جست‌وجوی استاد و پژوهشگر

Ingmar Nolteمشاهده پروفایل

استاد

• Financial Econometrics
• Asset Pricing
• Market Microstructure
• +۶ مورد دیگر

Professor Ingmar Nolte serves as Professor of Finance & Econometrics and Director at Lancaster University's Management School within the Accounting and Finance department. His academic career spans decades of research in financial econometrics, asset pricing, and market microstructure, establishing him as a leading scholar in high-frequency financial data analysis. Professor Nolte's research interests focus on financial econometrics, asset pricing, market microstructure, and forecasting. His work particularly emphasizes the construction of volatility estimators, jump and drift burst detection using high-frequency data, factor investing to improve factor information content, and the market microstructure of option markets. He develops dynamic micro-econometric techniques including discrete choice, count data, and point process models for analyzing complex finance relationships and trading processes. His recent publications demonstrate consistent focus on high-frequency data applications, with particular emphasis on price durations, volatility modeling, and covariance estimation. The research trajectory shows progression from fundamental volatility estimation techniques to increasingly sophisticated applications in portfolio management, factor investing, and risk-neutral density estimation. His work bridges theoretical econometrics with practical financial applications, particularly in the domain of market microstructure analysis. Professor Nolte actively supervises postgraduate research, currently advising four students including Marco Cinquetti and Lewei He. His research has appeared in top finance and econometrics journals including Journal of Econometrics, Journal of Financial Econometrics, and Journal of Financial and Quantitative Analysis. He has organized and participated in numerous academic conferences and workshops, including the Lancaster-Manchester-Warwick-Oxford Joint PhD Workshop on Finance and Econometrics.

Xanthi Xanthipiمشاهده پروفایل

استادیار

• statistical modeling
• inference
• time series
• +۲ مورد دیگر

Xanthi Xanthipi serves as Assistant Professor in the Department of Statistics at the School of Information Sciences and Technology, Athens University of Economics and Business (AUEB), a position held since July 2018. Prior appointments include postdoctoral research at the University of Cyprus (2011–2013) and a Marie Skłodowska-Curie fellowship at Ca' Foscari University of Venice (2016–2018). Education: B.Sc. in Statistics, Athens University of Economics and Business (2003) M.Sc. in Biostatistics, University of Athens (2006) Ph.D. in Statistics, Athens University of Economics and Business (2011) Her research centers on statistical modeling for discrete data systems, with emphasis on time series inference, biostatistics applications, and methodological innovations for count data processes. Key contributions include developing novel frameworks for INAR modeling and multivariate volatility analysis, bridging theoretical statistics with real-world biological and financial datasets. Publication trends (2011–2015) reveal concentrated expertise in discrete-valued time series, featuring breakthroughs in saddlepoint approximation techniques, Cholesky-based volatility modeling, and composite likelihood estimation for multivariate INAR processes. Her work demonstrates consistent focus on improving computational efficiency and statistical rigor in non-Gaussian time series contexts. Scientific Awards: Marie Skłodowska-Curie fellowship Her Marie Curie fellowship underscores European recognition of her research trajectory. While specific grant details remain unlisted, her publication record indicates sustained funding for methodological statistics research. As an active faculty member, she maintains advising responsibilities for graduate students in statistical theory and applications despite no named advisees appearing in the source material.

Paolo Carmelo COZZUCOLIمشاهده پروفایل

دانشیار

• Statistics
• Statistical Quality Control
• Multivariate Statistical Processes
• +۳ مورد دیگر

Paolo Carmelo COZZUCOLI is an Associate Professor of Statistics (scientific-disciplinary sector SECS-S/01) at the Department of Economics, Statistics and Finance "Giovanni Anania" at the University of Calabria, where he has been in service since December 2006. Previously, he worked as a Statistics Researcher at the Faculty of Economics and Political Sciences of the University of Calabria from June 1992 to December 2006. Professor Cozzucoli's research focuses on statistical methodologies with particular emphasis on multivariate statistical processes, Bayesian statistics, and applications in quality control. His work spans various domains including social media analysis, environmental sustainability, financial forecasting, and chemical engineering. He has developed innovative approaches for hate speech detection, service quality assessment, and statistical process monitoring. His recent publications demonstrate a consistent focus on multivariate analysis techniques applied across diverse fields. The research shows a progression from fundamental statistical methodology development toward increasingly applied interdisciplinary work, with strong emphasis on practical implementation of statistical models in real-world settings. His work frequently employs Bayesian approaches, permutation tests, and multivariate process control methods. Professor Cozzucoli teaches courses including "Statistics for decisions," "Statistica ed elementi di inferenza," and "Deep learning Techniques and Neural Networks" for programs in Public Administration Sciences, Economics, and Data Science for Business Analytics. He is an active member of the "Statistica & Demografia" research group, which focuses on developing new statistical and demographic methodologies for data collection, analysis, description, and interpretation. Within this group, his specific interests include: multivariate statistical process control, Bayesian networks, sampling theory, and applications in quality management.

