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Joon Jin SongView profile

Professor

• Statistical Science
• Bayesian Analysis
• Spatial Statistics
• +4 more

Dr. Joon Jin Song is a Professor of Statistical Science at Baylor University. His work focuses on Bayesian methods, spatial statistics, and their applications in environmental science, biostatistics, and public health. He holds a Ph.D. from Texas A&M University (2004), an M.S. from Kyungpook National University (1999), and a B.S. from Yeungnam University (1997). His research integrates advanced statistical techniques to address challenges in spatial data analysis, including rainfall estimation, disease mapping, and environmental monitoring. Notable projects include Bayesian correction for misclassification in count data and radar-rain gauge merging for high-resolution precipitation estimation. He has also contributed to studies on crime fear reduction through spatio-temporal modeling and public health surveillance during the COVID-19 pandemic. Dr. Song's publications span computational statistics, environmental modeling, and health informatics. His methodologies emphasize robustness to measurement error, high-dimensional data, and spatial dependencies. Collaborations include interdisciplinary work in meteorology, epidemiology, and bioinformatics.

Julia E. FaView profile

Professor

• Conservation Science
• Wildlife Hunting
• Wild Meat Use
• +3 more

Julia E. Fa is Professor of Biodiversity and Human Development at Manchester Metropolitan University and Senior Research Associate at the Center for International Forestry Research (CIFOR). Her research examines relationships between wildlife conservation and human development, focusing on wildlife hunting impacts, zoonotic diseases, and community-based conservation across Africa, Latin America, and Europe. She leads the Bushmeat Research Initiative at CIFOR, developing evidence-based solutions for sustainable wildlife use. Current projects include WARBIO (armed conflicts and biodiversity in Angola), Sustainable Wildlife Management in Cameroon, and Amazon conservation initiatives. Her work integrates ecological science with policy development and community engagement. Recent publications (2023-2025) address leprosy transmission through wild meat, COVID-19 impacts on food security, ethical insect trade, and Indigenous knowledge integration. Research consistently emphasizes balancing biodiversity protection with human wellbeing in tropical regions.

Mini DasView profile

Professor

• Medical Imaging
• Biomedical Engineering
• Optical Physics
• +7 more

Mini Das is the Moores Professor in the Department of Physics at the University of Houston, with affiliations in the Cullen College of Engineering's Biomedical Engineering and Electrical and Computer Engineering departments. She holds a Ph.D. from the Indian Institute of Technology, Delhi. Her research focuses on advanced imaging techniques at the intersection of optical physics, computational methods, and engineering, with applications in medical diagnostics, defense/security, and global health. Key areas include X-ray phase contrast imaging, photon-counting detectors (collaborating with CERN), virtual clinical trials, and psychophysical models for human perception in medical imaging. Her work aims to improve cancer screening accessibility in underserved regions through innovative imaging systems. Das leads interdisciplinary projects combining quantum detection, material decomposition algorithms, and virtual patient modeling. She is a leader in developing cutting-edge imaging tools and has pioneered methods for low-dose and high-resolution biomedical imaging. Education : Ph.D., Indian Institute of Technology, Delhi Research Interests : Experimental/Optical design and computational imaging, virtual clinical trials, psychophysics/image science, vision science-driven imaging systems, and global health applications in cancer screening. Her lab develops advanced imaging modalities like multi-contrast X-ray phase mammography and spectral CT material decomposition systems. Grants & Collaborations : Works with CERN to optimize quantum detectors, FDA on regulatory-compliant imaging systems, and non-profits to deploy screening tools in low-resource areas. Her lab also collaborates on eye-tracking studies for human observer performance modeling in radiology. Labs/Teams : Directs a lab focused on quantum detection systems, computational imaging platforms, and bio-inspired optical designs. Active in multi-university consortia for biomedical imaging innovation.

Hadi Safari KatesariView profile

Assistant Professor

• Bayesian Statistics
• Multivariate Time Series Analysis
• High-Dimensional Data Analysis
• +7 more

Hadi Safari Katesari is a Teaching Assistant Professor in the Department of Mathematical Sciences at Stevens Institute of Technology within the Charles V. Schaefer, Jr. School of Engineering and Science. He joined Stevens in August 2021 after completing his PhD in Statistics at Southern Illinois University and teaches core statistics courses for Data Science, Mathematics, and Business graduate programs. His educational background includes: PhD in Mathematics with concentration in Statistics, Southern Illinois University (2021) Dr. Safari Katesari develops statistical tools for analyzing complex dynamic and dependent data, with emphasis on dimension reduction for multivariate time series. His research spans Bayesian statistics, copula models for dependency structures, factor analysis, and high-dimensional techniques. Key research domains: Bayesian inference and multivariate time series modeling Factor analysis and cluster analysis for latent structures Copula-based approaches in actuarial science and business analytics His publication trends (2015-2022) reveal consistent innovation in copula methodology applied across actuarial science, insurance, and agricultural analytics, with recent work focusing on count data modeling, survival analysis, and IBNR loss reserving through advanced dependency frameworks. His honors include: Doctoral Fellowship Award (2019-2021) from Southern Illinois University Dr. Safari Katesari actively contributes as a reviewer for the Journal of Mahani Mathematical Research, Risk and Decision Analysis Journal, and Mathematical Reviews. His institutional service encompasses assessment of internship projects, graduate open house coordination, and domestic student recruitment events for data science master's programs. No specific research laboratories or dedicated teams were mentioned in the source materials.

