جست‌وجوی استاد و پژوهشگر

• Gravitational Wave Astronomy
• Data Analysis
• Quantum Mechanics
• +۵ مورد دیگر

Dr. John T. Whelan is a Professor at the Center for Computational Relativity and Gravitation within the School of Mathematical Sciences at Rochester Institute of Technology . His work spans gravitational wave data analysis, quantum mechanics, and Bayesian statistical methods, with a focus on continuous wave sources like Scorpius X-1 and applications in sports analytics via the Bradley-Terry model. Research Interests: Gravitational waves from compact binaries, Bayesian inference, computational methods for parameter estimation, stochastic background searches, and educational applications of Jupyter notebooks Notable Projects: Development of cross-correlation search techniques for gravitational waves, quantum decoherence modeling, and statistical teaching tools Advising: Mentored graduate students Yuanhao Zhang and undergraduates Jackson Henry, Anthony Castiglia, and Marc Cenac in gravitational wave research Publications: 15+ recent works on gravitational wave detection, including presentations at major conferences like GR22/Amaldi 13 and LSC-Virgo-KAGRA meetings Teaching Innovation: Pioneered use of Jupyter notebooks for online graduate statistics instruction, integrating code, visualizations, and mathematical derivations His work demonstrates the intersection of computational physics, statistical inference, and pedagogical innovation, with ongoing contributions to both gravitational wave astronomy and Bayesian methodology.

Quentin Gronauمشاهده پروفایل

مدرس

• Bayesian Statistics
• Mathematical Psychology
• Open Science
• +۴ مورد دیگر

Quentin Gronau is a Lecturer at the School of Psychological Sciences , University of Newcastle , and a member of the Newcastle Cognition Lab. He earned his PhD in 2021 from the University of Amsterdam under Prof. Eric-Jan Wagenmakers, contributing to the JASP project. His academic journey includes postdoctoral roles at the University of Amsterdam, University of Tasmania, and the University of Newcastle. Research Interests: Mathematical psychology, Bayesian statistics, computational cognitive modeling, open science, and shared mental models for human-bot cybersecurity teams. Key Contributions: Development of Bayesian model comparison techniques (e.g., warp-III bridge sampling), meta-analysis methodologies, and statistical software tools like JASP . Collaborations: Worked with Prof. Scott Brown, Dr. Ami Eidels, Dr. Dora Matzke, Prof. Andrew Heathcote, and Dr. Guy Hawkins. Grants: Lead on a $341,470 NWO grant (2016-2020) and investigator on a $46,305 BITSS grant (2016-2017).

• Decision making
• Cognitive processes
• Mathematical psychology
• +۷ مورد دیگر

Dr. Guy Hawkins is an Associate Professor in the School of Psychological Sciences at the University of Newcastle, Australia. His research focuses on developing and testing computational and mathematical models of cognitive processes, with a primary interest in decision-making. His work spans from low-level speeded perceptual decisions through to high-level cognition, including statistical reasoning and consumer preferences. Dr. Hawkins earned his PhD in 2013 and his Bachelor of Psychology (Honours 1) in 2008, both at the University of Newcastle. Prior to his current position, he held postdoctoral research positions at the University of Amsterdam's Brain and Cognition Center (2014-2016) and UNSW Sydney (2013-2014). In 2017, he was awarded an Australian Research Council Discovery Early Career Researcher Award (DECRA). His research examines the decision mechanisms and strategies that people use to select consumer products and service options. A key finding is that people make fewer reasoning errors when information is presented as counts ('8 out of 10') rather than probabilities ('80%'). This has practical applications in healthcare, where physicians could describe prognoses using counts to help patients make better-informed treatment choices. His work also investigates how people update their decision caution relative to time constraints and option quality. Dr. Hawkins' recent publications reveal a strong focus on evidence accumulation models, decision-making under time pressure, and the relationship between cognitive processes and neural activity. His research often employs computational modeling approaches to understand the psychological processes underlying decision behavior. 2024 John Keats Early Career Award, Society for Mathematical Psychology 2020 William K. Estes Early Career Award, Society for Mathematical Psychology 2018 Fellow of the Psychonomic Society 2017 Australian Research Council Discovery Early Career Researcher Award 2017 Vice-Chancellor's Award for Early Career Research and Innovation Excellence, University of Newcastle 2013 Clifford T. Morgan Best Article Award, Psychonomic Society Dr. Hawkins collaborates extensively with researchers across the globe, including at universities in Australia, USA, Canada, UK, The Netherlands, and Norway. He is part of the Newcastle Cognition Laboratory and the Functional Neuroimaging Laboratory. His current research projects include investigating cognitive neuroscience frameworks for attentional control, evaluating human-machine interfaces in vehicles, and studying perceptual inference anomalies in schizophrenia.

