جستجوی استاد راهنمای دانشگاهی

Sarat Chandra Dassمشاهده پروفایل

استاد

• Statistics
• Bayesian Statistics
• Data Science
• +۵ مورد دیگر

Professor Sarat Chandra Dass is affiliated with Heriot-Watt University in Edinburgh, where he works in the School of Mathematical and Computer Sciences . His research spans Statistics , Bayesian Statistics , and Data Science , with applications in Ionospheric Modeling , Solar Flares , and Seismic Analysis . Research Focus: Statistical modeling, machine learning, and data assimilation techniques applied to space weather and geophysical phenomena. Teaching: Leads courses in Bayesian inference and computational methods. Recent publications highlight his work on TEC prediction using advanced machine learning models like Bi-LSTM , LSTM , and XGBoost , with validation against frameworks such as IRI-Plas 2020 and IRI-2017 . These studies focus on ionospheric responses during solar flares and geomagnetic storms , leveraging GPS data and universal kriging for spatio-temporal analysis. Collaborations: Active in international research networks, particularly in Indonesia and India, addressing challenges in ionospheric disturbances and space weather forecasting .

Abel Rodriguezمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• Nonparametric Methods
• +۴ مورد دیگر

Abel Rodriguez serves as Professor of Statistics and Chair of the Department of Statistics at the University of Washington, with affiliate memberships at the eScience Institute and Center for Statistics in the Social Sciences. His research encompasses: Bayesian statistical methodology development Nonparametric and machine learning techniques Spatio-temporal modeling for complex data Network analysis applications in social sciences Extreme value theory for engineering problems Methodological innovations target biological, social science, and engineering challenges through interdisciplinary collaboration at affiliated research centers. Scientific Awards: No awards or honors were documented in the provided materials. Advising and Grants: Student mentorship activities and grant funding details were not specified in the source text.

• Stochastic modeling
• Spatial-temporal modeling
• Bayesian hierarchical modeling
• +۹ مورد دیگر

Catherine Calder (also known as Kate Calder) is a Professor of Statistics at The Ohio State University and served as Co-Director of the Mathematical Biosciences Institute (MBI) from 2018-2019. She is a faculty affiliate of the Institute for Population Research and the Translational Data Analytics Institute at Ohio State. PhD in Statistics, Duke University (2003) Her research focuses on developing stochastic models for complex spatial-temporal dependencies using Bayesian hierarchical modeling paradigms, particularly for high-dimensional and computationally intensive problems in environmental, social, and health sciences. Key methodological contributions include: Dimension reduction techniques for spatio-temporal data Covariate-driven nonstationary spatial models Data-augmentation algorithms for spatial generalized linear models Latent space models for two-mode networks Model-based network comparison frameworks She currently collaborates with social scientists on the Adolescent Health and Development in Context Study at Ohio State. Professional leadership roles include associate editorships for the Annals of Applied Statistics and Bayesian Analysis , and active participation in the American Statistical Association (ASA) and International Society for Bayesian Analysis. NIH-funded research NSF-funded research NASA-funded research

