جست‌وجوی استاد و پژوهشگر

David I Wartonمشاهده پروفایل

استاد

• Statistical Ecology
• Ecological Statistics
• Applied Statistics
• +۱۱ مورد دیگر

Professor David I Warton is a leading ecological statistician at the School of Mathematics and Statistics , University of New South Wales (UNSW). He leads the Eco-Stats research group , which has secured over $9M in Australian Research Council (ARC) funding and focuses on developing model-based statistical methodologies to address ecological questions. Education: PhD in Ecological Statistics (Macquarie, 2003), MA in Mathematical Statistics (Sydney, 1999), BSc (Hons) in Ecology and Pure Mathematics (Sydney, 1997) His research spans allometry , multivariate abundance analysis , species distribution modelling , and error-in-variables regression , with applications ranging from biodiversity conservation to medical studies. His recent publications emphasize AI-driven ecological data analysis , phylogenetic GLMMs , and efficient algorithm development . Scientific Awards: ARC College of Experts (2025-27), Highly Cited Researcher (2019-22), Fellow of the Royal Society of NSW (2020), Christopher Heyde Medal (2014) He supervises PhD students working on machine learning for fish classification, spatio-temporal species distribution models, and latent variable approaches in ecology. As academic lead of Stats Central , he promotes statistical collaboration across disciplines, and serves as Associate Editor for leading journals.

• Statistics on Manifolds
• Bayesian Theory
• Deep Learning
• +۶ مورد دیگر

Lizhen Lin is a Professor of Statistics in the Department of Mathematics at the University of Maryland. Her research bridges statistical theory, Bayesian methods, and machine learning through theoretical and applied work in statistics on manifolds, deep learning, and network analysis. University: University of Maryland Department: Mathematics Email: lizhen01@umd.edu Office: Kirwan 1107 Her research focuses on: Foundations of deep neural networks via statistical theory Bayesian modeling for infinite-dimensional and high-dimensional data Geometry and statistics for manifold-valued data Network analysis with covariates and topological structures Robust optimization and inference on non-Euclidean spaces Recent publications emphasize: Bayesian community detection and stochastic blockmodels Variational inference and posterior contraction Manifold-adaptive deep generative models High-dimensional change point detection Applications to microbiome networks and DNA topology Teaching includes courses on linear models (STAT 741) and statistical foundations of deep learning (STAT 818).

Jae Kyoung Kimمشاهده پروفایل

دانشیار

• Dynamical systems
• Biological oscillators
• Circadian rhythms
• +۷ مورد دیگر

Jae Kyoung Kim is an Associate Professor in the Department of Mathematical Sciences at KAIST (Korea Advanced Institute of Science and Technology) and serves as Chief Investigator in the Biomedical Mathematics Group at the Institute for Basic Science (IBS). He is also a Member of the Young Korean Academy of Science and Technology and the KOFST Future Generation Committee. His academic journey began with a B.S. from Seoul National University, followed by a Ph.D. from the University of Michigan, Ann Arbor, under advisors Daniel Forger and Victoria Booth. He completed postdoctoral training at the Mathematical Biosciences Institute at Ohio State University. Kim's research spans mathematical and biological domains, focusing on dynamical systems, biological oscillators (particularly circadian rhythms), systems pharmacology, cancer chronotherapy, stochastic processes, Bayesian inference, and single-cell sequencing data analysis. His work bridges abstract mathematical concepts with practical biomedical applications, developing mathematical frameworks to solve complex biological problems related to sleep patterns, drug development, and cancer treatment timing. His publications demonstrate a strong trend toward integrating machine learning with traditional mathematical modeling, particularly in sleep medicine and circadian biology. Recent work emphasizes wearable technology applications, personalized chronotherapy, and the development of clinical tools with mathematical foundations. The research shows increasing interdisciplinary collaboration across mathematics, biology, medicine, and data science. Choi Seok-jeong Award (Minister of Science and ICT Award) KAIST Global Research Cooperation Awards Citation from the Minister Ministry of Science and ICT Innovation Award from MEDITEK J. Shelton Horsley Research Award Yonam International Collaborative Research Professorship KSIAM Young Researcher Award Kim actively advises numerous graduate students and postdoctoral fellows, with several former students securing prestigious academic positions. His research has been supported by significant grants including Samsung Science & Technology Foundation, LG Yonam Foundation, and Pfizer Inc. He organizes major international conferences and serves on editorial boards for leading journals including SIAM Review, PLOS Computational Biology, and Current Opinion in Systems Biology. His book '수학이 생명의 언어라면' (If Mathematics is the Language of Life) has gained recognition as a significant contribution to mathematical biology education.