Guohua YanView profile

Professor

• Cluster Analysis
• Linear Clustering
• Statistical Modeling
• +2 more

Guohua Yan is Professor in Mathematics and Statistics at the University of New Brunswick, specializing in statistical methodologies for pattern recognition and complex data structures. His research develops innovative approaches to cluster analysis with applications across biological and medical sciences. Primary contributions include advancing linear clustering techniques that identify linear relationships within datasets, such as allometric studies in biology and genotyping analysis. Complementary research creates statistical models for clustered count data with excessive zeros, incorporating random effects to account for subject-level heterogeneity in medical studies. Current methodologies address challenges including missing data, measurement errors, and spatial dependencies in large-scale epidemiological research.

Xia WangView profile

Professor

• Bayesian Methodology
• Categorical Data Analysis
• Scalable Modeling
• +6 more

Xia Wang is a Professor in the Department of Mathematical Sciences at the University of Cincinnati, specializing in Bayesian methodology, categorical data analysis, and scalable modeling of high-dimensional data. She holds dual Ph.D.s in Statistics (2009) and Economics (2007) from the University of Connecticut, followed by postdoctoral training at the National Institute of Statistical Sciences (NISS) from 2009 to 2011. Education: Ph.D. in Statistics, University of Connecticut, 2009 Ph.D. in Economics, University of Connecticut, 2007 Research Interests: Bayesian methodology and computation Categorical data analysis Scalable modeling for complex data Applications in genomics, proteomics, climate science, and biomedical studies Grants & Collaborations: NIH Grant (R01MH119814): Collaborator in studying stress resilience via neurocircuit mechanisms (2019–2024) Students & Advising: Supervised 5 doctoral students (e.g., Leo Duan, Dan Li, Weizhe Su) and multiple undergraduates Research focuses on Bayesian nonparametric methods, survival analysis, and machine learning applications Labs & Teams: Active collaborations with the CCHMC (Cincinnati Children’s Hospital Medical Center) and NISS, with interdisciplinary projects in proteomics, climate modeling, and healthcare analytics.

Farah ChancharyView profile

Lecturer

• Computer Science
• Cloud Computing
• Data Structures
• +7 more

Farah Chanchary is a Lecturer and Lab/Course Coordinator at the School of Computer Science, Carleton University. She specializes in areas such as data structures, cloud computing, and educational technologies. Her work spans algorithm design, privacy in advertising, and challenges in e-learning, particularly in Saudi Arabia. She coordinates laboratory and course activities, supporting academic infrastructure. Research Interests: - Data Structures and Algorithms - Cloud Computing and Migration - Privacy-Preserving Models in Advertising - E-Learning Readiness and Challenges - Machine Learning Applications (Neural Networks, Genetic Algorithms) - Geometric Query Processing Publications Trends: Her recent work focuses on efficient data structures for window queries, privacy concerns in online advertising, and cloud computing challenges. Earlier research includes studies on e-learning adoption in Saudi Arabia and neural network applications in rainfall forecasting. No awards or grants are listed. She has no documented advisees. She manages laboratory facilities and course coordination in the School of Computer Science.

Dennis DoblerView profile

Assistant Professor

• Resampling methods
• Survival and event time analysis
• Multivariate analysis
• +1 more

Dennis Dobler is an Assistant Professor in Statistics at TU Dortmund University, appointed in September 2023. His academic foundation includes a doctorate from Ulm University (2016) and dual bachelor's degrees in Mathematics and Computer Science from Heinrich-Heine-Universität Düsseldorf (2011). Research expertise centers on Resampling methods, Survival analysis, Multivariate techniques, and Asymptotic statistics, with applications spanning clinical trials, risk modeling, and nonparametric inference. His methodological work frequently addresses censored data challenges in survival contexts. Prior roles include Assistant Professorship at Vrije Universiteit Amsterdam, postdoctoral research at Ulm University, and a research stay at the University of Copenhagen. Current publications reflect sustained focus on survival analysis innovations, particularly in competing risks and resampling methodologies.

