جست‌وجوی استاد و پژوهشگر

• Ecological Modelling
• Agent-Based Systems
• Epidemiological Modelling
• +۴ مورد دیگر

Dr. Vianney Sicard is a Researcher in the Department of Ecological Modelling at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany, leading the Ecological Epidemiology research group. He holds a PhD in Epidemiological Modelling and Artificial Intelligence (2019-2022) from INRAE, Nantes, and prior engineering degrees in computer science (2018, École Polytechnique de l'Université de Tours) and software design (2012). His research focuses on agent-based modelling, ecological epidemiology, and computational epidemiology, including applications to livestock disease dynamics and multi-level simulation frameworks. Key contributions include the EMULSION modelling framework and work on Bayesian networks for veterinary diagnostics. Sicard's interdisciplinary work bridges computer science, environmental science, and public health, with publications in journals like PLoS Computational Biology and PAAMS. He collaborates with teams like FORMIND and EcoEpi, addressing challenges in sustainable systems and policy design under global change.

• Consumer Behavior
• Brand Management
• Global Marketing
• +۳ مورد دیگر

Dr. Xuan Huy Nguyen is an award-winning Associate Professor in Marketing at the University of Sussex Business School, specializing in consumer behavior, brand management, and global marketing strategies. His research integrates quantitative methodologies like choice modeling and Bayesian statistics, with a focus on industries such as sports and automobiles. He holds a PhD in Marketing from the University of New South Wales and serves as an External Examiner at the University of East Anglia. Dr. Nguyen is a Fellow of the Higher Education Academy and advises Harvard Business Review's Advisory Council. His teaching innovations, including cross-disciplinary approaches and inclusive education strategies, earned him the 2023 Teaching to Disrupt Award. He has also been a finalist in multiple Sussex Education Award categories (World Readiness, Inclusive Sussex, Better World) and contributed to global sustainability goals like Climate Action and Responsible Consumption. His academic journey spans roles from Lecturer (2017) to Senior Lecturer (2024) before his current position. Research interests include entrepreneurial passion’s impact on innovation, consumer decision-making processes, and brand strategy in dynamic markets. He actively publishes case studies on automotive and beverage industries, alongside presenting at leading conferences such as INFORMS and the European Marketing Academy. Award highlights include the World Readiness Award finalist status (2023) and sustained recognition for bridging academic rigor with real-world relevance. His multilingual proficiency (English, Japanese, Vietnamese) and international experience inform his globally oriented teaching modules.

Marta Nai Rusconeمشاهده پروفایل

دانشیار

• Statistics
• Data Analysis
• Environmental Economics
• +۳ مورد دیگر

Marta Nai Ruscone is an Associate Professor in the Department of Economics at the University of Genoa. She specializes in statistical methodologies, particularly focusing on copula models, data analysis, and their applications in environmental economics and econometrics. Her teaching responsibilities include courses such as Advanced Data Analytics, Statistical Forecasting, and Statistics for Business Economics. Her research emphasizes developing and applying copula-based techniques for clustering, dependence analysis, and modeling complex economic and environmental datasets. Key areas include studying the influence of economic sectors on financial markets, analyzing the dynamics of human development indicators, and exploring relationships between environmental factors and pandemic outcomes. She has also contributed to open-source statistical software like the R package OBsMD for Bayesian model discrimination. Dr. Nai Ruscone’s work bridges theoretical statistical advancements with practical applications, addressing challenges in multivariate analysis, ordinal data modeling, and interdisciplinary problem-solving. Her publications reflect a strong commitment to advancing methodologies in statistics and their relevance to real-world economic and environmental issues.

Katrien Antonioمشاهده پروفایل

استاد

• Insurance Analytics
• Data Science
• Predictive Modeling in Insurance
• +۳ مورد دیگر

Katrien Antonio is a **full professor** in actuarial science and insurance analytics at **KU Leuven** and holds a **part-time professorship** in actuarial data science at the University of Amsterdam. She leads the **Insurance Research Group** and chairs the **Department of Accountancy, Finance and Insurance** within the **Faculty of Economics and Business (FEB)**. Her roles include directing the LRISK research center and overseeing education commissions at KU Leuven’s Leuven and Kortrijk campuses. **Research Interests**: Insurance analytics, data science, predictive modeling in insurance, actuarial science, and sustainable finance. Key projects include designing inclusive insurance products, compliant actuarial models, and risk analytics for societal impact. **Publications**: Her recent work focuses on environmental impacts on mortality, neural networks for insurance pricing, fraud detection, and IoT-driven maintenance risk assessment. She has published in top journals like *Journal of the Royal Statistical Society*, *North American Actuarial Journal*, and *European Journal of Operational Research*. **Education**: PhD in Mathematics (KU Leuven, 2007), with prior degrees in Mathematics from the same institution. Extensive teaching experience across higher education, emphasizing data-driven insights and innovative materials. **Grants & Projects**: Leads initiatives such as the “Actuaries and STatisticians” project (2022–2025) and “VALERIA” (2021–2024), focusing on emerging risks and sustainable finance. **Labs & Teams**: Directs the LRISK center, fostering interdisciplinary collaboration in risk analysis. Active in academic networks, including IMAC and the Faculty Board of Economics and Business.