• Cognitive Psychology
• Human Memory
• Skill Acquisition
• +۳ مورد دیگر

Andrew Heathcote is a Professor at the University of Newcastle's School of Psychology, specializing in cognitive psychology, memory, and evidence accumulation models. He founded the Newcastle Cognition Laboratory (www.newcl.org) and focuses on understanding neural and cognitive processes in rapid decision-making, skill acquisition, and attention. Research Expertise: Human memory, Bayesian cognitive modeling, conflict recognition, and decision-making under pressure. Key Contributions: Developed the Linear Ballistic Accumulator (LBA) model, advanced Bayesian estimation methods for cognitive models, and explored multitasking and cognitive control through neuroimaging. Scientific Awards: Andrew McGhie Memorial Prize (1990), Commonwealth Graduate Fellowship (1986), ARC Professorial Fellowship (2011), and election to the Australian Academy of Social Sciences (2012). Grants & Collaborations: Multiple ARC Discovery Projects, collaborations with international researchers like Scott Brown and Frini Karayanidis. Supervision: Supervises honours, master's, and PhD students in cognitive psychology and neuroscience. Publications: Over 200 journal articles and chapters, with recent work on evidence accumulation, cognitive control, and dynamic modeling in high-impact journals like Trends in Cognitive Sciences and Psychonomic Bulletin & Review .

Claus-Eberhard Liedtkeمشاهده پروفایل

استاد

• Image Analysis
• Remote Sensing
• Industrial Automation
• +۳ مورد دیگر

Claus-Eberhard Liedtke is a Professor of Digital Signal Processing at the Department of Electrical Engineering, Leibniz University Hannover. He has held academic positions at the University of Minnesota (Assistant Professor, 1973–1976) and lectured as a Visiting Professor at Zhejiang University, China (1985). His work focuses on knowledge-based image analysis, 3D scene modeling, and remote sensing applications in industrial automation. Education: Diplom (1968) and Dr.-Ing. (1972) in Electrical Engineering from TU Berlin. Research Interests include automated knowledge acquisition for image systems, semantic networks in remote sensing interpretation, and multisensor fusion for 3D geometry reconstruction. His publications span hierarchical pose estimation, GIS-data integration, and Bayesian reasoning in vision systems. Publications Trends highlight expertise in remote sensing (7/15), 3D modeling (5/15), knowledge-based systems (6/15), and sensor fusion (3/15). Key subfields include topographic dataset verification, multitemporal analysis, and lifelike 3D human modeling. Professional Affiliations include EURASIP, EUROGRAPHICS, GI, ITG, and DAGM. He chairs the ITG working group on Pattern Recognition and serves on the Signal Processing journal’s editorial board, with over 90 publications and 30 grants from DFG, EU, and industry.

Marius Lindauerمشاهده پروفایل

استاد

• AutoML
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Marius Lindauer is a Professor of Machine Learning at the Department of Artificial Intelligence , Leibniz University Hannover , and Deputy Head of the Institute since 2025. Previously, he served as Spokesperson of Computer Science Professors (2023-2025) and Head of the Institute (2022-2024). PhD (Dr. rer. nat, 2010-2015), Master (2008-2010), and Bachelor (2005-2008) in Computer Science from University of Potsdam His research focuses on democratizing AI through AutoML innovations, including: Green AutoML for sustainable deep learning Human-Centered AutoML for user-centric optimization Dynamic Algorithm Configuration in reinforcement learning Generalization techniques for production and health applications Recent publications show strong multi-objective optimization trends across medical imaging , protein design , and time series forecasting , with 15+ papers in 2024-2025 at venues like NeurIPS, AAAI, and IEEE TPAMI. Key scientific awards : ERC Starting Grant (2022), NeurIPS BBO-Challenge winner (2020), multiple AutoML/ML competition victories Advisory role in 140+ publications and leadership of LUHAI Institute

