جست‌وجوی استاد و پژوهشگر

Gaurav Tiwariمشاهده پروفایل

دانشیار

• Geotechnical Engineering
• Rock Engineering
• Experimental Rock Mechanics
• +۳ مورد دیگر

Dr. Gaurav Tiwari is an Associate Professor in the Department of Civil Engineering at the Indian Institute of Technology Kanpur . His research focuses on Rock Mechanics , with expertise in Geotechnical Engineering and Rock Engineering . Education : PhD in Geotechnical Engineering (Rock Mechanics) from IISc Bangalore, M.Tech in Geotechnical Engineering from IIT Roorkee, and B.E. in Civil Engineering from M.B.M. Govt. Engineering College, Jodhpur. His work emphasizes Experimental Rock Mechanics , Probabilistic Rock Engineering , and Reliability Analysis for rock structures with limited data. Key applications include rock slope stability , tunnel support design , and dynamic behavior of jointed rocks . He has developed hybrid reliability frameworks and sensitivity analysis methods for spatially varying rock properties and data-deficient scenarios , as evidenced by his recent articles in journals like International Journal of Rock Mechanics and Mining Sciences and Rock Mechanics and Rock Engineering . Dr. Tiwari’s students have contributed to high strain rate rock analysis , grouted flaw dynamics , and probabilistic modeling . His scientific awards include the IGS-Leonard’s Best PhD Thesis Award (2018-2019), ISSMGE scholarship (2017), and multiple IGS-Roorkee YGE Awards for Best Papers. Laboratory Facilities : Computational tools (FLAC-2D/3D, UDEC, Rocscience Academic Bundle) and experimental setups (high-rate direct shear testing, 3D-printed synthetic samples, rock durability tests).

Jamie Lynn Toddمشاهده پروفایل

دانشیار

• Pulmonary Medicine
• Lung Transplantation
• Idiopathic Pulmonary Fibrosis
• +۴ مورد دیگر

Jamie Lynn Todd is an Associate Professor of Medicine at Duke University School of Medicine and a member of the Duke Clinical Research Institute, specializing in lung transplantation and advanced lung disease with a focus on idiopathic pulmonary fibrosis (IPF). Her work bridges clinical practice and translational research to address critical challenges in pulmonary medicine. Education: Fellow in Pulmonary Medicine, Duke University (2008-2009) Resident, Duke University, Medicine (2005-2008) M.D., University of Colorado, School of Medicine (2005) Research Focus: Dr. Todd investigates clinical and genetic risk factors for lung fibrosis in transplant rejection and IPF, and examines how airway epithelial injury responses influence repair, fibrosis, and immunity. Her approach integrates molecular biology with clinical data to develop predictive models and biomarkers for disease progression. Publication Trends: Her 2025 publications emphasize biomarker discovery (prostasin, hyaluronan), risk stratification in lung transplantation, and environmental impacts on IPF. Utilizing large registries like IPF-PRO and ILD-PRO, her work prioritizes practical applications for early detection and intervention in fibrotic lung diseases. Grants & Collaboration: As PI on 10 active grants (2019-2029), including NIH K23 and Cystic Fibrosis Foundation awards, she leads the Lung Transplant Clinical Trial Network and Duke PROSPER training program. Her extensive grant portfolio supports translational research and mentorship in pulmonary academia. Research Ecosystem: Embedded in Duke's Department of Medicine and Clinical Research Institute, she collaborates with the Lung Transplant Outcomes Group and IPF-PRO Registry teams, driving multicenter studies that shape clinical guidelines for transplant complications and fibrotic lung diseases.

Christopher Adolphمشاهده پروفایل

استاد

• Comparative Politics
• Computational Methods
• Data Visualization
• +۸ مورد دیگر

Christopher Adolph is a Professor of Political Science at the University of Washington, Seattle, with additional appointments as Adjunct Professor of Statistics and Associate Director of the Center for Statistics and the Social Sciences. His research spans comparative politics, political economy, and political methodology, with a specialization in data visualization and statistical analysis. His substantive research interests include the politics of monetary policy, bureaucratic power, budget trade-offs, domestic impact of international trade, and comparative study of institutions governing health. He has made significant contributions to the visual display of scientific information, particularly illustrating findings from statistical models. His methodological expertise focuses on statistical inference for data with logical bounds, including political rank, compositional data, and ecological inference. Adolph's recent research has focused extensively on policy responses to the COVID-19 pandemic across US states, where he serves as faculty lead for a team collecting and analyzing data on state-level social distancing policies. His publications on pandemic politics have examined partisan patterns in social distancing mandates, mask mandates, and the easing of restrictions. International Political Science Association's Charles H. Levine Prize for best contribution in comparative public policy and administration Ranked 1st globally among political scientists for 'recent impact' (2025) Ranked 3rd globally for overall impact (2025) Former Robert Wood Johnson Scholar in Health Policy Research As an expert witness, he has consulted on statistical methods for contested elections and provides expertise on statistical methodology and data visualization. His book 'Bankers, Bureaucrats, and Central Bank Politics: The Myth of Neutrality' (Cambridge University Press, 2013) received significant recognition in the field.