• Structural Engineering
• Civil Engineering
• Risk-Based Asset Management
• +۹ مورد دیگر

Marios Chryssanthopoulos is a Professor of Structural Systems at the University of Surrey, affiliated with the School of Sustainability, Civil and Environmental Engineering. His academic journey includes a BSc from the University of Newcastle, an MS from MIT, and a PhD from Imperial College London. He has held roles such as Director of the Engineering Materials and Structures Research Centre and Head of the Division of Civil, Chemical and Environmental Engineering. His research focuses on structural reliability, risk-based asset management, and probabilistic modeling in infrastructure systems. Research interests include the influence of uncertainties in structural performance, corrosion effects on metallic structures, and integration of structural health monitoring (SHM) with decision-support tools. He has supervised over 25 PhD students and collaborated with industry on projects like fatigue life prediction of bridges and offshore structures. Funding sources include EPSRC, the EU, and industry partners. Teaching responsibilities include courses on structural safety, bridge management, and advanced composites. His work extends to editorial roles for journals like Structural Safety and participation in national codification committees (e.g., UK SCOSS, Eurocode 3 Drafting Panel). Key contributions include methodologies for corrosion detection in tubular structures, probabilistic frameworks for fatigue assessment, and frameworks enhancing building robustness through segmentation. His experimental work spans structural dynamics, material degradation, and consequence analysis of failures.

• Application of statistical methods
• Bayesian inference
• Data mining
• +۶ مورد دیگر

Dr. Kasia Bijak is a Lecturer in Management Science at the Southampton Business School, University of Southampton. Her research focuses on applying statistical methods (including Bayesian inference), data mining, artificial intelligence, stochastic processes, and econometrics to address challenges in credit scoring and healthcare analytics. She has contributed to dynamic consumer risk modeling, affordability assessment, and LGD model validation. Education: MSc in Quantitative Methods and Information Systems, Warsaw School of Economics (2004) PhD in Management Science, University of Southampton (2013) Her research explores dynamic modeling techniques such as Kalman filters and random effects in consumer credit risk assessment. She has also applied scoring methodologies in intensive care and cancer care services optimization. Teaching includes modules on business analytics and SAS programming. Research Highlights: Development of Bayesian models for car lease fraud detection Evaluation of classification tools in healthcare patient risk stratification Quantitative analysis of university research impact frameworks No scientific awards listed. Current role includes supervision of PhD candidates (details pending) and active engagement in operational research applications across financial and healthcare sectors.

Anqi Wuمشاهده پروفایل

استادیار

• Machine Learning
• Computational Neuroscience
• Probabilistic Modeling
• +۳ مورد دیگر

Anqi Wu is an Assistant Professor at the School of Computational Science and Engineering (CSE) within the College of Computing at Georgia Institute of Technology. She previously held a postdoctoral fellowship at Columbia University’s Zuckerman Mind Brain Behavior Institute and earned her Ph.D. in Computational and Quantitative Neuroscience from Princeton University, with a graduate certificate in Statistics and Machine Learning. Her research focuses on developing probabilistic modeling approaches and scalable inference algorithms for neural and behavioral analysis, with applications in robotics and real-world problems. Education: Ph.D., Computational and Quantitative Neuroscience, Princeton University (2014–2019) Graduate Certificate in Statistics and Machine Learning, Princeton University Postdoctoral Fellow, Center for Theoretical Neuroscience, Columbia University (2019–2022) Research Interests: Probabilistic Modeling for Neural Latent Discovery: Developing disentangled generative models to extract interpretable neural representations. Behavior Analysis: Extracting multi-layered information from animal/human behavior via hierarchical models and reinforcement learning. Efficient Inference: Advancing Bayesian neural networks, Gaussian processes, and scalable algorithms. Her work bridges machine learning, neuroscience, and robotics, with applications in brain decoding, motor control, and reward learning. Publications Trends: Recent work emphasizes reinforcement learning (e.g., EmoBipedNav for emotion-aware robotics), generative models (diffusion models for neural dynamics), and Bayesian methods (Gaussian processes for multi-region brain analysis). Applications span robotics, neuroscience, and spatio-temporal data modeling. Affiliations: Director, BRAINML Lab (BRAIN Intelligence and Machine Learning) Affiliated with Machine Learning Center, Neuro @ GT, and Bioengineering at Georgia Tech

• Hydrogels
• Biomaterials
• Supramolecular Chemistry
• +۴ مورد دیگر

Martin G.T.A. Rutten is a University Researcher in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), specializing in Biomedical Materials and Chemistry. His research focuses on developing advanced biomaterials, particularly hydrogels with tunable properties for biomedical applications. Rutten's research interests span multiple domains within biomaterials science and engineering. He specializes in hydrogel engineering , focusing on supramolecular chemistry approaches to create dynamic biomaterials with precisely controlled physical properties. His work integrates principles from polymer science, materials chemistry, and tissue engineering to develop synthetic matrices that mimic natural biological environments. Rutten investigates how molecular design influences macroscopic material properties, particularly examining hydrogen bonding interactions, self-assembly processes, and the relationship between biochemical complexity and cellular response. Analysis of Rutten's recent publication record (2018-2025) reveals a strong focus on supramolecular biomaterials, particularly hydrogels with dynamic properties. His research demonstrates an evolving trajectory from fundamental material characterization toward increasingly complex biological applications, including kidney organoid development and cardiac tissue engineering. The work consistently emphasizes the engineering of material properties at the molecular level to achieve specific biological outcomes, with growing attention to clinical translation potential.

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.