جست‌وجوی استاد و پژوهشگر

Yi Huangمشاهده پروفایل

دانشیار

• Biostatistics
• Causal Inference
• Comparative Effectiveness Research
• +۹ مورد دیگر

Yi Huang is an Associate Professor in the Department of Mathematics and Statistics at UMBC since 2007, specializing in biostatistics with a focus on causal inference and public health. She holds a Ph.D. in Biostatistics from Johns Hopkins University (2007) and has been a key contributor to interdisciplinary research at the interface of statistics and public health/medicine/biology. Her work emphasizes methodological innovation in comparative effectiveness research, personalized medicine, and post-marketing safety evaluations. Education: Ph.D. in Biostatistics, Johns Hopkins University Bloomberg School of Public Health (2007) NASA Graduate Fellowship recipient (1999) for aerosol-cloud interaction research Research Interests: Causal inference methodologies, including latent propensity score frameworks Comparative effectiveness research (CER) and meta-analysis Applications in gerontology, environmental health, and maternal/child health Statistical design for evaluating traditional Chinese medicine Grants and Awards: NIH R01 Grant Co-PI ($897,348) for pediatric care coordination study (2015–2020) National Institute of Aging Grant ($77,289) for hip fracture studies (2014–2015) Two FDA Critical Path Grants ($50,000 each) for causal methodology development (2008–2011) NASA Graduate Fellowship (1999) Professional Activities: Member: American Statistical Association, International Biometric Society, American Gerontology Society NIH Grant Reviewer (NIDDK), FDA External Reviewer Editorial Board: Internet Journal of Mental Health Teaching and Mentoring: Developed courses in biostatistics principles, categorical data analysis, and longitudinal methods Advised over 20 graduate students across statistics and interdisciplinary programs Focus on critical thinking and real-world application in pedagogy

Florian Huberمشاهده پروفایل

استاد

• Bayesian Econometrics
• Macroeconomics
• State Space Modeling
• +۳ مورد دیگر

Florian Huber is a Professor of Economics and Vice-Head of the Department of Economics at the University of Salzburg. His research focuses on Bayesian macroeconometrics, particularly large-scale non-linear multivariate time series modeling. He has published in top journals such as the Journal of Econometrics and the Journal of Applied Econometrics. His work integrates machine learning and nonlinear methods to analyze macroeconomic dynamics and uncertainty. Huber serves as a Scientific Consultant to the Oesterreichische Nationalbank (OeNB), European Central Bank (ECB), and European Commission. He holds roles as Associate Editor of Macroeconomic Dynamics, Senior Scientist at the International Institute for Applied Systems Analysis (IIASA), and Research Fellow of Bocconi University’s Baffi Center. His accolades include the 2024 Kurt-Zopf-Förderpreis and Fellow status in the Society for Economic Measurement. Research interests span Bayesian econometrics, state space modeling, forecasting, and financial spillovers. His recent work addresses topics like growth-at-risk, nonlinear VAR models, and real-time inflation forecasting. Huber’s contributions bridge theoretical econometrics with policy-relevant analysis, emphasizing the integration of big data and structural models.

• Model-Driven Engineering
• Cloud Computing
• Fog Computing
• +۳ مورد دیگر

Hugo BRUNELIERE is an Enseignant Chercheur (Teaching & Researcher) at IMT Atlantique, Nantes, affiliated with the NaoMod team within the LS2N laboratory. His primary roles involve leading innovative projects on software and systems engineering, focusing on model-based techniques and their applications in Cloud/Fog Computing, Cyber-Physical Systems, and Digital Twins. He holds a position in the Department of Automation, Production and Computer Sciences (DAPI). His research spans Model-Driven Engineering (MDE), tool interoperability, and industrial collaboration through projects like MOSAICO, MATISSE, and AIDOaRt. He has collaborated with companies such as Orange, Sopra Steria, and BNP Paribas on R&D contracts. Research Interests : - Model Management & Traceability - Model-Based Reverse Engineering - Cloud/Fog System Architecture - Cyber-Physical Systems Design - Tool/Language Interoperability - Open Source Contributions (Eclipse MoDisco, ATL). Awards : Best Paper at SAC 2024 (VeriFog) Best Paper in SoSyM Journal 2018 (Model View Survey) Best Paper at CLOSER 2018 (Cloud Systems Modeling) Grants & Projects : European Projects: MOSAICO (AI Communities), MATISSE (Digital Twins), AIDOaRt (DevOps Automation) National Projects: SeMaFoR (Fog Management), CoMe4ACloud (Autonomic Clouds) He also leads open-source initiatives in the Eclipse community and organizes Eclipse DemoCamps. Labs & Teams : Part of the NaoMod team (LS2N) and actively contributes to the MegaM@Rt2 and ARTIST projects.

