جست‌وجوی استاد و پژوهشگر

Jürgen Frankeمشاهده پروفایل

استاد

• Nonlinear Time Series Analysis
• Stochastic Processes
• Statistical Methodology
• +۵ مورد دیگر

Prof. Dr. Jürgen Franke is a renowned academic in statistics and applied mathematics. He held the position of Professor C4 (Chair) for Applied Mathematical Statistics at Technische Universität Kaiserslautern from 1988 to 2017. Since 2017, he has been a Consultant Researcher at the Fraunhofer Institute for Industrial Mathematics (ITWM) in Kaiserslautern. His research focuses on nonlinear time series analysis, stochastic processes, and their applications in finance, risk management, and biomedical data analysis. Education: - Diplom in Mathematics, Goethe-Universität Frankfurt a.M., 1974. - PhD (Dr. phil.nat.) in Mathematics, Goethe-Universität Frankfurt a.M., 1980. - Habilitation in Mathematics, Goethe-Universität Frankfurt a.M., 1985. Research Interests: Franke's research emphasizes the development of statistical methodologies for nonlinear time series, spatial data, and stochastic processes. Key areas include local smoothing techniques (kernel and wavelet-based estimates), functional data analysis, resampling methods (bootstrap), and nonparametric approaches in machine learning. His applied work addresses challenges in finance, risk management, and biomedical data analysis where traditional methods may be insufficient. Advising and Grants: While specific grants are not detailed, Franke has collaborated extensively with researchers in applied mathematics and statistics, contributing to numerous projects. His work often intersects with interdisciplinary applications, reflecting a commitment to bridging theoretical and practical domains. Labs/Teams: Affiliated with the Department of Mathematics at TU Kaiserslautern and the Fraunhofer Institute for Industrial Mathematics (ITWM), where he focuses on applied statistical research and collaboration with industry.

Stefano Magriniمشاهده پروفایل

استاد

• Economic Policy
• Regional Economics
• Urban Growth
• +۳ مورد دیگر

Stefano Magrini is a Full Professor at Ca' Foscari University of Venice's Department of Economics. His research examines economic policy, regional and urban economics, income inequality, and spatial econometrics. Recent publications analyze urban growth patterns, regional convergence/divergence dynamics, inequality measurement, and pandemic economic impacts using advanced spatial and statistical methodologies.

Yogendra P. Chaubeyمشاهده پروفایل

استاد

• Sampling
• Distribution Theory
• Survival Analysis
• +۳ مورد دیگر

Dr. Yogendra P. Chaubey is a Professor in the Department of Mathematics and Statistics at Concordia University, Montréal, Canada. Holding a Ph.D. from the University of Rochester (1977), his research focuses on statistical methodology with emphasis on sampling theory, distribution modeling, and survival analysis. Education: Ph.D., University of Rochester (1977) His work spans nonparametric density estimation for circular and non-negative data, wavelet-based noise reduction in biological imaging, entropy estimation, left-censored spatial modeling (e.g., arsenic contamination), and inverse Gaussian distributions. Recent collaborations include applications in protein classification and public health. Dr. Chaubey has published extensively on statistical theory and methodology, including corrections and extensions to classical estimators. His teaching includes advanced courses in sample survey theory and applications.

• Bayesian Statistics
• Statistical Modeling
• Operations Research
• +۷ مورد دیگر

David Valentín Conesa Guillén is a Full Professor in the Department of Statistics and Operations Research within the Faculty of Mathematics at the University of Valencia. He is an active researcher and a key member of the Valencia Bayesian Research Group (VABAR), focusing on advanced statistical methodologies and their applications. His primary research interests encompass Bayesian Statistics , Statistical Modeling , Operations Research , Epidemiological Modeling , Spatial Statistics , and Ecological Statistics . His work bridges theoretical development with practical applications in public health, environmental science, and financial systems. The trends in his recent publications reveal a strong emphasis on developing and applying Bayesian hierarchical models to complex real-world problems. Key areas include dynamic forecasting of influenza outbreaks, modeling spatial distributions of ecological and bioclimatic data, correcting biases in ecological survival estimates, and analyzing efficiency in sectors like banking and higher education. His methodology frequently involves advanced computational techniques like the Integrated Nested Laplace Approximation (INLA). PhD in Statistics, University of Valencia (2000) Thesis: "Inferencia y predicción en colas con ingresos o servicios en grupos" Supervised by Dr. Carmen Armero Cervera David Conesa has supervised academic work, as indicated by his role as a thesis advisor. His research has been supported by collaborative projects, leading to publications in top-tier journals such as Bayesian Analysis , European Journal of Operational Research , and Journal of Agricultural, Biological, and Environmental Statistics . He has extensive collaborations with researchers across Spain and internationally. His research is conducted within the VABAR (Valencia Bayesian Research Group) , a collaborative team dedicated to Bayesian methodology and its applications. This group provides a platform for interdisciplinary projects and the training of future statisticians.