• Development Economics
• Social Protection
• Migration Studies
• +۳ مورد دیگر

Jules Gazeaud is a CNRS Research Fellow at the Centre d'Études et de Recherches sur le Développement International (CERDI), affiliated with Clermont Auvergne University and Institut Convergences Migrations. His research spans development economics with a focus on social protection systems, migration, and field experiments across African contexts including Morocco, Tunisia, Comoros, and Egypt. Dr. Gazeaud's research interests center on the effectiveness of social protection programs in developing countries. His work examines conditional cash transfer programs, index insurance for smallholder farmers, migration patterns, and poverty targeting mechanisms. He employs rigorous empirical methods including randomized controlled trials, regression discontinuity designs, and Bayesian hierarchical models to evaluate program impacts and understand underlying mechanisms. His recent publications (2023-2025) reveal a strong focus on the intersection of social protection and education outcomes, with several studies examining Morocco's Tayssir program. His work demonstrates how increased school enrollment through cash transfers creates pressure on educational resources, leading to challenges in learning quality. Other research strands investigate gender dimensions of cash transfer programs in Tunisia, migration effects in Comoros, and the limitations of index insurance for small farmers. Dr. Gazeaud has established significant research collaborations with institutions including the World Bank and IDinsight. His fieldwork demonstrates deep engagement with local contexts, particularly in North Africa and the Indian Ocean region. His research methodology combines theoretical economic modeling with extensive primary data collection through field experiments and household surveys.

Gracia Yunruo Dongمشاهده پروفایل

استادیار

• Healthcare Equity
• Population Health
• Computational Statistics
• +۷ مورد دیگر

Gracia Yunruo Dong is an Assistant Professor in the Teaching Stream at the University of Toronto, affiliated with the Department of Cell & Systems Biology and the Department of Statistical Sciences. She also collaborates with Island Health on healthcare equity research. PhD, MMath, and BMath in Statistics from the University of Waterloo CANSSI Distinguished Postdoctoral Fellow (2022–2024) at University of Toronto and University of Victoria Her research spans Computational Statistics , Healthcare Equity , Quasi-Monte Carlo Methods , and Population Health , with a focus on marginalized populations. She develops capture-recapture models for homeless populations, applies case-crossover designs to environmental health, and explores quasi-random number generation for high-dimensional integration. Her publications reflect interdisciplinary collaboration, combining Bioinformatics , Public Health , and Statistical Computing . Recent work addresses gaps in healthcare access, overdose prediction, and simulation experiments. CANSSI Distinguished Postdoctoral Fellowship NSERC funding for SMMEID network Dong teaches courses such as Epidemiology of Health & Disease and The Practice of Statistics I , integrating real-world applications into her instruction. She collaborates with researchers like Patrick Brown and Laura Cowen, applying statistical methods to ecological and human health challenges. Her affiliations with Vancouver Island Health Authority’s Applied Health Data Analytics team highlight her commitment to addressing health disparities through data-driven solutions.

Attila Lazarمشاهده پروفایل

استاد

• Population Estimation
• Geospatial Analysis
• GIS and Machine Learning
• +۲ مورد دیگر

Professor Attila Lazar (PhD, MSc, BEng) is a faculty member at the University of Southampton , specializing in geospatial research and population modeling. His work integrates GIS, machine learning, and public health planning, with a focus on low- and middle-income countries. Research Interests : Population estimation, geospatial data validation, poverty modeling, and health program spatial optimization. Projects : Leads GRID3 initiatives funded by the Bill & Melinda Gates Foundation, focusing on high-resolution demographic mapping and small area population estimation. Awards : Winner of the 2019/2020 Dean’s Enterprise Award. Students : Supervises PhD candidates in Geography, including Sonnia-Magba and Bu-Buakei Jabbi.

• Machine Learning
• Robotics
• Autonomous Driving
• +۳ مورد دیگر

Jatan Shrestha is a Doctoral Researcher at Aalto University, affiliated with the Department of Electrical Engineering and Automation. His research focuses on applying machine learning techniques to solve complex problems in robotics and autonomous systems. Department: Electrical Engineering and Automation Email: jatan.shrestha@aalto.fi Research Interests: Jatan's work spans machine learning, robotics, and optimization. Key areas include autonomous driving algorithms, multi-agent trajectory planning, visibility-aware navigation, and GPU-accelerated computing for real-time robotic applications. Publication Trends: His research output demonstrates expertise in applying machine learning to hardware optimization (2024), advancing autonomous driving algorithms in dense traffic scenarios (2023), and developing GPU-based optimization frameworks for robotic navigation (2021-2022).

Thao Nguyenمشاهده پروفایل

استادیار

• Robotics
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Thao Nguyen is an Assistant Professor in the Department of Computer Science at Haverford College . She focuses on robotics and artificial intelligence, particularly in areas like visual object search, language-conditioned observation models, and hierarchical planning systems. Her office is located at KINSC L303. Her research bridges human-guided robot learning with advanced state abstractions and multimodal interaction frameworks. Key contributions include integrating gesture-based inputs and natural language queries into robotic object retrieval systems, enabling more intuitive human-robot collaboration. Recent publications highlight her work on language-visual alignment , contextual robotics , and non-Markovian planning . Her projects leverage Bayesian inference, deep learning, and temporal logic to enhance robot perception and task execution.