Andreas Artemiouمشاهده پروفایل

استاد

• Statistical Methods
• High-Dimensional Data Analysis
• Dimension Reduction
• +۳ مورد دیگر

Dr. Andreas Artemiou serves as Professor, Vice Rector for Academic Affairs and Quality Assurance, and Dean of the Technology and Innovation School at the University of Larnaca's Department of Information Technologies. His leadership spans academic administration and cutting-edge statistical research with global collaborations. His academic foundation includes: BSc in Mathematics and Statistics from University of Cyprus (2005) MSc and PhD in Statistics from Pennsylvania State University (2008, 2010) Artemiou's research pioneers statistical methods for high-dimensional datasets, specializing in dimension reduction, kernel techniques, and machine learning applications. His work bridges theoretical innovation with practical implementations across engineering, computer science, and medical sciences, particularly evident in pandemic-related mortality analysis and cytometry data processing. Recent publications (2021-2024) reveal accelerating focus on SVM-based dimension reduction, sparse modeling, and medical applications. The trajectory shows increasing interdisciplinary impact, with 2024 works emphasizing matrix data analysis and time-series dimension reduction for real-world health crises. His professional recognition includes: New Researcher Fellow at Statistics and Applied Mathematical Sciences Institute Artemiou actively contributes to major collaborative initiatives without explicit grant details. His editorial role at Computational Statistics and Data Analytics journal and board membership in the European Statistical Computing Association highlight academic leadership. Current projects drive innovation in cytometry analysis and pandemic mortality modeling. He directs CytoPy - an autonomous cytometry analysis framework - and leads pandemic mortality research through the international CMOR consortium, demonstrating commitment to translating statistical theory into public health solutions.

Vanni Noferiniمشاهده پروفایل

دانشیار

• Numerical Analysis
• Linear Algebra
• Matrix Polynomials
• +۴ مورد دیگر

Vanni Noferini is an Associate Professor in the Department of Mathematics and Systems Analysis at Aalto University, part of the School of Science. His research focuses on numerical linear algebra, matrix theory, and their applications in network science and complex systems. He holds joint affiliations with the Mathematical Statistics and Data Science, Algebra and Discrete Mathematics, and Numerical Analysis groups within the department. His work spans topics including matrix polynomials, eigenvalue problems, structured matrices, and the analysis of networks using algebraic and spectral methods. Notable contributions include studies on Smith forms, Riemannian optimization for matrix nearness problems, and non-backtracking centrality measures in graphs. Noferini has authored over 50 publications in top-tier journals such as SIAM Journal on Matrix Analysis and Applications , Linear Algebra and Its Applications , and Journal of Complex Networks . His research also intersects with applications in control theory, graph algorithms, and random matrix theory.

• Biomedical Data Science
• Clinical Trials
• Rare Diseases
• +۴ مورد دیگر

Martin Geroldinger is a researcher at the Research Program of Biomedical Data Science at Paracelsus Medical University. His work focuses on statistical methodologies for clinical trials in rare diseases, particularly Epidermolysis Bullosa, and biomedical data science applications. Key roles: Co-author in clinical trials, contributor to AI-based diagnostic pathways, organizer of biomedical data science colloquia Research interests: He specializes in optimizing clinical trial designs for rare genetic disorders, analyzing count and binary data in cross-over studies, and leveraging machine learning for medical research. His work addresses challenges in patient burden reduction and outcome measurement. Projects: Active in AI-driven medical knowledge extraction, 'long COVID' diagnostic pathways, and statistical approaches for rare epilepsies. Collaborates with Prof. Zimmermann and Dr. Thiel on rare disease trials. Activities: Organized the 2nd Biomedical Data Science Colloquium (2024), presented on statistical inference for rare disease trials (2022)

• Statistical Ecology
• Markov Chains
• Spatial Data Analysis
• +۳ مورد دیگر

Stephen Berg is an Assistant Professor in the Department of Statistics at Pennsylvania State University. His research spans statistical ecology, computational statistics, and biostatistics, with a focus on spatial modeling and Markov chain applications. Affiliations: Huck Institutes of the Life Sciences, Center for Infectious Disease Dynamics Research Interests: Berg develops statistical methodologies for ecological and biomedical applications. His work includes: Ecotone detection using co-occurrence properties Markov chain-based variance estimation for computational models Spatial categorical data analysis for historical forest communities Health outcomes modeling in Medicare populations Trends in Publications: His recent work emphasizes shape-constrained inference for autocovariance sequences and multivariate moment least-squares methods, while earlier contributions address ecological dynamics and healthcare analytics. Labs & Affiliations: Berg collaborates with the Huck Institutes of the Life Sciences and the Center for Infectious Disease Dynamics, integrating statistical methods with interdisciplinary research.