Leslie Chandrakanthaمشاهده پروفایل

استاد

• Mathematical Modeling
• Statistical Analysis
• Regression Models
• +۵ مورد دیگر

Leslie Chandrakantha is a Professor in the Mathematics and Computer Science Department at John Jay College of Criminal Justice (CUNY). He holds a Ph.D. and M.A. in Mathematics from Temple University and a B.S. in Mathematics from the University of Kelaniya, Sri Lanka. His research focuses on mathematical modeling, statistical methods, and technology-enhanced education, with applications in epidemiology and climate data analysis. Education: PhD in Mathematics, Temple University MA in Mathematics, Temple University BS in Mathematics, University of Kelaniya Research Interests: Leslie specializes in regression models, time series analysis, generalized linear models (GLM), and the integration of statistical tools like R and Excel in education. His recent work includes developing risk prediction models for dengue transmission using artificial neural networks and logistic regression, as well as advancing pedagogical methods for statistical concepts through simulation-based learning. Articles Trends: His publications (2014–2020) emphasize statistical methodology, computational tools (R/Excel), and applications in public health. Key themes include disease transmission modeling, confidence interval simulations, and educational technology to enhance statistical literacy. Advising & Grants: No formal advisees or grant details listed. Professional affiliations include the American Statistical Association. Labs/Teams: No specific labs or collaborative teams mentioned.

• Computational Neuroscience
• Neural Coding
• Statistics
• +۳ مورد دیگر

Asohan Amarasingham is a Professor at the City University of New York (CUNY), affiliated with both City College and the Graduate Center. He holds a B.Sc. in Mathematics & Cognitive Science from the University of Virginia (1997), an M.Sc. in Cognitive & Linguistic Sciences from Brown University (1999), and a Ph.D. in Applied Mathematics from Brown (2004). His postdoctoral training was with György Buzsáki at Rutgers University. His research focuses on statistics, neural coding, and computational neuroscience, emphasizing large-scale neurophysiological data analysis and neuronal circuit dynamics. His academic career includes teaching mathematics courses such as Elements of Calculus and Statistics, Probability Theory, and Applied Statistics. Notable advisees include Ben Tupper, who joined the PhD program at the Graduate Center in 2025. His work spans statistical methodologies for neural spike analysis, including studies on spike train reliability, temporal coding, and ensemble activity dynamics. Key contributions include rejecting the Poisson hypothesis in neural spike variability, analyzing hippocampal and prefrontal cortical assemblies, and developing statistical tools for neurophysiological data. His interdisciplinary approach bridges applied mathematics, neuroscience, and computational modeling.

Xiaofeng Shaoمشاهده پروفایل

استاد مدعو

• Statistics
• Data Science
• Time Series Analysis
• +۵ مورد دیگر

Xiaofeng Shao is an Adjunct Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. His research focuses on statistical methodology for time series, spatial data, and high-dimensional data, with applications in econometrics, environmental science, and neuroscience. Shao earned his PhD in Statistics from the University of Chicago in 2006. He has held grants as PI or co-PI from NSF-CMG, NSF-DMS, the Research Board at UIUC, and industry partners like ABInBev and Jump. His work has been recognized with awards such as the Tjalling C. Koopmans Econometric Theory Prize (2009) and the Centennial Scholar designation (2013–2016). His research spans long memory processes, functional data analysis, resampling methods, and applications in atmospheric science, economics, and neuroscience. Notable contributions include self-normalization techniques for high-dimensional time series and spatial bootstrap methods. He has advised numerous students and collaborates across disciplines, maintaining an active publication record in top journals like the Journal of the American Statistical Association and Biometrika.