• Wireless Networks
• Network Scheduling
• Sufficient Dimension Reduction
• +۴ مورد دیگر

Bin Li is an Associate Professor at the School of Electrical Engineering and Computer Science. His research focuses on wireless networks, network scheduling, sufficient dimension reduction, and statistical inference. NSF-funded projects: EAGER: TaskDCL, CAREER: Wireless Collaborative Mixed Reality Networking, CNS Core: Scalable Algorithms for Virtual Reality Over Wireless Networks. Grants include foundational work in AI-driven task training, geospatial digital twins, and joint communication-computation-learning systems. His research spans wireless scheduling algorithms, data freshness optimization, and nonlinear sufficient dimension reduction. Recent work explores Fréchet regression, functional graphical models, and kernel-based hypothesis testing. Articles highlight interdisciplinary applications in computer science, statistics, and mathematics. Statistical methods dominate his contributions, including Bayesian credible sets, copula models, and additive independence frameworks. Collaborations extend to multi-source genomic data analysis and immersive educational platforms via augmented reality. With an h-index of 16 and 74 research outputs, Bin Li’s expertise intersects wireless network optimization and statistical learning. His work addresses challenges in edge computing, cloud offloading, and cyber-physical systems through algorithmic innovation and theoretical rigor.

John Fiebergمشاهده پروفایل

استاد

• Statistical Ecology
• Wildlife Telemetry
• Habitat Selection Modeling
• +۳ مورد دیگر

John Fieberg is a Professor in the Department of Fisheries, Wildlife, and Conservation Biology at the University of Minnesota, where he applies statistical and mathematical models to ecological problems with emphasis on wildlife telemetry data, survival analysis, and habitat selection modeling. His educational credentials include: Ph.D. in Biomathematics from North Carolina State University MS in Biostatistics from the University of North Carolina-Chapel Hill BA in Mathematics from Westminster College Fieberg's research bridges sophisticated modeling using MCMC techniques for complex ecological data and practical statistical guidance for biologists. He focuses on tailoring models to address challenges like imperfect detection in wildlife surveys, autocorrelation in movement data, and functional responses in habitat selection, while advocating for robust yet accessible methods that deliver 99% of analytical value for 99% of applications. His recent publications (2020-2024) reveal concentrated advancements in animal movement ecology, habitat selection methodology, and statistical validation frameworks. Key trends include innovative goodness-of-fit testing via lineup protocols, integrated step-selection analyses accounting for individual variability, and critical examinations of detection scale in multi-sensor wildlife monitoring systems, complemented by two foundational textbooks on ecological statistics. He directs the Fieberg Lab, which synergizes research with teaching through courses like Biometry (FW4001) and Statistics for Ecologists (FW8051), incorporating real-world ecological examples to demonstrate statistical concepts and mentoring future quantitative ecologists for natural resource management careers.