• Nursing
• Mental Health
• Coping Strategies
• +۲ مورد دیگر

Nurul Huda is a Lecturer in the Nursing Faculty at Universitas Riau in Pekanbaru, Riau, Indonesia. Her research focuses on the intersection of nursing practice, mental health, and coping strategies, particularly in challenging healthcare contexts such as the COVID-19 pandemic and advanced cancer care. Dr. Huda's research interests center on mental health challenges faced by both healthcare providers and patients. She has conducted significant work on coping strategies among nurses during the COVID-19 pandemic, examining how fear of infection relates to mental health problems. Her scholarship also addresses psychological distress among patients with advanced cancer, with particular attention to cross-cultural adaptations of assessment tools in the Indonesian context. A substantial portion of her work involves quantitative analysis of mental health indicators and coping mechanisms in high-stress medical environments. Analysis of Dr. Huda's publications reveals a consistent focus on practical applications of psychological principles in nursing practice. Her work demonstrates expertise in quantitative research methods, particularly in measuring psychological constructs like coping strategies, anxiety, and depression. She frequently employs statistical techniques such as bootstrap resampling to test mediation effects. A significant portion of her research addresses frontline healthcare challenges in Indonesia, with implications for nursing education and practice in similar contexts across Southeast Asia. Dr. Huda frequently collaborates with international researchers, as evidenced by her work with colleagues from the United States, Taiwan, and other Indonesian institutions. This collaborative approach enriches her research and demonstrates her ability to work within global research networks while maintaining focus on local Indonesian healthcare contexts. Her work with Malissa Kay Shaw from the University of Health Sciences and Pharmacy and Hsiu Ju Chang from Taiwan highlights her international research partnerships.

• Nonparametric predictive inference
• Nonparametric statistics
• Modeling dependence
• +۳ مورد دیگر

Professor Tahani Coolen-Maturi is a faculty member in the Department of Mathematical Sciences at Durham University and Fellow of the Durham Research Methods Centre. Her research focuses on advanced statistical methodologies with applications across multiple domains. Her primary research interests include: Nonparametric predictive inference and statistics Reliability and survival analysis using survival signatures Diagnostic accuracy and ROC analysis Modeling dependence structures Uncertainty quantification through imprecise probability Statistical reproducibility frameworks Analysis of her recent publications reveals a strong trend toward statistical reproducibility research, particularly in hypothesis testing and educational trial methodologies. Her work bridges theoretical statistics with practical applications in reliability engineering, medical diagnostics, and educational assessment. A significant portion of her recent output develops nonparametric predictive inference frameworks for complex systems and censored data. Professor Coolen-Maturi actively supervises doctoral students, with ten current supervisees including Azza Alzahrani, Fatemah Alammar, and Hadeer Ghonem. Her collaborative research includes major educational trials funded by the Education Endowment Foundation, focusing on statistical power calculations and meta-analysis of intervention effectiveness.

Yeonwoo Rhoمشاهده پروفایل

دانشیار

• Time Series Analysis
• Econometrics
• Bootstrap/Resampling
• +۴ مورد دیگر

Dr. Yeonwoo Rho is an Associate Professor in the Department of Mathematical Sciences at Michigan Technological University. His academic credentials include a PhD in Statistics from University of Illinois at Urbana-Champaign, an MS in Statistics from Seoul National University, and a BS/BA in Mathematics/Economics from the same institution. His research focuses on advanced statistical methodologies including: Time series modeling and econometrics Bootstrap/resampling techniques for inference Unit root testing for non-stationary data Spatio-temporal analysis of complex systems Statistical methods for p-value combination Modeling of heavy-tailed distributions Office: Fisher Hall 226C | Email: yrho@mtu.edu | Phone: 906-487-2220