Erik Otárola-Castilloمشاهده پروفایل

دانشیار

• Archaeology
• Anthropology
• Human Evolutionary Biology
• +۷ مورد دیگر

Erik Otárola-Castillo is an Associate Professor in the Department of Anthropology at Purdue University, where he joined the faculty in 2015 and was promoted to Associate Professor. His interdisciplinary work bridges archaeology, human evolutionary biology, and biostatistics, focusing on prehistoric and modern hunter-gatherer populations and their responses to environmental change. His educational background includes: Ph.D. in Anthropological Science, Stony Brook University M.A. in Anthropology, Iowa State University B.A. in Anthropology, Stony Brook University Dr. Otárola-Castillo specializes in the evolution, ecology, and diversity of behavior in hunter-gatherer societies, with particular emphasis on climate change impacts and food availability effects on early North American native diets. As a computational anthropologist, he develops quantitative tools including 3D morphometrics software, statistical models for zooarchaeologists, and spatio-temporal analysis frameworks. His research integrates Geographic Information Systems (GIS) and Bayesian statistics to address core archaeological questions of space, time, and form, with applications ranging from bone surface modification analysis to human-megafauna interactions. Recent publications reveal a strong methodological trend toward Bayesian inference for archaeological hypothesis testing and computational morphometric analysis of artifacts and skeletal remains. Key thematic areas include lithic technology standardization, subsistence intensification mechanisms among Great Plains hunters, and health impacts of urbanization on indigenous Peruvian populations, demonstrating consistent application of quantitative rigor across diverse anthropological contexts. He is recognized as an international authority, evidenced by an invitation to contribute a review on Bayesian approaches to the Annual Review of Anthropology (2018). His work appears in high-impact journals including The Proceedings of the National Academy of Sciences (PNAS) and PLoS One. Dr. Otárola-Castillo directs the Laboratory for Computational Anthropology and Anthroinformatics (LCA), which develops open-source tools like geomorph and zooaRch for anthropological research. The lab's work enables advanced morphometric analysis and zooarchaeological quantification, supporting collaborative projects worldwide through computational innovation in data collection, analysis, and visualization.

• Biostatistics
• Bioinformatics
• High-dimensional data analysis
• +۷ مورد دیگر

Tao Wang is an Assistant Professor Adjunct in the Department of Biostatistics at Yale School of Public Health and a tenure-track Associate Professor and Principal Investigator in the SJTU-Yale Joint Center for Biostatistics and Data Science at Shanghai Jiao Tong University. His work focuses on statistical methods for high-dimensional biomedical data, especially dimension reduction, microbiome analysis, and integrative genomic modeling. Education: PhD in Statistics, Hong Kong Baptist University, 2013 MS, East China Normal University, 2010 Research Interests: Dr. Wang develops statistical and computational techniques to tackle complex, high-dimensional data arising from modern biotechnologies. His methodological portfolio includes adaptive independence tests for microbiome community data, penalized prediction models for sequencing counts, and counting-process-based dimension reduction for censored survival outcomes. He also works on Bayesian tree-structured models linking dietary nutrients to gut microorganisms and joint estimation of Gaussian graphical models over spatial and temporal domains. These interests sit at the intersection of biostatistics, bioinformatics, machine learning, and epidemiology , with applications spanning the human microbiome, epigenomics, and large-scale cohort studies. Publications at a Glance: Across six major papers (2015-2019) in Biometrics and Biometrika , Wang has consistently advanced methodology for high-dimensional count, compositional, and censored data. Themes include adaptive testing, predictive modeling, dimension reduction, and network estimation, often motivated by collaborative projects in genomics and microbiome science. Honours: Elected Member, International Statistical Institute (2020) Collaboration & Advising: He maintains an active collaboration with Yale’s Dr. Hongyu Zhao, co-authoring all showcased publications. No formal advisees are listed in the provided material. Laboratories & Teams: He leads research activities within the SJTU-Yale Joint Center for Biostatistics and Data Science, integrating resources from both Shanghai Jiao Tong University and Yale to advance data-intensive biomedical science.

• Physical Education
• Sports Science
• Exercise Physiology
• +۴ مورد دیگر

Henryk Król is a multidisciplinary researcher affiliated with the Jerzy Kukuczka Academy of Physical Education in Katowice, with expertise spanning physical education, sports science, public health, and biomedical engineering. His collaborative research bridges exercise physiology, cancer survivorship, and optical engineering domains. Research Interests: Sports Science & Exercise Physiology: Investigating biomechanics of acrobatic movements and jump training effectiveness Cancer Survivorship: Examining obesity, physical fitness, and lifestyle factors in breast cancer survivors Public Health Epidemiology: Studying diabetes, cardiovascular risk factors, and socioeconomic health disparities Optical Engineering: Developing thermal imaging techniques for optical coating characterization His recent work demonstrates a unique integration of physical education principles with biomedical applications, particularly in cancer survivorship studies combining accelerometer-based activity monitoring with self-reported health metrics. The optical engineering contributions showcase expertise in thermal radiation effects on specialized coatings, with applications in laser systems and photonics. Collaborative Research: Dr. Król's extensive co-authorship networks span Polish, Swedish, and French institutions, reflecting international collaboration in both health sciences and engineering disciplines. His research has been continuously funded as evidenced by publications from 2017-2025.