Li Chouمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Data Science
• +۳ مورد دیگر

Dr. Li Chou is an Assistant Professor of Computer Science in the College of Engineering at West Texas A&M University. He holds a B.S. in Computer Science and an M.B.A. from Southern Methodist University, and an M.S. and Ph.D. in Computer Science from The University of Texas at Dallas. Prior to joining WTAMU, he was a Postdoctoral Research Associate at Rice University's Department of Computer Science. His research focuses on artificial intelligence , machine learning , data science , and scientific computing , with applications in recommendation systems, genomic data analysis, and federated learning. His work emphasizes efficient algorithms, model compression, and optimization for large-scale data processing. Notable contributions include the Random Offset Block Embedding (ROBE) technique for recommendation systems and diversified sampling methods for genomic streams. Dr. Chou teaches courses such as Programming Principles II, Data Structures and Algorithms, Computer Organization, and Artificial Intelligence. He is affiliated with the university's ECORE Lab and Human-Machine Teaming Laboratory , contributing to interdisciplinary research initiatives in engineering and computer science. His publications span topics like neural network optimization, federated learning frameworks, and bioinformatics applications, demonstrating a commitment to advancing both theoretical and applied computing.

• Applied Labour Economics
• Labour Market Dynamics
• Econometric Analysis
• +۵ مورد دیگر

Martyn Andrews is a Professor of Applied Econometrics at the University of Manchester, affiliated with the School of Social Sciences and the Department of Economics. He holds editorial roles at the Manchester School and is a member of professional organizations such as the Royal Economic Society and European Association of Labour Economists. His research focuses on applied labour economics using large-scale datasets, including linked employer-employee data (LEEDs), with collaborations at institutions like the IAB (Nuremberg) and the University of Nottingham. Education and Career: PhD from the London School of Economics (1977–1983), followed by roles at the ESRC Macroeconomic Modelling Bureau (University of Warwick) and the Centre for Research on Globalisation and Economic Policy (University of Nottingham, part-time, 2001–2002). He has led postgraduate programs in the School of Social Sciences (2010–2012). Research Interests: Youth labour markets, unionised wage structures, work-sharing policies, education gender gaps, and regional migration. Current projects emphasize methodological advancements in LEEDs, supported by ESRC grants and international collaborations. Teaching: Courses include Cross Section Econometrics (ECON30341, ECON60052) and Applied Econometrics (ECON60512). Supervised PhD students include Obbey Elamin, Rabeya Khatoon, and Mohammad Rahman (all submitted 2013). Grants and Collaborations: Lead researcher on the ESRC Grant 'Ownership, wages, and spill-overs using linked employer-employee data' (2013), collaborating with Nottingham, Erlangen, and IAB researchers. His work contributes to UN Sustainable Development Goals through Digital Futures initiatives.

• Applied Mathematics
• Bayesian Inference
• Monte Carlo Methods
• +۵ مورد دیگر

Simon Cotter is a Professor of Applied Mathematics at The University of Manchester. His research focuses on Bayesian inference, stochastic modeling, and computational methods, with applications in biological systems (e.g., tendon mechanics, placental development) and financial modeling. He contributes to the university’s Digital Futures research beacon and collaborates across disciplines including biomechanics, computational biology, and data science. Research Interests: Bayesian data assimilation and parameter estimation Monte Carlo methods (e.g., multi-index, adaptive importance sampling) Multiscale stochastic systems and reaction networks Inverse problems in material science and biological systems Recent work highlights include developing NuZZ (a numerical Zig-Zag algorithm for general models) and advancing hierarchical Bayesian methods for data selection. His projects address challenges in debt recovery forecasting, placental development modeling, and cell cycle heterogeneity analysis. He co-leads a multi-modal pregnancy research project aimed at understanding stillbirth mechanisms and is actively involved in initiatives promoting gender equity in academia.