Scott L. Zegerمشاهده پروفایل

استاد

• Biostatistics
• Environmental Statistics
• Epidemiologic Statistics
• +۷ مورد دیگر

Scott L. Zeger is the John C. Malone Professor of Biostatistics and Medicine at Johns Hopkins Bloomberg School of Public Health and School of Medicine . He co-directs the Hopkins inHealth initiative and leads methodological research in biostatistics. PhD, Princeton University (1982) MS, Drexel University (1978) BA, University of Pennsylvania (1974) His research focuses on developing statistical models for longitudinal data analysis , Bayesian hierarchical models , and individualized health applications in global disease epidemiology and clinical outcomes. Recent publications emphasize global health (PSERENADE pneumococcal vaccine studies), diabetes monitoring (CGM metrics), and AML prognosis (measurable residual disease analysis). Scientific Awards Karl Pearson Award (2015) Samuel S. Wilks Award (2008) Marvin Zelen Leadership Award (2007) National Academy of Sciences Member (2006) Zeger serves as principal investigator for projects like PERCH pneumonia etiology research and Johns Hopkins Center for Global Health . He has mentored numerous doctoral students in biostatistics and epidemiology.

Dan Cooleyمشاهده پروفایل

استاد

• Extreme Value Analysis
• Spatial Statistics
• Environmental Statistics
• +۳ مورد دیگر

Dan Cooley is a Professor and Graduate Director in the Department of Statistics at Colorado State University. He is affiliated with the College of Natural Sciences and focuses on extreme value analysis, particularly tail dependence modeling and environmental risk assessment. Extreme value theory Multivariate extremes Heavy-tailed phenomena Spatial statistics Meteorological/environmental applications His research has been recognized with awards including ASA Fellow (2023), Distinguished Achievement Award from the ASA Section on Statistics and the Environment (2023), and the College of Natural Sciences Professor Laureate (2017-2019). Collaborations include work with the National Center for Atmospheric Research and Lawrence Berkeley National Labs. cooleyd@rams.colostate.edu Office: Statistics 217, Fort Collins, CO 80523-1877

James Albert Fosterمشاهده پروفایل

استاد

• Bayesian Statistics
• Sports Analytics
• Statistical Computing
• +۱ مورد دیگر

Dr. James Albert Foster is a Professor of Mathematics and Statistics in the College of Arts and Sciences at Bowling Green State University (BGSU), honored as Distinguished University Professor in 2018 for his lifetime contributions to statistics and the university. Education: Ph.D. in Statistics, Purdue University Research Interests: An internationally renowned expert in Bayesian statistics, Foster pioneered the application of statistical methods to sports analytics—particularly baseball—revolutionizing quantitative literacy in athletics. His work bridges theoretical statistics, computational methods (notably R software development), and innovative pedagogy, with seminal contributions to Bayesian regression and hierarchical modeling that transformed statistical practice beyond academia. Scientific Awards: Distinguished University Professor (BGSU, 2018) ASA Founders Award (2015) Fellow of the American Statistical Association (2000) Grants & Academic Leadership: Secured $800,000 from NSF and Ohio state funds to establish BGSU's undergraduate Data Science major, co-developing courses like Computing with Data and Statistical Programming. As departmental undergraduate coordinator, he authored four foundational textbooks and expanded graduate offerings including Bayesian Statistical Inference. His editorial leadership spans The American Statistician , Journal of Quantitative Analysis of Sports , and ASA committees focused on education and sports statistics. Professional Impact: Foster's 14 books and 104 journal articles (7,000+ citations) catalyzed Bayesian methods' adoption across scientific disciplines. His blog and R-focused publications democratized sports data analysis, while workshops and curriculum reforms elevated statistics education from high school to graduate levels globally.

Paul Bürknerمشاهده پروفایل

استاد

• Bayesian Statistics
• Probabilistic Programming
• Computational Statistics
• +۵ مورد دیگر

Paul Bürkner is a Full Professor of Computational Statistics at TU Dortmund University , focusing on probabilistic (Bayesian) methods. His research sits at the intersection of statistics and machine learning, with applications across quantitative sciences. Key Roles : Developer of the brms R package, member of the Stan and BayesFlow development teams. Research Pillars : Bayesian inference, uncertainty quantification, amortized workflows, simulation-based inference, and probabilistic programming. His lab advances methods for prior specification, model evaluation, and scalable inference, collaborating on applications from cognitive science to ecology. Recent work emphasizes neural superstatistics and BayesFlow for efficient mixture and multilevel models. Students and researchers are encouraged to reach out for collaboration or thesis opportunities. Key Labs/Teams : BayesFlow Development Team Stan Project ELLIS Network (European Laboratory for Learning and Intelligent Systems)