Oludare AriyoView profile

Lecturer

• Biostatistics
• Data Science
• High-Dimensional Data Analysis
• +4 more

Dr. Oludare Ariyo is a Lecturer in the Department of Mathematical Sciences at the University of Essex, part of the School of Mathematics, Statistics and Actuarial Science (SMSAS). He holds a PhD in Biostatistics from KU Leuven (2020) and has extensive experience in academia, including a previous position as Lecturer in Statistics at the Federal University of Agriculture, Abeokuta (2013–2022), and a Research Associate role at KU Leuven’s Department of Public Health and Primary Care (2016–2020). Education: PhD (Biostatistics), KU Leuven, Belgium (2020) M.Sc. (Statistics), Federal University of Agriculture (2010) B.Sc. (Mathematical Sciences), Federal University of Agriculture (2005) Research Interests: Dr. Ariyo focuses on statistical methodology in health, environment, agriculture, and social sciences. His work emphasizes Bayesian model selection for longitudinal and mixed-effects models, high-dimensional data analysis, survival analysis, and applications in biostatistics and data science. He currently leads a Royal Society-funded project developing structured covariance Bayesian models and collaborates with institutions like the International Institute of Tropical Agriculture. Grants & Supervision: Recipient of Royal Society funding for Bayesian covariance modeling research Open to PhD supervision in Bayesian statistics, biostatistics, mixed models, and machine learning Labs & Collaborations: Engaged in interdisciplinary projects at the intersection of statistics, agriculture, and public health, with past affiliations at the National Horticultural Research Institute (NIHORT), Ibadan, Nigeria.

Dinesh MehtaView profile

Professor

• Algorithms
• Data Structures
• VLSI Design Automation
• +4 more

Dinesh Mehta is a Professor in the Department of Computer Science at Colorado School of Mines, where he has been since 2000. His research focuses on algorithms and data structures applied to interdisciplinary fields such as VLSI design automation, cheminformatics, computational materials, and cyber-physical systems. He holds a B.Tech. from the Indian Institute of Technology Bombay (1987), an M.S. from the University of Minnesota (1990), and a Ph.D. from the University of Florida (1992). His research emphasizes algorithmic solutions for big graph analytics, including parallel algorithms, subgraph pattern matching, and network science applications. He has contributed to advancements in data structures for cheminformatics and VLSI layout optimization. His work bridges computer science with domains like materials science and geophysics, demonstrating strong interdisciplinary impact. Key research themes include optimizing algorithms for large-scale networks, predictive modeling in fracture mechanics, and efficient graphlet counting. His recent papers reflect a focus on parallel computing, machine learning applications in materials science, and algorithmic competitions in education. No significant scientific awards are listed in the provided texts. His advising and grants are not detailed, but his extensive publication record indicates active research and collaboration. He has no listed labs or teams, though his work likely involves interdisciplinary teams across Mines and industry partners.

Viktor DreminView profile

Research Fellow

• Biophotonics
• Biomedical Optics
• Biomedical Engineering
• +9 more

Dr. Viktor Dremin is a Research Fellow at the Aston Institute of Photonic Technologies (AiPT), part of the College of Engineering and Physical Sciences at Aston University. He holds a PhD and is a Fellow of the Higher Education Academy (FHEA). His research focuses on biophotonics, biomedical optics, and optical imaging technologies. Key areas include laser Doppler flowmetry, fluorescence spectroscopy, and hyperspectral imaging for medical diagnostics. Dr. Dremin leads projects on cerebral blood flow monitoring, singlet oxygen generation for theranostics, and wearable medical sensors. Education: PhD (details unspecified), FHEA qualification Research Interests: Biomedical engineering applications, non-invasive diagnostics, optical sensors, machine learning integration His work spans collaborations in neurovascular dynamics, liver cancer detection, and 3D skin model analysis. He has authored 144+ research outputs, including peer-reviewed articles and datasets. Notable contributions include a hyperspectral imaging system for skin diagnostics and wearable devices for cardiovascular monitoring. Dr. Dremin's lab develops optical tools for minimally invasive surgery and metabolic assessments. Recent studies emphasize transcranial cerebral blood flow analysis, singlet oxygen's role in cellular bioenergetics, and AI-driven maxillary sinus diagnostics. His datasets explore skin polarization and intestinal perfusion under ischemia. Collaborations include institutions in Russia and the UK, focusing on translational medical optics.