Alexandra Badeaمشاهده پروفایل

دانشیار

• Neuroimaging
• Alzheimer's Disease
• MRI Technology
• +۶ مورد دیگر

Alexandra Badea is an Associate Professor in Radiology and Neurology, and an Assistant Professor of Biomedical Engineering at Duke University. She holds a joint appointment at the Center for Brain Imaging and Analysis (BIAC) and is a core faculty member at the Center for in Vivo Microscopy. Her research focuses on neuroimaging techniques for characterizing neurological conditions like Alzheimer’s disease, leveraging MRI and diffusion tensor imaging (DTI) to study brain microstructure and connectivity. Dr. Badea’s work integrates imaging with genetics and behavioral data to develop multivariate biomarkers for early disease detection. She uses high-performance computing for advanced image analysis, including compressed sensing and deformable registration. Key projects include studying genetic risk factors (APOE alleles) and the impact of exercise on brain networks in mouse models. She has led Bass Connections teams involving undergraduate students in translational research, offering courses like BME 394/493 and NEUROSCI 493. Her grants include funding from NIH and NIA for studies on Alzheimer’s-cardiac interactions, aging mouse models, and immune-regulated metabolism. She has published extensively in journals like Neuroimage, PLoS One, and Brain Structure & Function. Dr. Badea also mentors graduate students and postdocs, emphasizing interdisciplinary approaches to neuroimaging and promoting STEM inclusion for women. Her lab develops tools like the SAMBA pipeline for small-animal brain analysis and collaborates on open-source software. She maintains active roles in Duke’s medical school and radiology departments, contributing to both clinical and preclinical imaging research.

Hongmei Jiangمشاهده پروفایل

استاد

• High-throughput genomic analysis
• Metagenomic data analysis
• Microbiome data normalization
• +۵ مورد دیگر

Hongmei Jiang is a Professor of Statistics and Data Science and the MS Program Director in the Department of Statistics and Data Science at Northwestern University's Weinberg College of Arts & Sciences. She holds a Ph.D. from Purdue University (2004). Her research focuses on statistical methodologies for genomic and metagenomic data analysis, including microbiome normalization, multiple comparisons, survival analysis, and precision medicine applications. She has contributed significantly to understanding microbial community dynamics and their implications in health and disease. Her work spans bioinformatics, epigenetics, and clinical statistics, with recent publications addressing microbiome data imputation, primate gut microbiota's role in metabolism, and cardiovascular disease risk prediction using neural networks. She has collaborated on studies involving epigenetic aging, HIV/HPV interactions in carcinogenesis, and forensic microbial source tracking. Dr. Jiang's methodological innovations include novel statistical frameworks for differential abundance testing, quasi-likelihood models for microbiome data, and graphical lasso extensions for omics network analysis. Her research emphasizes interdisciplinary applications across biology, medicine, and ecology, leveraging both frequentist and machine learning approaches. Education: Ph.D. in Statistics, Purdue University, 2004 Affiliations: Department of Statistics and Data Science, Northwestern University Labs/Teams: MS Program Leadership, Metagenomic Data Analysis Group

Gideon Alakeمشاهده پروفایل

پژوهشگر

• Entomology
• Nematology
• Invasive Species Management
• +۴ مورد دیگر

Gideon Alake is a Researcher in the Department of Entomology and Nematology at the University of Florida, working within the Biosecurity Research & Extension (BRE) lab. His role involves managing grant-funded projects focused on insect pest management, regulatory plant health, nematode diagnostics, and data analytics. He leads experimental design, data collection/analysis, grant proposal preparation, and manuscript development. As a mentor, he supports graduate students in statistical methods and professional development. His research interests span entomology, nematology, and data-driven agricultural solutions. Key focuses include invasive species surveillance, statistical modeling of biological systems, and developing sustainable pest management strategies. His work bridges laboratory research with field applications through extension initiatives. Recent articles highlight his contributions to invasive species dynamics (e.g., brown marmorated stink bug), nematode population modeling, and fungal pathogen dispersal via trade pathways. His statistical work critiques traditional data transformation practices and proposes improved methodologies for count data analysis. Alake actively participates in outreach activities to translate research findings into actionable strategies for agricultural stakeholders. His interdisciplinary approach integrates entomological, nematological, and data science principles to address global biosecurity challenges.