• macroeconomics
• econometrics
• Applied economics
• +۲ مورد دیگر

Dr. Ivan Mendieta-Munoz is an Associate Professor with tenure in the Department of Economics at the University of Utah, where he has been a faculty member since 2016. He holds a Ph.D. in Economics from the University of Kent, an M.S. in Economics and Econometrics from the University of Kent, and a B.S. in Economics from the National Autonomous University of Mexico (UNAM). His research focuses on macroeconomics and econometrics , particularly monetary economics, hysteresis dynamics, Bayesian time series methods, nonlinear/non-Gaussian models, and the interplay between heterogeneity, inequality, and business cycles. His work has been published in journals like Journal of Economic Dynamics and Control , Economics Letters , and Studies in Nonlinear Dynamics and Econometrics . Notable awards include the Superior Teaching Award from the University of Utah, an INET research grant , and the Best Paper Award at the INFER Annual Conference. He has refereed for over thirty journals in economics and statistics. Education: Ph.D., Economics, University of Kent (2012–2016) M.S., Economics and Econometrics, University of Kent (2011–2012) B.S., Economics, National Autonomous University of Mexico (UNAM) (2006–2011) Teaching: Courses on Macroeconomics, Bayesian Macroeconometrics, Shadow Banking, and Forecasting Models at the University of Utah, SOAS University of London, and University of Kent.

• Smooth Transition Models
• State-Space Models
• Macroeconometrics
• +۸ مورد دیگر

Yukai Yang is a Senior Lecturer at the Department of Statistics, School of Economics and Business, Uppsala University. His research bridges econometric theory with applied statistical methods. Education : B.Eng, Shanghai Jiao Tong University Cand. Polit, University of Copenhagen PhD, Aarhus University (2012) His research spans smooth transition models , state-space models , Bayesian VAR , panel data analysis , and directional statistics , with recent interdisciplinary work in medical data science . Articles highlight methodological innovation in time series , machine learning , and mobile biometrics . Scientific Awards : None listed in provided data. His work includes software development (e.g., mfbvar package) and collaborations in multimedia systems and health economics .

Jonathan O. Hutchinsمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Active Learning
• +۷ مورد دیگر

Jonathan O. Hutchins is an Associate Professor of Computer Science at Grove City College. His research focuses on probabilistic machine learning and data mining, particularly Bayesian modeling, time series analysis, and sensor data interpretation. He earned a Ph.D. in Computer Science from the University of California, Irvine, with a focus on artificial intelligence, and holds a B.S. in Chemical Engineering from Virginia Tech. His research interests include: Bayesian networks for time series data Active learning systems Unsupervised clustering techniques Recommender systems Text mining applications Markov-Poisson models His publications demonstrate expertise in sensor-based modeling, event detection algorithms, and probabilistic analysis. During his 9-year tenure at Google (2009-2018), he developed AI systems for user query processing and recruitment efforts. He currently teaches machine learning at Grove City College, including an innovative course where students created AI chatbots trained on Dr. Seuss texts.

• Business Administration
• Social Systems Engineering
• Statistical Science
• +۱ مورد دیگر

Hiroki Iwamoto is an Assistant Professor at Waseda University's School of Creative Science and Engineering, Department of Industrial & Management Systems Engineering since 2023. Previously, he held research and teaching positions at Keio University and Tokyo Keizai University. His academic work bridges Business Administration, Social Systems Engineering, and Statistical Science with applications in Accounting. Education: Ph.D. in Engineering (2021, Keio University) Research Interests: Management Strategy Human Resource Management Creating Shared Value (CSV) Statistical Quality Control Business Analytics Recent Research Trends: Focus on advanced statistical methods like Mahalanobis-Taguchi Systems, DEA-R models, and SMOTE for small-sample anomaly detection. His work connects quality management with corporate social responsibility and human capital analytics. Scientific Awards 22th Asian Network for Quality Best Paper Award (2024) 21th Asian Network for Quality Best Paper Award (2023) 20th Asian Network for Quality Best Paper Award (2022) 2015 Japan Academy of Management Information Student Award Teaching: Delivers courses on Business Data Analysis, Multivariate Analysis, and Science/Engineering Literacy. Professional memberships span Japanese Society of Applied Statistics, Japan Industrial Management Association, and Bayesian SEM research communities.