• Operations Research
• Optimization Algorithms
• Logistics
• +۶ مورد دیگر

Dr. Mahdi Shavarani is a Lecturer in Business Analytics at the Kent Business School, University of Kent. He holds two Ph.D.s: one in Industrial Engineering from Eastern Mediterranean University (2018), and another in Multi-Objective Optimization and Multi-Criteria Decision-Making from the University of Manchester. His research focuses on optimizing real-world business challenges through advanced algorithms, with specialties in logistics, supply chain management, transportation, and manufacturing systems. Education: - Ph.D. in Industrial Engineering, Eastern Mediterranean University (2018) - Ph.D. in Multi-Objective Optimization and Multi-Criteria Decision-Making, University of Manchester Research Interests: Dr. Shavarani specializes in developing sustainable optimization models using machine learning and interactive evolutionary algorithms. His work emphasizes reducing decision-makers' cognitive load while improving computational efficiency. Key areas include multi-criteria decision-making, dimension reduction in high-dimensional objective spaces, and simulating decision-making behaviors. He is affiliated with the Decision and Cognitive Science Research Center at Alliance Manchester Business School. Publications: His recent work explores topics like energy-efficient robotic systems, dynamic preference adaptation in algorithms, and congested facility location problems. Over 30 peer-reviewed publications demonstrate his contributions to optimization theory and practical applications. Grants and Advising: No specific grants or advising roles are detailed in the provided text. Affiliations: - Member, International Society on Multiple Criteria Decision Making - Affiliated Researcher, Decision and Cognitive Science Research Center

Neil Spencerمشاهده پروفایل

استادیار

• Network Analysis
• Bayesian Modeling
• Forensic Statistics
• +۲ مورد دیگر

Dr. Neil Spencer is an Assistant Professor in the Department of Statistics at the University of Connecticut. His research integrates Bayesian inference, network analysis, and computational statistics, with applications ranging from forensic science to neurological disorders. He earned a PhD in Statistics and Machine Learning from Carnegie Mellon University, MSc from University of British Columbia, and BScH from Acadia University. Research focuses on developing novel methods for network data analysis (latent position models, efficient MCMC), robust Bayesian inference, and forensic statistics. Publications demonstrate consistent innovation in computational techniques for complex data structures and interdisciplinary applications. Teaching includes STAT5410 (Statistical Computing) and STAT3345Q (Probability Models for Engineers). He co-advised PhD candidate Tolani Olarinre and participates in the New England Statistical Society's NextGen committee. Research publications emphasize methodological innovations in network modeling, Bayesian computation, and experimental design, with significant applications in neuroscience and forensic science.

Pratheepa Jeganathanمشاهده پروفایل

استادیار

• Statistical Learning
• Spatial Statistics
• Bayesian Methods
• +۳ مورد دیگر

Dr. Pratheepa Jeganathan is an Assistant Professor in the Department of Mathematics and Statistics at McMaster University. Her research focuses on developing statistical methods for multi-view learning, particularly in modeling dependencies across heterogeneous data sources. Applications span molecular microbiology, spatial omics, sensor-based traffic data, and loss reserving. Her methodological work includes generative models, Bayesian sampling, constrained clustering, and spatio-temporal statistics. Education: PhD in Mathematics (Statistics) from Texas Tech University (2016), Postdoctoral Fellowship at Stanford University (2016–2020). Research Interests: Spatial statistics, statistical learning, high-throughput data methods, and statistical theory. Recent work includes analyzing microbiome interventions using transfer functions, studying vaginal microbiota communities, and applying recurrent neural networks to multivariate loss reserving. She has published in journals like PLoS Computational Biology and Genome Biology . Teaching: Instructs courses in data science (STATS 3DA3, CSE 780), statistical research projects, and graduate-level topics in statistics.

Albert Young-Sun Kimمشاهده پروفایل

دانشیار

• Spatial Epidemiology
• Forest Ecology
• Data Science
• +۳ مورد دیگر

Albert Young-Sun Kim is an Associate Professor in the Department of Statistical & Data Sciences at Smith College. His work bridges data science, statistics, and ecology, with applications in spatial epidemiology and forest ecology. Previously, he held positions at Reed, Middlebury, and Amherst Colleges. Ph.D., University of Washington B.Sc., McGill University Kim’s research focuses on spatial epidemiology , forest ecology , and data science education . He develops tools like the Forestecology and SpatialEpi R packages, emphasizing reproducible workflows and ethical data practices. His teaching integrates real-world data analysis and tools like the fivethirtyeight R package. His recent publications highlight interdisciplinary applications of data science, including ecological modeling, statistical education, and computational methods. Key trends include spatial cross-validation for ecological models, Bayesian cluster detection in epidemiology, and promoting tidy data principles in curricula.

