work, including Bichna, Rongrong Wang, and Kathryn McDonald, guiding them in biostatistical research and methodological innovation.

Naftali Weinbergerمشاهده پروفایل

پژوهشگر ارشد

• Causation
• Causal Modeling
• Philosophy of Science
• +۹ مورد دیگر

Naftali Weinberger is a Postdoctoral Fellow at the Munich Center for Mathematical Philosophy within Ludwig Maximilian University of Munich's Faculty of Philosophy, Philosophy of Science and Religious Studies. His research focuses on causal modeling methods applied to foundational questions in causal inference and explanation across multiple scientific disciplines. Education PhD, University of Wisconsin, Madison (2015) Dr. Weinberger's research addresses the theoretical foundations and practical applications of causal modeling, with particular emphasis on dynamical systems and discrimination. His work spans population genetics, psychometrics, neuroscience, and economics, examining how causal concepts operate in diverse scientific contexts. Current projects investigate causation in dynamical systems and develop causal frameworks for modeling discrimination. His publication record (2011-2022) demonstrates consistent output in leading philosophy of science journals, with evolving focus from evolutionary biology and psychometrics toward dynamical systems and causal frameworks. Key themes include equilibrium models, time-scale relativity, non-factual scientific disagreement, and path-specific causal effects across disciplines. No scientific awards were mentioned in the source materials. No information regarding student advising or research grants was provided in the available documentation. Dr. Weinberger is currently affiliated with the Munich Center for Mathematical Philosophy. Previously, he contributed to the 'Causation, Continuity, and Complexity' project at the University of Pittsburgh's Center for Philosophy of Science and the 'Bridging Causal and Explanatory Reasoning' project at Tilburg University.

• Statistics
• Biostatistics
• Mathematical Statistics
• +۴ مورد دیگر

Ben Boukai is a Chancellor's Professor in the Department of Mathematical Sciences at Indiana University and serves as Co-Director of the Biostatistics Ph.D. Program. His research focuses on mathematical statistics, with primary expertise in sequential analysis, where he develops parametric and non-parametric methodological frameworks for statistical inference problems. His work increasingly addresses random effects in nonlinear models applicable to statistical pharmacokinetics and econometrics, integrating hierarchical Bayesian modeling with structured parametric approaches. Key research areas include: Statistics Biostatistics Mathematical Statistics Sequential Analysis Bayesian Statistics Nonlinear Models Resampling Methods No scientific awards or student advisees were documented in the provided text. He maintains an active leadership role through the Biostatistics Ph.D. Program and conducts methodological research on resampling schemes for evaluating parameter estimate properties in small and large samples.

Brian Hugh Neelonمشاهده پروفایل

استاد

• Bayesian Methods
• Longitudinal Data Analysis
• Spatial Data Analysis
• +۳ مورد دیگر

Brian Hugh Neelon is a Professor and Graduate Training Director for Biostatistics in the Department of Public Health Sciences at the Medical University of South Carolina (MUSC). He holds joint appointments at the Ralph H. Johnson VA Medical Center and the Biostatistics Shared Resource at Hollings Cancer Center. His research focuses on developing Bayesian hierarchical models for longitudinal and spatial data, with applications spanning infectious/chronic diseases, maternal/child health, environmental health, and health services research. Research Interests: Bayesian inference, longitudinal/spatial data analysis, zero-inflated models, latent growth modeling, and health disparities. His work integrates statistical innovation with real-world public health challenges, such as COVID-19 surveillance and social vulnerability assessments. Publications: Recent articles emphasize Bayesian methods in spatiotemporal modeling of pandemics (e.g., COVID-19), zero-inflated data frameworks, and health equity. Key themes include geospatial analysis of disease spread, policy impacts on public health, and computational advances in biostatistics. Awards: Recipient of the AJPM 2021 Paper of the Year Award for research on political affiliations and COVID-19 outcomes. Education & Advising: As Graduate Training Director, he oversees biostatistics students and collaborates with interdisciplinary teams. No specific grant or lab details are provided in the source text.

• Machine Learning
• Remote Sensing
• Signal Processing
• +۳ مورد دیگر

Jianbo Liu is a prolific researcher with a focus on interdisciplinary fields spanning machine learning, remote sensing, signal processing, and computer vision. His work emphasizes developing advanced algorithms for applications in environmental monitoring, telecommunications, and data-driven decision-making. Liu collaborates frequently with institutions and researchers in China and internationally, contributing to journals like IEEE Transactions on Wireless Communications , Remote Sensing , and Pattern Recognition . Research Interests: Machine learning frameworks for computer vision tasks, remote sensing data analysis, signal processing in communication systems, and optimization of industrial processes. Key Collaborators: Fu Chen, Jimmy S. J. Ren, Ningyuan Cao, Hongsheng Li, and Boyang Cheng. Publications: Over 138 papers across venues such as CVPR, ICCV, and IEEE journals, demonstrating expertise in neural networks, sensor fusion, and spatiotemporal data analysis. His recent work highlights contributions to physics-informed machine learning, gesture recognition via hierarchical attention networks, and secure communication protocols for satellite systems. Liu’s research bridges theoretical advancements with practical applications in environmental science, healthcare, and smart infrastructure.

• Theoretical Physics
• Gravitational Wave Astronomy
• Particle Physics
• +۴ مورد دیگر

Juan Calderon Bustillo is a Ramón y Cajal Researcher at the University of Santiago de Compostela (USC), affiliated with the Galician Institute of High Energy Physics (IGFAE) in the Department of Particle Physics. He leads research within the TEOFPACC group, focusing on theoretical physics, gravitational wave astronomy, and astroparticle physics. His work addresses fundamental questions in black hole dynamics, neutron star mergers, and dark matter candidates using advanced numerical relativity techniques. Calderon Bustillo holds a PhD from the Universitat de les Illes Balears (2015), where his thesis on gravitational radiation from compact binaries laid groundwork for hybrid waveform constructions. Supervised by Dr. Sascha Husa and Dr. Alicia Magdalena Sintes Olives, his research bridges theoretical models with observational data from LIGO, Virgo, KAGRA, and GEO600 detectors. His research interests span gravitational wave signatures of mirror symmetry, ultralight boson mergers, and post-merger remnant properties. Key projects include analyzing recoil velocities in black hole mergers, probing dark matter through gravitational waves, and developing surrogate models for exotic compact object collisions. Calderon Bustillo has contributed to multi-messenger campaigns, such as Swift-BAT follow-ups of GW triggers, and searches for continuous gravitational waves from pulsars and magnetars. His recent work emphasizes third-generation detector readiness, exploring thermal effects in neutron star mergers and constraints on vector dark matter. Collaborations with international teams like EuCAPT highlight his role in shaping next-decade astroparticle physics priorities, focusing on cosmological tests and dark sector interactions.