Mark Johnsonمشاهده پروفایل

استاد

• Ecohydrology
• Carbon Cycle
• Environmental Science
• +۳ مورد دیگر

Mark Johnson is a Professor at the University of British Columbia (UBC), holding dual appointments in the Institute for Resources, Environment and Sustainability (IRES) and the Department of Earth, Ocean and Atmospheric Sciences within the Faculty of Science. He also serves as the Canada Research Chair (Tier 2) in Ecohydrology , focusing on interdisciplinary water and carbon cycle research. His research emphasizes ecohydrological processes, land-use impacts, and sustainable management strategies under climate change. Johnson’s work spans tropical and temperate ecosystems, including the Amazon, Cerrado, and Pantanal biomes, as well as Canadian watersheds. Key areas include carbon sequestration dynamics, freshwater ecosystem health, and agricultural water efficiency. He collaborates with communities, governments, and industry to translate scientific insights into actionable policies and practices. His research interests are driven by innovative methods such as drone-based hyperspectral and thermal imaging, remote sensing integration, and Bayesian inference to study ecosystem productivity and water-carbon interactions. He has pioneered studies on biochar applications to improve soil fertility and water retention in drought-prone regions. Johnson’s scientific contributions include advancing understanding of CO2 and CH4 fluxes in wetlands, evaluating water footprints, and modeling evaporative demands. His work highlights the urgency of rethinking water and land management in agriculturally intensive regions like the tropics and the Lower Fraser Valley. Awards: Canada Research Chair (Tier 2, Ecohydrology) In teaching, Johnson instructs courses such as Global Aspects of Ecohydrology , Social-Ecological Systems , and Ecohydrology of Watersheds , bridging theoretical and applied environmental science. His grants and projects, including the AgWIT initiative, prioritize community-driven solutions for water security and sustainable agriculture. He leads the Ecohydrology Lab at UBC, coordinating with global networks like AmeriFlux and NASA’s ECOSTRESS mission to enhance large-scale ecohydrological monitoring. His team addresses pressing issues like climate adaptation, wetland restoration, and the socio-hydrological resilience of rural communities in Costa Rica and Brazil.

Rodney Strachanمشاهده پروفایل

استاد

• Econometric and Statistical Methods
• Econometrics
• Macroeconometric Analysis
• +۵ مورد دیگر

Professor Rodney Strachan is a Professor of Econometrics at the School of Economics, University of Queensland (UQ), within the Faculty of Business, Economics and Law. He holds a PhD from Monash University (2000) and has held prior positions, including at the Australian National University (ANU), where he served as Deputy Head of the Research School of Economics. His research focuses on Bayesian econometrics, time-varying parameter models, macroeconometric analysis, and dynamic factor models. He also serves as a Senior Fellow at the Rimini Centre for Economic Analysis and a Research Associate at the Centre for Applied Macroeconomic Analysis. Strachan's research interests include Bayesian methods, time series analysis, cointegration, and the development of large-scale macroeconomic models. His work addresses challenges in model uncertainty, parameter instability, and the application of stochastic volatility models. Notable projects include 'Large dynamic time-varying models for structural macroeconomic inference' (2018–2022) and 'Estimation of the continuous piecewise linear model and macroeconomic applications' (2012–2015). His publications span journals like the Journal of Econometrics, Journal of Business and Economic Statistics, and Studies in Nonlinear Dynamics and Econometrics. Recent work includes advancements in multivariate stochastic volatility models and dynamic factor models with drifting trends. He is actively involved in advising and mentoring graduate students, though specific advisee names are not listed here. Strachan’s contributions to econometric theory and macroeconomic modeling have been recognized through collaborations with institutions like the Rimini Centre for Economic Analysis and the Centre for Applied Macroeconomic Analysis. His research emphasizes methodological innovation, with a focus on practical applications to policy analysis and forecasting.

Debora Correaمشاهده پروفایل

مدرس ارشد

• Data-Driven Techniques
• Temporal Dataset Analysis
• Bioengineering
• +۳ مورد دیگر

Dr. Debora Correa is a Senior Lecturer at the University of Western Australia's School of Physics, Maths and Computing, specializing in Computer Science and Software Engineering. She holds a PhD in Applied Physics (Computational Physics) from the University of São Paulo (2012), an MSc in Computer Science from Universidade Federal de São Carlos (2008), and a BSc (Honours) in Computer Science from COC University (2005). Her research focuses on developing data-driven methodologies for analyzing large-scale temporal datasets across domains like bioengineering, biomedical signals, music, and engineering systems. Notable contributions include work on reservoir computing for concept drift detection and network inference algorithms. She leads the ARC Training Centre for Transforming Maintenance through Data Science and collaborates on projects like TSuNAMi: Time Series Network Animal Modelling. Dr. Correa has received awards including the 2020 Dr Vincent Harry Cooper Memorial Prize for supervising Jet Chong's honors thesis. Her work addresses UN Sustainable Development Goals through interdisciplinary applications. She actively engages in community initiatives like the Girls' Programming Network mentorship program.