• Statistics
• Big data analytics
• Spatial statistics
• +۳ مورد دیگر

Laura Šaltytė-Vaisiauskė is an Associate Professor and currently serves as Vice-Rector for Research and Innovation at Klaipėda University, Lithuania. She is affiliated with the Department of Informatics and Statistics within the Faculty of Marine Technology and Natural Sciences, with additional connections to the Faculty of Social Sciences and Humanities and the Institute of Baltic Region History and Archaeology. Dr. Šaltytė-Vaisiauskė defended her doctoral thesis in Mathematics at Vilnius University in 2013 and has since established herself as a versatile researcher with publications spanning multiple disciplines while maintaining core expertise in statistical methodology. Her research interests center around Statistics, Big data analytics, and Spatial statistics, with significant contributions to Image recognition and Discriminant analysis. Analysis of her publication record reveals a strong focus on developing advanced statistical methods for spatio-temporal data analysis, particularly using Hidden Markov Models with spatial weighting. Her work demonstrates increasing interdisciplinary reach, extending from pure statistical methodology to applications in finance, healthcare, energy systems, and social sciences. Recent publications show her applying statistical techniques to diverse problems including financial volatility modeling, electric vehicle optimization, and even therapeutic interventions for depression. Her scholarly output demonstrates consistent productivity across multiple domains, with publications in reputable journals and conference proceedings. Her work shows particular strength in developing methodological innovations with practical applications, often collaborating across disciplinary boundaries to address complex real-world problems. As Vice-Rector for Research and Innovation, Dr. Šaltytė-Vaisiauskė oversees research strategy and development at Klaipėda University. Her leadership role complements her active research career, allowing her to influence both the direction of her own work and the broader research landscape at her institution. Her recent projects include studies on the impact of COVID-19 restrictions on businesses in the Klaipėda region, demonstrating her commitment to addressing timely societal challenges through rigorous research.

Nick Clarkمشاهده پروفایل

دانشیار

• Spatio-Temporal Modelling
• Bayesian Statistics
• Data Literacy Education
• +۱ مورد دیگر

Nick Clark serves as Associate Professor in the Department of Mathematics and Statistics within the College of Arts and Sciences at the University of St. Thomas. His academic credentials include: BS in Mathematics from United States Military Academy MS in Statistics from George Mason University PhD in Statistics from Iowa State University Professor Clark's research spans four interconnected domains: Spatio-Temporal Modelling for geographic and time-series data, Bayesian Statistics for probabilistic inference, Data Literacy Education for statistical communication, and Sports Statistics for athletic performance analysis. His methodological work emphasizes practical implementation of advanced statistical techniques. Teaching remains central to his role with Fall 2025 courses spanning undergraduate levels: Finite Mathematics (MATH 101-05), Mathematical Statistics (STAT 314-01) covering frequentist/Bayesian hypothesis testing, and Predictive Modeling (STAT 333-D01) featuring regression, GLM, and ARIMA time-series analysis with writing-intensive projects. While specific awards aren't documented in available materials, his curriculum development demonstrates commitment to statistical education through writing-intensive requirements and applied methodology training. Prospective students should contact him directly regarding research alignment with his expertise in Bayesian modeling, sports analytics, or spatio-temporal data applications.

• Plant Signaling
• Computational Biology
• Mathematical Modeling
• +۳ مورد دیگر

Professor Richard J Morris leads the Morris Lab at the John Innes Centre, where he develops mathematical and computational models to decipher how plants process environmental information for adaptive responses. His work bridges plant biology and quantitative sciences through close collaboration with experimental groups. Research focuses on plant information processing mechanisms, including temperature/day-length signaling for developmental transitions, pathogen-induced cell-to-cell communication, and nutrient-sensing networks. The lab employs physics-based modeling approaches to uncover mechanistic insights into complex plant behaviors, prioritizing techniques most appropriate for each biological question. Professor Morris supervises four PhD students and multiple postdoctoral researchers supported by active BBSRC and European Research Council grants. His team maintains strong collaborative ties with experimentalists both within the John Innes Centre and internationally. The Morris Lab comprises an interdisciplinary team including postdocs (Dr Melissa Tomkins, Dr Hugh Woolfenden), PhD researchers, and research staff. The group fosters a collaborative environment focused on translating computational insights into biological understanding through regular experimental validation.