Xiaofeng Liمشاهده پروفایل

استادیار

• Travel and Traffic Monitoring
• Public Transport
• Big Data and AI
• +۳ مورد دیگر

Xiaofeng Li is an Assistant Professor in Transportation Management at the School of Travel Industry Management, University of Hawaiʻi at Mānoa. He holds a Ph.D. in Transportation Engineering from The University of Arizona (2021), an M.S. in Transportation Planning and Management from Beijing Jiaotong University (2017), and a B.S. in Traffic Engineering from Fujian Agriculture and Forestry University (2014). His research focuses on Travel and Traffic Monitoring , Public Transport , Big Data and AI , and Shared Micro-Mobility , with applications in Intelligent Transportation Systems , Traffic Control , and Urban Mobility Optimization . His work leverages advanced data analytics and AI to address challenges in transit operations, pedestrian safety, and sustainable transportation. Selected publications highlight trends in using synthetic control methods to analyze transit integration, Bayesian regression trees for pedestrian volume estimation, and interaction networks for transit arrival prediction. His studies often incorporate real-time data and event-based analytics to improve urban infrastructure efficiency. Scientific awards include: Outstanding Graduate Student Award, CAEM, University of Arizona (2021) Best Graduate Research Paper Award, ITS Arizona Conference (2020) Contact: Email xiaofeng.li@hawaii.edu | Phone: 808-956-7111 | Office: George Hall 204, University of Hawaiʻi at Mānoa.

• Quantitative Methods for Economy and Business
• Statistics
• Econometrics
• +۷ مورد دیگر

Maria Antonia Barceló Rado is a Professor in the Department of Economics and affiliated with the Faculty of Economic and Business Sciences at the University of Girona. She is also a member of the Research Group in Statistics, Econometrics and Health (GRECS) and collaborates with national and regional research networks like CIBERESP and COSDA. Her research spans methodological and applied fields, including Survival Analysis Bayesian Statistics Spatial-Temporal Modeling Clinical and Environmental Epidemiology Real-World Data applications Recent publications focus on environmental stressors, pandemic impacts, and spatial inequalities. Notably, her work addresses air pollution’s health effects, socioeconomic disparities in vaccination, and longitudinal studies on chronic disease. She has contributed to methodological advancements in compositional regression and spatiotemporal modeling. Her collaborations extend to institutions like Johns Hopkins University and the Functional Unit of Motor Neuron Disease at Bellvitge University Hospital.

Jon Cockayneمشاهده پروفایل

دانشیار

• Bayesian Numerical Methods
• Probabilistic Numerical Methods
• Differential Equations
• +۴ مورد دیگر

Jon Cockayne is an Associate Professor at the University of Southampton specializing in Bayesian Numerical Methods , Probabilistic Numerical Methods , and Uncertainty Quantification . His research focuses on integrating probabilistic approaches into numerical algorithms, particularly for solving linear systems , differential equations , and MCMC output analysis . He has contributed to BayesCG (Bayesian Conjugate Gradient) and Computation-Aware Gaussian Processes . Notable collaborations include work on Statistical Finite Element Methods , Probabilistic Iterative Methods , and Calibrated Numerical Algorithms for industrial applications like Hydrocyclone Equipment state estimation.

• Biomedical Signal Processing
• Respiratory Analysis
• Cardiovascular Monitoring
• +۲ مورد دیگر