H.A.L. Kiersمشاهده پروفایل

استاد

• Statistics
• Data Analysis
• Multivariate Statistics
• +۳ مورد دیگر

Prof. Henk A.L. Kiers is a Professor in Statistics and Data Analysis at the University of Groningen's Faculty of Behavioural and Social Sciences, within the Department of Psychometrics & Statistics. His research focuses on advanced statistical methodologies, particularly in multivariate analysis, three-way component analysis, and psychometrics. He has contributed extensively to data analysis techniques for large datasets, including methods for uncovering underlying structures in complex data matrices. Key research interests include Bayesian analysis, statistical education, and the application of statistical methods in psychology and psychiatry. He has held editorial roles and received the Psychometrika Reviewer Award in 2017. His work bridges theoretical advancements with practical applications, such as improving classification methods and addressing challenges in missing data analysis. Prof. Kiers is actively involved in academic collaborations, including projects on data fusion and the development of statistical software tools like the R package ThreeWay. His contributions to the field have been recognized through numerous high-impact publications and methodological innovations.

Quefeng Liمشاهده پروفایل

دانشیار

• High-dimensional statistics
• Biomedical research
• Omics data integration
• +۵ مورد دیگر

Dr. Quefeng Li is an Associate Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill (UNC-Chapel Hill), part of the Gillings School of Global Public Health. He earned his Ph.D. in Statistics from the University of Wisconsin-Madison (2013) and completed a postdoctoral fellowship at Princeton University (2013–2015). His research focuses on high-dimensional statistics and its applications in biomedical and neuroimaging data analysis, emphasizing robust methodologies and integrative data analysis techniques. Dr. Li's work has contributed to advancements in variable selection, data integration, and statistical methods for complex biomedical datasets. He has held editorial roles at journals like Biometrics and Biostatistics , and serves on multiple professional committees. His research has been supported by grants from the NIH, including R01AG073259 and R01CA282648, and he collaborates extensively with clinical researchers across disciplines. His research interests span high-dimensional statistics, neuroimaging analysis, and robust statistical methods. Notable contributions include developing the ARTdeConv algorithm for cell-type deconvolution and the glmmPen package for penalized generalized linear mixed models. He has advised numerous PhD students, many of whom now work in industry and academia. Awardees include the Gillings Research Excellence Award (2023), Junior Faculty Development Award (2018), and University Research Council Award (2016). His work bridges theoretical statistics with practical biomedical applications, addressing challenges in data heterogeneity and reproducibility.

Panos Deloukasمشاهده پروفایل

استاد

• Cardiovascular Genomics
• Genetic Epidemiology
• Molecular Biology
• +۳ مورد دیگر

Professor Panos Deloukas is the Director of the William Harvey Research Institute and a Professor of Cardiovascular Genomics at Queen Mary University of London, within the Faculty of Medicine and Dentistry. He holds a BSc in Chemistry from the Aristotle University of Thessaloniki, an MSc in Microbiology from Paris 7, and a PhD in Genetics from the Biozentrum University of Basel (1991). His career includes pioneering roles in the Human Genome Project and International HapMap Project, followed by research into genetic basis of common diseases and drug response variability. Research interests focus on molecular mechanisms of coronary artery disease (CAD), lipid metabolism, and epigenetic factors in disease progression. He leads studies on gene-environment interactions using large-scale genomic datasets from UK Biobank and international consortia. Notable contributions include identifying over 70 CAD/MI risk loci and developing biomarkers for cardiovascular risk stratification. Awards: Thomson Reuters Highly Cited Researcher (Molecular Biology & Genetics, 2012–present) Top 200 influential scientists in Molecular Biology & Genetics (2002–2012) Top 1% Highly Cited Researcher (2014–2016) Grants and sponsors include British Heart Foundation (BHF), National Institute for Health Research (NIHR), and Horizon 2020. Collaborations involve global consortia such as CARDIoGRAMplusC4D, Global Lipids Genetics Consortium, and UK Biobank Cardiometabolic Consortium. His research team includes Dr. Stavroula Kanoni, Dr. Andrew Smith, and BHF PhD student Olga Giannakopoulou. Key projects investigate epigenetic biomarkers in blood for MI recurrence prediction and functional genomics of CAD risk variants.