Nodari SitchinavaView profile

Professor

• Parallel Algorithms
• Cache-efficient Computing
• I/O-efficient Algorithms
• +4 more

Nodari Sitchinava is a Professor in the Department of Information and Computer Sciences at the University of Hawaii at Manoa, where he leads the Algorithms and Parallel Computing Group (AlgoPARC). His research focuses on designing efficient algorithms for modern parallel systems, particularly cache-efficient and I/O-optimized solutions for multicore and GPU architectures. His educational background includes advanced training in computer science with specialization in parallel algorithms. Research interests span: Parallel algorithm design for multicores and GPUs Cache-oblivious and I/O-efficient algorithms Communication-efficient distributed computing models Computational geometry and graph algorithms Lower bounds in computational complexity Publication analysis reveals consistent focus on parallel computing foundations, with recent work emphasizing GPU algorithm efficiency, external memory models, and geometric computations. Theoretical contributions frequently include novel lower bound proofs and practical implementations optimized for modern hardware constraints. Awards and honors include: Best Paper Award at the European Symposium on Algorithms (2019) for contributions to comparison-based algorithm complexity Teaching encompasses graduate and undergraduate courses in algorithms, parallel computing, and data structures. His instructional approach emphasizes fundamental principles of algorithm design and analysis across sequential and parallel computational models.

Qi ZhengView profile

Associate Professor

• High-dimensional data analysis
• Semiparametric and nonparametric models
• Varying effects survival analysis
• +4 more

Qi Zheng, PhD, is an Associate Professor in the Department of Bioinformatics and Biostatistics at the University of Louisville's School of Public Health and Information Sciences (SPHIS) . Joined in Fall 2015, Dr. Zheng's methodological research focuses on high-dimensional data analysis, semiparametric models, survival analysis, personalized treatments, tree-based learning, and statistical applications in epidemiology and biomedical sciences. Postdoctoral Fellow (2015), Bioinformatics and Biostatistics, Emory University PhD (2013), Mathematical Science, Clemson University BS (2006), Mathematics, Nanjing University, China Research interests span statistical methodology development for complex biomedical data. Key areas include high-dimensional regression, quantile regression, survival analysis, and machine learning applications in public health. Recent publications (2022–2013) demonstrate expertise in statistical hypothesis testing, survival modeling, and high-dimensional data methods. Articles address observational studies, censored quantile regression, time series change-point detection, and generalized linear models with complex covariates. Contact: qi.zheng@louisville.edu | Personal Website

Douglas RobinsonView profile

Professor

• Bird ecology
• Conservation biology
• Biodiversity benchmarking
• +4 more

Douglas Robinson is a Professor in the Department of Fisheries, Wildlife, and Conservation Sciences within Oregon State University's College of Agricultural Sciences. He directs the Oregon 2020 Birds project and leads the Field and Lab Ornithology Collaboratory (FLOCK). His research program investigates bird ecology and conservation strategies, biodiversity benchmarking methodologies, environmental change impacts on avian communities, citizen science applications in ecology, and tropical/aridlands ecosystem dynamics. He serves as undergraduate student advisor and maintains research focus on Pacific Northwest bird communities. Robinson's extensive publication record demonstrates consistent investigation into avian distribution modeling techniques, nesting ecology, bioacoustic monitoring, and community science data validation. Recent work emphasizes spatial analysis improvements for species distribution models and long-term avian population trends. No scientific awards, student advisees, or grant information are documented in the provided profile.

• Biomedical Engineering
• Microvascular Hemodynamics
• Computational Fluid Dynamics
• +3 more

Peter Balogh is an Assistant Professor in the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology (NJIT). His research focuses on computational modeling of blood flow in microvascular systems, particularly investigating hemodynamic characteristics, red blood cell dynamics, and their implications in angiogenesis and cancer cell transport. Balogh leads the Integrated Multiphase Blood Flow Modeling and Experiments project funded by the National Science Foundation (NSF), aiming to predict microvascular growth through advanced simulations. Education: Not explicitly stated in provided texts. His research interests include microvascular hemodynamics, computational fluid dynamics (CFD), and the biomechanical interactions between blood cells and vessel walls. Key topics involve tortuous microvessel simulations, wall shear stress patterns in angiogenic networks, and the role of red blood cell stiffness in blood flow dynamics. Balogh's recent work highlights advancements in high-fidelity blood cell resolved simulations, adaptive physics refinement techniques, and their applications in understanding cancer cell trajectory tracking. His studies often bridge experimental and computational approaches to model complex biological systems. Grants & Projects: NSF Grant (2023-2026): Investigating microvascular growth through multiphase blood flow modeling and experiments. Teaching: Courses include ME 618 (High Performance Computing & BiopInspired CFD) and ME 407 (Heat Transfer). Labs/Teams: Collaborates with interdisciplinary teams on projects involving microvascular networks, cancer cell transport, and GPU-accelerated CFD simulations. Key collaborators include Prof. William Murfee (NJIT), Dr. Aaron Randles (Johns Hopkins), and others in computational biomedicine.