Daniel Romero Pérez is a Lecturer at the Department of Systems Engineering, Automation and Industrial Informatics within the Barcelona East School of Engineering (EEBE) at Universitat Politècnica de Catalunya (UPC). He maintains a research affiliation with the Institute for Bioengineering of Catalonia (IBEC) and leads projects in the BIOSPIN group focused on Biomedical Signal Processing and Interpretation. His research centers on biomedical signal processing with applications in respiratory/cardiac monitoring and disease diagnostics. Key interests include: Developing machine learning models for obstructive sleep apnea detection using ECG and respiratory signals Analyzing heart rate variability in cardiovascular disorders like Brugada syndrome Creating non-invasive respiratory monitoring systems for COPD patients Investigating hypoxia effects on cardiac electrophysiology Publication analysis (29 conference papers, 18 journal articles) reveals consistent focus on: Algorithm development for physiological signal interpretation Clinical validation of wearable sensors Multivariate modeling of cardiorespiratory pathologies Translational research bridging engineering and clinical medicine He collaborates extensively with European institutions on competitive R&D projects including 'Smart Health Ecosystem for Breathing Illnesses' and 'Unintrusive Remote Health Assessment'. Research outputs demonstrate strong emphasis on practical healthcare solutions with 16 open-access publications.

• Bayesian nonparametric methods
• Statistical analysis of financial data
• Sparse Bayesian regression
• +۳ مورد دیگر

Jim Griffin is a Professor of Statistics at the University of Kent's School of Mathematics, Statistics and Actuarial Science. His research focuses on Bayesian nonparametric methods, computational statistics, and applications in financial and economic data analysis. He has collaborated extensively with researchers such as M. Kalli, F. Leisen, and M.F.J. Steel, producing influential work on nonparametric priors, volatility modeling, and sparse regression techniques. Griffin's research interests include developing novel Bayesian methodologies for high-dimensional data, time series analysis, and stochastic volatility modeling. His contributions to computational methods, such as adaptive MCMC and sequential Monte Carlo algorithms, have advanced efficient inference in complex statistical models. He has supervised numerous PhD students, including Alex Diana, Mark Sinclair-McGarvie, and Su Wang, whose work spans Bayesian nonparametrics, computational methods, and financial econometrics. Griffin has published widely in top-tier journals like the Journal of the Royal Statistical Society , Journal of Econometrics , and Bayesian Analysis . His recent work emphasizes integrating computational efficiency with theoretical rigor, addressing challenges in modern statistical applications across finance, ecology, and bioinformatics.

Maria Kalliمشاهده پروفایل

مدرس ارشد

• Bayesian Nonparametric Methods
• Bayesian Regression
• Time Series Modelling
• +۳ مورد دیگر

Dr. Maria Kalli is a Senior Lecturer in Statistics at the Department of Mathematics, King's College London, since August 2021. Previously, she held the position of Senior Lecturer in Statistics at the University of Kent and worked as an investment banker at Goldman Sachs in New York. She holds a BSc in Econometrics and Mathematical Economics (LSE), an MBA in Financial Engineering (NYU Stern), an MSc in Mathematical Statistics (University of Michigan), and a PhD in Statistics (University of Kent). She is a Fulbright Scholar and Senior Fellow of the UK Higher Education Academy. Her research focuses on Bayesian Nonparametric Methods, Bayesian Regression, and Time Series Modelling in Macroeconomics and Finance, with applications in financial econometrics and high-dimensional data analysis. She serves as the PhD Admissions Tutor for the Statistics group. Her work emphasizes methodological advances in Bayesian statistics, including MCMC techniques, shrinkage priors, and volatility models. Notable contributions include the development of Bayesian nonparametric vector autoregressive models and flexible dependence frameworks for financial time series. Recent research explores market liquidity effects and predictive distributions in financial markets. Education: BSc Econometrics and Mathematical Economics, London School of Economics MBA Financial Engineering, New York University Stern School of Business MSc Mathematical Statistics, University of Michigan PhD Statistics, University of Kent Scientific Awards: Fulbright Scholar Senior Fellow of the UK Higher Education Academy Advising & Grants: While specific grants are not detailed, her research has been supported through institutional funding and collaborative projects. She actively mentors PhD candidates in Bayesian statistical methodologies and time series analysis. Labs/Teams: She contributes to King's Statistics group, focusing on time series analysis, Bayesian computation, and econometric modelling.