Taylor Okonekمشاهده پروفایل

استادیار

• Statistics
• Survival Analysis
• Demography
• +۳ مورد دیگر

Taylor Okonek is an Assistant Professor of Statistics in the Department of Mathematics, Statistics, and Computer Science at Macalester College. She holds additional affiliations as an External Affiliate of the Center for Studies in Demography and Ecology and an External Member of the Minnesota Population Center. Dr. Okonek earned her BA in Mathematics and Religion from St. Olaf College in 2018 and completed her PhD in Biostatistics from the University of Washington in 2023 under the supervision of Jon Wakefield. During her doctoral studies, she worked as a research assistant focusing on spatio-temporal methods for under-5 mortality estimation and HIV prevalence estimation, primarily in sub-Saharan Africa. Her research focuses on developing statistical methodologies for demographic applications, with particular emphasis on small area estimation, survival analysis, and statistical methods for official statistics and complex survey data settings. Much of her work is motivated by applications for the United Nations in producing under-5 mortality estimates. She is especially interested in creating computationally efficient statistical methods, with current research extending existing approaches for estimating mortality across time using continuous, parametric survival models applied to complex survey data. Dr. Okonek's publications demonstrate a consistent focus on improving computational efficiency in Bayesian frameworks for demographic estimation, particularly in benchmarking subnational estimates to national level estimates. Her methodological contributions address critical needs in official statistics where agreement between different levels of estimation is often required. As a former TRIO McNair scholar and first-generation college student from a low-income background, Dr. Okonek is deeply committed to creating inclusive learning environments. She teaches multiple statistics courses at Macalester College including Statistical Theory (MATH/STAT 355), Probability (MATH/STAT 354), and Introduction to Statistical Modeling (STAT 155), where she implements teaching practices informed by her own educational journey.

• Bayesian analysis
• Data mining
• Machine learning
• +۳ مورد دیگر

Dr. Tri M. Le serves as Associate Professor of Mathematics and Computer Science and Program Coordinator for the M.S. in Data Science at Mercer University's College of Professional Advancement, Department of Informatics and Mathematics. He joined Mercer in 2017 after working as a Predictive/Computational Statistician at the University of Nebraska-Lincoln, bringing expertise in statistical software (SAS, SPSS, R) and extensive teaching experience at undergraduate and graduate levels. His educational background includes: PhD and MA in Statistics, University of Missouri-Columbia (2014) MS in Probability and Statistics, Ho Chi Minh City University of Natural Sciences (2002) BS in Mathematics and Informatics, Ho Chi Minh City University of Natural Sciences (1999) Dr. Le's research spans Bayesian analysis, decision theory, spatio-temporal modeling, and machine learning. His work addresses fundamental questions in model uncertainty, prediction reliability, and the interpretability-performance trade-off in statistical learning. He has published in leading journals including Journal of Machine Learning Research and Bayesian Analysis, with recent focus on model averaging superiority over selection and theoretical foundations of ensemble methods. Analysis of his 2016-2022 publications reveals consistent focus on Bayesian predictive modeling, with increasing emphasis on interpretable machine learning. His work bridges theoretical statistics and practical applications in healthcare analytics and environmental systems, demonstrating interdisciplinary relevance through collaborations in geoscience and criminal justice research. Dr. Le actively contributes to the academic community as reviewer for Bayesian Analysis and International Conference on Fuzzy Systems and Data Mining, and as Session Chair for the Joint Statistical Meetings. His leadership extends to Mercer's Tenure and Promotion committee and the M.S. in Data Science program coordination. While no dedicated research lab is mentioned, Dr. Le's role as Program Coordinator provides structured opportunities for students through the M.S. in Data Science curriculum. His courses in Data Analytics and Healthcare Data Analytics offer practical training grounded in his research on predictive modeling and statistical inference.