Ryan Renslowمشاهده پروفایل

دانشیار

• Metabolomics
• Chemical Biology
• Exposure Science
• +۴ مورد دیگر

Ryan Renslow serves as a Chemical Engineer at Pacific Northwest National Laboratory (PNNL) and holds a Research Associate Professor position at Washington State University's Gene and Linda Voiland School of Chemical Engineering and Bioengineering. His interdisciplinary work bridges computational modeling, advanced imaging, and experimental biology to address complex challenges in metabolomics and microbial systems. Education BS in Chemical Engineering, Washington State University MS in Chemical Engineering, Washington State University PhD in Chemical Engineering, Washington State University Linus Pauling Distinguished Postdoctoral Fellowship, Pacific Northwest National Laboratory Renslow's research centers on identifying novel metabolites in complex biological samples, deciphering microbial community structure-function relationships, and understanding emergent properties in multispecies systems. He employs computational mathematics, machine learning, and high-resolution imaging techniques including mass spectrometry and nuclear magnetic resonance. His work spans diverse applications from human health diagnostics and bioenergy production to ecological monitoring and national defense solutions, with particular emphasis on biofilm dynamics and metabolite characterization in challenging environments. Analysis of his recent publications reveals a strong trend toward integrating ion mobility spectrometry with computational modeling for metabolite identification, developing in silico libraries for small molecule annotation, and applying machine vision to biological systems. His work consistently demonstrates cross-cutting applications across energy, environmental science, and biomedical research through sophisticated data analysis frameworks. Scientific Awards: No awards listed in available documentation. Renslow's collaborative research program involves extensive partnerships across PNNL's Environmental Molecular Sciences Laboratory and Washington State University. His work receives institutional support through DOE-funded initiatives focused on chemical biology and exposure science, with emphasis on developing advanced analytical capabilities for complex sample analysis. While specific grant details aren't provided, his publication record indicates consistent funding for interdisciplinary projects combining experimental and computational approaches. At PNNL, Renslow contributes to the Chemical Biology and Exposure Science group within the Biological Sciences division. He leverages specialized facilities including high-field mass spectrometers, nuclear magnetic resonance microimaging systems, and biofilm reactors to investigate microbial community dynamics. His research team integrates expertise from chemical engineering, microbiology, and computational science to develop novel approaches for characterizing complex biological systems at multiple scales.

• Nonparametric Statistics
• Bootstrap Methods
• Smoothing Methods
• +۵ مورد دیگر

Alan M. Polansky is an Associate Professor in the Department of Statistics and Actuarial Science at Northern Illinois University (NIU), where he has maintained a continuous faculty appointment since 1995. His research program centers on advancing nonparametric statistical methodologies with significant contributions to bootstrap theory, smoothing techniques, and observed confidence levels. His academic credentials include a Ph.D. from Southern Methodist University and M.S./B.S. degrees from the University of Texas at San Antonio. As an Honors Faculty Fellow (2022-2023), he developed and taught an innovative seminar on Data and Social Justice for the University Honors Program. Polansky's research spans several interconnected domains: Foundational work on bootstrap methodology and confidence interval construction Development of observed confidence levels as an alternative to multiple comparison techniques Asymptotic theory for statistical limit theorems Emerging research on network data analysis and Bayesian inference for stochastic processes His 25+ year publication record shows consistent evolution from core nonparametric methods toward contemporary applications, with recent work focusing on network statistics and Bayesian approaches while maintaining theoretical rigor. Publications appear in premier journals including the Journal of the Royal Statistical Society, Technometrics, and Computational Statistics and Data Analysis. Key recognitions include: Honors Faculty Fellowship (2022-2023) Authorship of two influential monographs: 'Observed Confidence Levels' (2007) and 'Introduction to Statistical Limit Theory' (2011) As an educator, Professor Polansky maintains regular office hours in DuSable Hall and has advised students across statistics and actuarial science programs. His research demonstrates sustained methodological innovation with practical applications in insurance, manufacturing, and emerging network-based domains, reflecting both deep theoretical expertise and commitment to real-world problem solving.

Johannes Krebsمشاهده پروفایل

استاد

• Topological Data Analysis
• Persistent Homology
• Spatial Statistics
• +۳ مورد دیگر

Prof. Dr. Johannes Krebs holds the Chair of Mathematics - Statistics at the Faculty of Mathematics and Geography, Catholic University of Eichstätt-Ingolstadt since 2021. His academic service includes membership on the editorial board of Statistics , the tenure track board, examination committees for B.Sc. Mathematics and Data Science programs, and the faculty council. His research focuses on topological data analysis, persistent homology, and spatial statistics with applications in stochastic geometry. Funded by Deutsche Forschungsgemeinschaft (KR 4977/2-1 and KR 4977/1-1), his work bridges theoretical statistics with computational topology to develop novel methods for analyzing complex data structures. His methodological contributions span wavelet analysis, functional data, and bootstrap techniques for dependent data. Analysis of his recent publications reveals strong emphasis on topological inference (persistent homology, Betti numbers), spatial statistics, and wavelet methods across multiple high-impact journals including Bernoulli , Annals of Statistics , and Stochastic Processes and their Applications . His work demonstrates consistent innovation in applying topological concepts to statistical problems with theoretical guarantees. Academic service highlights include: Editorial Board Member, Statistics journal Member of Tenure Track Board Examination Committee for B.Sc. Mathematics & Data Science Faculty Council Member His research program is supported by significant DFG funding for projects on Topological Data Analysis (2021-2023) and Dynamic Objects on Random Fields (2017-2020), reflecting sustained contribution to advancing statistical methodology through topological frameworks. He maintains active collaborations with researchers at UC Davis, KU Leuven, and Heidelberg University.