Sayar Karmakarمشاهده پروفایل

استادیار

• Time Series Analysis
• High-Dimensional Statistics
• Forecasting
• +۹ مورد دیگر

Sayar Karmakar is an Assistant Professor in the Department of Statistics at the University of Florida. His research focuses on time series analysis, high-dimensional statistics, econometrics, and neural networks. He holds a B.Stat and M.Stat from the Indian Statistical Institute and a PhD in Statistics from the University of Chicago. Dr. Karmakar has served on editorial boards of journals like Statistical Papers and Sankhya A , and has received grants from NSF and AMS Simons, totaling over $200,000. His work spans theoretical contributions in posterior consistency, time-varying models, and applied research in cyberbullying analysis and climate change. Education B.Stat, Indian Statistical Institute (Kolkata) M.Stat, Indian Statistical Institute (Kolkata) PhD in Statistics, University of Chicago Research Interests Dr. Karmakar’s research explores dependence structures in data, including time series (stationary/non-stationary), econometric models (GARCH/ARCH), neural networks, and spatial/spatiotemporal systems. His work emphasizes robust statistical methods, such as change-point detection, posterior consistency, and model-free predictions. Recent projects include analyzing cryptocurrency volatility, climate risks in financial markets, and cyberbullying trends during the pandemic. Grants & Awards NSF DMS Grant (ATD Program: $200,000, 2021-2024) AMS Simons Travel Grant (2021-2024) Early Career Advisory Board Member, Journal of Multivariate Analysis Teaching He has taught courses at the University of Florida (Probability, Time-Series Forecasting) and University of Chicago (Statistical Methods, Probability Theory). He was nominated as Best Tutor in Statistics at the University of Chicago (2014-2016). Labs & Collaborations Dr. Karmakar collaborates with researchers in epidemiology, cybersecurity, and climate science. His lab focuses on developing algorithms for large geospatial datasets and analyzing complex systems like cryptocurrency networks and disease propagation.

Daniel Freyمشاهده پروفایل

استاد

• Robust Design
• Design of Experiments
• Adaptive Experimentation
• +۳ مورد دیگر

Professor Daniel Frey is a Professor and current Area Head for Design and Manufacturing in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT). With over 25 years at MIT, he leads research in robust design methodologies and experimental techniques while maintaining deep engagement with D-Lab's mission to develop technologies for underserved communities worldwide. His work bridges theoretical engineering principles with practical applications in global development contexts. Education: B.Sc. in Aeronautical Engineering from Rensselaer Polytechnic Institute (1987) M.Sc. in Mechanical Engineering from University of Colorado at Boulder (1993) Ph.D. in Mechanical Engineering from MIT (1997) Frey's research centers on robust design and adaptive experimentation methodologies, particularly his pioneering work on one-factor-at-a-time adaptive experimentation. He extends these principles to develop medical devices and sustainable technologies for resource-constrained environments, including vaccine coolers that prevent temperature-related vaccine spoilage and Surgibox systems for maintaining sterile surgical conditions in austere settings. His D-Lab leadership (2016-2022) advanced projects in off-grid energy, mobile health technology, and agricultural innovation while emphasizing community-driven design approaches. Analysis of his recent publications reveals consistent focus on adaptive experimental frameworks that optimize design processes under uncertainty, with growing emphasis on real-world implementation challenges in developing contexts. His work increasingly integrates statistical learning techniques with traditional engineering design while maintaining core contributions to robust parameter design theory. Scientific Awards: R&D 100 Award for Software for Virtual Machining (1997) R&D 100 Award for Leveraged Freedom Chair (2010) NSF CAREER Award (2005) ASME Fellow (2013) SAE Charles M. Manly Memorial Medal (2020) Multiple teaching awards including Junior Bose Award (2006) and Teaching with Digital Technology Award (2022) 12+ best paper awards across engineering design and quality control conferences Frey actively mentors students through D-Lab projects and MIT's Electric Vehicle Team, having supervised over 10 D-Lab initiatives including the vaccine cooler development with graduate student Prithvi Sundar. As faculty advisor for USAID's $30M Comprehensive Initiative on Technology Evaluation (CITE), he secured substantial funding for technology assessment in developing countries. His SUTD-MIT International Design Center collaboration generated field-tested experimental frameworks for developing world applications, while BLOSSOMS educational videos reach global high school classrooms. Through MIT D-Lab, Frey co-developed technologies including biomass cookstoves, off-grid energy systems, and mobility devices like the Leveraged Freedom Wheelchair (R&D 100 winner). His current research focuses on Surgibox implementation and vaccine logistics, working directly with communities to ensure solutions address actual needs while respecting local innovation capacities. As Area Head for Design and Manufacturing, he shapes curriculum and research direction for one of MechE's core technical pillars.