Chenlei Lengمشاهده پروفایل

استاد

• Statistical Methodology
• High-Dimensional Statistics
• Network Analysis
• +۳ مورد دیگر

Professor Chenlei Leng is a faculty member in the Department of Statistics at the University of Warwick. Previously, he held positions at Peking University, the University of Munich, and the National University of Singapore. He earned a bachelor's degree in mathematics from the University of Science and Technology of China and a PhD in statistics from the University of Wisconsin-Madison. His research focuses on developing statistical methodologies for analyzing complex, high-dimensional data, including network analysis, longitudinal data modeling, and machine learning applications. He has organized workshops on statistical network analysis and co-directed the Oxford-Warwick Statistics Centre for Doctoral Training (CDT). Elected Member of the International Statistical Institute Fellow of the Institute of Mathematical Statistics His current research group at Warwick explores network structures and high-dimensional statistical challenges. Advising includes PhD candidates like Yuanhe Zhang and Xinyuan Fan, alongside visiting students from institutions such as Tsinghua University. He actively participates in academic leadership roles, including chairing the Research Section of the Royal Statistical Society. Publications span topics like network models, covariance estimation, and sparse regression, with a focus on methodological advancements in statistics and machine learning.

Feng Guمشاهده پروفایل

استاد

• Modeling and Simulation
• Complex Systems
• High Performance Computing
• +۴ مورد دیگر

Feng Gu is a Professor of Computer Science at The College of Staten Island, CUNY, and a doctoral faculty member at The Graduate Center, CUNY. He holds a BS in Mechanical Engineering from China University of Mining and Technology, MS in Information Systems from Beijing Institute of Machinery, and MS/PhD in Computer Science from Georgia State University. His research focuses on Modeling and Simulation , Complex Systems , High Performance Computing , and Bioinformatics . Notable contributions include work on wildfire spread simulation, particle filters, and machine learning applications in healthcare and genomics. Recent grants include a $563,411 NSF grant for crime analysis and a $40,000 CUNY-IRG grant for obesity modeling. He has received awards such as the TMS/DEVS 2018 Best Paper Award and the 2018 Emerald Literati Award. His work bridges computational methods with real-world challenges in environmental science, cybersecurity, and social policy. Professional activities include serving as a reviewer for journals like IEEE Transactions and conferences like Winter Simulation Conference . He has organized sessions at events such as the International Conference on Cloud Computing and Big Data Analysis (ICCCBDA).

Ian McLeodمشاهده پروفایل

استاد

• Time Series Analysis
• Wavelet Methods
• Data Visualization
• +۴ مورد دیگر

Ian McLeod is a Professor in the Department of Statistical and Actuarial Sciences at Western University. He holds a Ph.D. from the University of Waterloo (1977) and conducts research in time series analysis, wavelet methods, data visualization, neural networks, and statistical computing. His work spans traffic safety, climate modeling, and clinical epidemiology, demonstrating interdisciplinary applications of statistical methodologies. His research focuses on developing advanced statistical tools for real-world problems, including traffic legislation impact assessment, climate change analysis, and medical diagnostics. Publications frequently involve collaborative studies with healthcare and environmental science researchers. McLeod has developed specialized R packages for time series analysis and contributed to methodological advancements in ARMA modeling and unit root testing. His recent work includes stroke classification systems and climate impact simulations using multivariate resampling techniques.

Curtis Cohenourمشاهده پروفایل

دانشیار

• Avionics Engineering
• Navigation Systems
• GPS and Satellite Technology
• +۴ مورد دیگر

Curtis Cohenour is an Associate Professor in the Department of Engineering Technology and Management at Ohio University's Russ College of Engineering and Technology. He holds a Ph.D. in Electrical Engineering (2009) from Ohio University, along with earlier degrees from Ohio University (M.S., 1988) and West Virginia Institute of Technology (B.S., 1980). His professional background includes industry roles as an electrical engineer and project manager, alongside research engineer positions at Ohio University's Avionics Engineering Center since 2002. Research interests focus on avionics systems, navigation technology (including GPS/WAAS), image geo-registration corrections, and aerospace data analysis. His work with the Air Force Research Lab addresses airborne platform imaging errors, while collaborations span projects like the WPAFB WAMI data set and MLS/Airbus A380 critical area assessments. He is a registered professional engineer in Ohio and West Virginia. Publications emphasize aerospace imaging accuracy (e.g., WAMI error elimination, diagnostic clock synchronization) and satellite signal integrity (GPS orbit/clock error distributions). He has contributed to education innovations, including PLC course design using finite state machines and Arduino-based labs. Technical reports cover critical aviation infrastructure impacts and advanced DGPS multipath models. Cohenour's affiliations include the Air Force Research Laboratory and Avionics Engineering Center. His work bridges academic research with practical aerospace engineering challenges in navigation, imaging, and system validation.