Dino Distefanoمشاهده پروفایل

استاد

• Software Verification
• Program Analysis
• Static Analysis
• +۳ مورد دیگر

Professor Dino Distefano is a Professor of Software Verification at Queen Mary University of London's School of Electronic Engineering and Computer Science. He is currently on leave at Anthropic, focusing on connections between large language models and program verification. His research centers on automatic program verification, leveraging separation logic and scalable static analysis tools like Infer, which is widely used at Meta and other tech giants. Distefano co-founded Monoidics, acquired by Facebook in 2013, and leads WhatsApp's Dev Infra Org, developing tools to enhance software reliability and security. Education: PhD from the University of Twente, Netherlands. Awards include the IEEE Cybersecurity Award (2021), ACM SIGPLAN POPL 2019 Most Influential Paper Award, and Royal Academy of Engineering Silver Medal (2014). His work spans formal methods, program analysis, and industrial-scale verification tools. Publications focus on compositional verification, dynamic/static analysis integration, and privacy-aware code analysis. He has contributed to over 50 conferences and workshops, including key roles on program committees for VMCAI, ECOOP, and CAV.

Tom Luk R Michoelمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• Machine Learning
• +۳ مورد دیگر

Tom Luk R Michoel is a Professor at the Computational Biology Unit within the Department of Informatics at the University of Bergen. His research focuses on bioinformatics, computational biology, and machine learning applied to understanding gene regulation and causal relationships in biological systems. He teaches in both the Bachelor and Master programs in Informatics, including courses like BINF301 and MNF130. Research Interests: Michoel’s work explores how genetic variation influences gene expression and disease mechanisms. He develops machine learning algorithms to infer causal gene regulatory networks from large-scale genomic data, emphasizing causal inference over mere correlations. His recent projects include analyzing plasma protein networks linked to cardiovascular disease and applying Bayesian networks to understand gene-disease associations. Publications: His most recent work (2025) focuses on causal protein networks in myocardial infarction risk and network-driven frameworks for coronary artery disease studies. He also contributes to methodological advancements like integrating graph neural networks with metabolic models. Current Activities: Michoel leads the development of tools like Findr.jl for network inference and teaches short courses on causal inference in drug discovery. His lab collaborates on projects involving single-cell analysis, multi-tissue genomics, and systems pharmacology.

Scott Saleskaمشاهده پروفایل

استاد

• Global Change Ecology
• Biogeochemical Cycles
• Microbial Ecology
• +۵ مورد دیگر

Scott Saleska is a Professor in the Department of Ecology & Evolutionary Biology at the University of Arizona. He holds a joint appointment in Soil, Water, and Environmental Sciences (SWES) and is a Faculty Affiliate at the Institute of Environment (IE). His research focuses on understanding how terrestrial vegetation and microbial communities regulate large-scale biogeochemical processes, particularly their interactions with climate systems. Saleska’s expertise spans global change ecology, remote sensing, and microbial meta-omics, with a strong emphasis on scaling ecological processes from individual organisms to global scales. Education includes a Ph.D. in Energy and Resources from UC Berkeley (1998) and a B.S. in Physics from MIT (1986). He has held postdoctoral roles at Harvard University and has been at the University of Arizona since 2005, advancing through ranks from Assistant to Full Professor. His honors include election as a Fellow of the Ecological Society of America (2019) and the Agnese Nelms Haury Faculty Fellowship (2014–present). Research interests integrate ecophysiology, atmospheric science, and computational modeling to study how ecosystems influence climate. Key projects include analyzing methane dynamics in Arctic peatlands, tropical forest resilience to drought, and microbial responses to permafrost thaw. Saleska’s work often employs cutting-edge tools like meta-omics and satellite remote sensing, emphasizing interdisciplinary approaches to address global environmental challenges. His publications highlight themes such as microbial community controls on methane emissions, tropical forest phenology, and the interplay between vegetation structure and biogeochemical cycles. Collaborative projects, including studies at Stordalen Mire (Sweden) and Amazonian forests, underscore his commitment to field-based research and global climate modeling. Awards reflect his contributions to ecology and environmental justice, while his teaching and mentorship roles further his impact on the next generation of environmental scientists. Saleska’s work bridges fundamental research and policy-relevant outcomes, addressing urgent questions about Earth’s changing systems.