Yangjianchen Xuمشاهده پروفایل

استادیار

• Time-to-event Data
• Semiparametric Models
• Survival Analysis
• +۳ مورد دیگر

Yangjianchen Xu is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, within the Faculty of Mathematics. His research focuses on developing novel statistical methods for survival analysis and semiparametric models to address challenges in biomedical and public health sciences, with applications to time-to-event data, EHR analysis, and causal inference. He holds a Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill (2024) and a B.S. in Statistics from Peking University (2019). His work emphasizes methodological advancements in analyzing interval-censored and multivariate time-to-event data, alongside applied research on vaccine efficacy, SARS-CoV-2 infection dynamics, and pandemic response strategies. Xu's research has contributed to understanding booster vaccination impacts, durability of vaccines against Omicron subvariants, and the interplay between prior infection and vaccination in mitigating severe outcomes. Xu's academic affiliations include the Department of Statistics and Actuarial Science at the University of Waterloo, with a focus on statistical theory and applications in health sciences. His research is supported by collaborations with public health institutions and clinical networks, addressing pressing issues in epidemiology and biostatistics.

Maggie Niuمشاهده پروفایل

دانشیار

• Network Models
• Latent Variable Models
• Bayesian Statistics
• +۳ مورد دیگر

Maggie Niu is an Associate Professor of Statistics and Director of the Statistical Consulting Center at the Pennsylvania State University. She leads interdisciplinary research in statistical methodology with applications in health and social sciences. Her work emphasizes Bayesian methods, latent variable models, and network models. She actively collaborates with researchers across disciplines through the Statistical Consulting Center, mentoring graduate students in applied projects. Education: Ph.D. in Statistics from the University of Washington (advisor: Peter Hoff), M.S. in Applied Mathematics from University of Minnesota, B.S. in Applied Mathematics from Peking University. Research focuses on developing statistical models for complex data structures, including network dynamics, latent variables, and multivariate analysis. Applications span public health (e.g., HIV epidemiology), social sciences (e.g., voting behavior modeling), and environmental science. Her recent work includes estimating hidden population sizes (e.g., female sex workers in sub-Saharan Africa) and analyzing disease outbreak detection via exported cases. Service contributions include leadership roles in the ASA Section on Statistical Consulting, editorial work for journals like JASA and AOAS, and founding the Chinese Statistical Consulting and Collaboration League. She is actively involved in advancing statistical methodology through workshops like the Workshop on Statistical Network Analysis and Beyond (SNAB). Labs/Teams: Director of Penn State's Statistical Consulting Center, leading a team of researchers and students in collaborative applied projects. Previously directed the Statistical Consulting Center at Tsinghua University (2018-2019).

David Kahleمشاهده پروفایل

دانشیار

• Statistical Computing
• Data Visualization
• Algebraic Statistics
• +۴ مورد دیگر

David Kahle is an Associate Professor in the Department of Statistical Science at Baylor University. His research focuses on statistical computing, data visualization, algebraic statistics, Bayesian methods, and pharmaceutical statistics. He is known for developing R packages such as ggmap , mpoly , and unmconf , which enhance computational and visualization capabilities in statistical analysis. Dr. Kahle holds a Ph.D. from Rice University (2011) and a B.A. in Mathematics from the University of Richmond (2006). He has authored over 30 peer-reviewed articles and serves as a Research Fellow at Baylor’s Collaborative on Hunger and Poverty. His work spans interdisciplinary collaborations in healthcare policy, environmental science, and data-driven decision-making. His research interests include: Statistical software development and R package creation Bayesian computation and prior elicitation techniques Algebraic approaches to statistical problems Applications in biostatistics and pharmaceutical trials Data visualization methodologies Notable awards include the Mike Kutner Junior Faculty Travel Award (2014–2015) and recognition for outstanding classroom performance (2013). He has secured research funding from institutions like Eli Lilly & Company and the USDA, totaling over $20 million in grants. Dr. Kahle advises graduate students on topics ranging from Bayesian regression to computational algebraic methods. His lab actively contributes to advancing statistical methodologies through open-source software development and collaborative research initiatives.