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Buddika PeirisView profile

Associate Professor

• Order Restricted Inference
• Meta-Analysis
• Bayesian Statistics
• +3 more

Buddika Peiris is an Associate Professor of Teaching in the Department of Mathematical Sciences at Worcester Polytechnic Institute (WPI), where he also serves as the Coordinator of the Applied Statistics Master's Program and the Statistics Consulting Lab. His academic career at WPI has progressed from Postdoctoral Fellow (2014-2016) to Assistant Teaching Professor (2016-2021) and now to Associate Professor of Teaching (2021-present). BS in Mathematics from University of Sri Jayewardenepura (2005) MS in Mathematical Statistics from Southern Illinois University, Carbondale (2010) PhD in Mathematical Statistics from Southern Illinois University, Carbondale (2014) Dr. Peiris's research focuses on developing new statistical methodologies with applications across various fields. His primary research areas include Order Restricted Inference, Meta-Analysis, Bayesian Statistics, and Actuarial Science. His work addresses complex problems in public health, weather forecasting, Food Science, and various industries where traditional 'ad hoc' methods are reaching their limits. His teaching philosophy emphasizes clear communication of statistical concepts, exposing students to statistical analysis structures, and teaching effective communication of statistical results to diverse audiences. His publication record demonstrates consistent contributions to statistical methodology, particularly in constrained regression models, meta-analysis techniques, and Bayesian approaches. His work spans theoretical developments with practical applications in biomedical research, environmental studies, and industrial settings. The publications show a progression from foundational work on order restricted inference to more complex applications involving circular-linear regression and meta-analysis of cylindrical time series data. As an educator, Dr. Peiris has supervised numerous graduate students through WPI's Master's program, with projects spanning healthcare analytics, financial applications, environmental modeling, and industrial statistics. His teaching portfolio includes both undergraduate and graduate courses in probability, mathematical statistics, regression analysis, experimental design, and specialized topics in statistical methodology. Through his role as Coordinator of the Statistics Consulting Lab, he facilitates connections between statistical expertise and real-world problems across disciplines. His current research continues to develop constrained prediction intervals, diagnostic tests in regression, and applications of statistical methodology to forensic analysis and plant science.

Abdul A. HusseinView profile

Professor

• Sequential analysis
• Survival analysis
• Finite mixtures
• +3 more

Dr. Abdul A. Hussein is a Professor in the Department of Mathematics and Statistics at the University of Windsor's Faculty of Science. He holds a Ph.D. from the University of Alberta. His research focuses on sequential analysis, survival analysis, finite mixtures, statistical process control, and health outcomes research. He has supervised multiple master’s students and is actively seeking Ph.D. candidates with expertise in probability theory and stochastic processes. His work spans theoretical and applied statistics, including contributions to nonparametric methods, robust estimation, and clinical trial design. Notable collaborations include studies on pediatric safety interventions and medical device reliability. He has secured research grants supporting projects in sequential analysis and health outcomes. His publications emphasize methodological advancements in statistical testing and applications to biomedicine. Dr. Hussein teaches advanced courses and mentors students in statistical theory and computational methods.

Nagaraj K. NeerchalView profile

Professor

• Time Series Analysis
• Statistical Computing
• Environmental Statistics
• +3 more

Nagaraj K. Neerchal is a Professor of Statistics at the University of Maryland Baltimore County (UMBC), Department of Mathematics and Statistics, where he has held roles including Chair since 2006 and former Interim Chair (1999-2000). He earned his Ph.D. in Statistics from Iowa State University in 1986, following B.S. and M.S. degrees from the Indian Statistical Institute. His research focuses on modeling correlated data, particularly in time series analysis and overdispersed categorical data, with applications in environmental, transportation, agricultural, and biomechanical engineering. He is also active in statistical computing and parallel computing methodologies. Neerchal has led interdisciplinary initiatives like the Math Gym and contributed to the Center for Interdisciplinary Consulting and Research (CIRC), fostering collaborative research and education. He has been recognized with the Freeman Hrabowski Innovation Award (2013), the Board of Regents’ Award (2013), and Fellowship in the American Statistical Association (2011). His administrative roles include Graduate Program Director and program development in Applied Statistics. Neerchal has advised numerous Ph.D. and M.S. students, contributing to their success in academia and industry. His grants include NSF funding for high-performance computing and statistical methodology projects. He maintains active involvement in professional societies, including the ASA and IMS, and has served on various editorial and advisory boards.

Davide FerrariView profile

Professor

• Data Integration
• Composite Likelihood Procedures
• Model Selection
• +5 more

Davide Ferrari is a Full Professor in the Faculty of Economics and Management at the Free University of Bozen-Bolzano. He teaches statistical methods and applied statistics at undergraduate and graduate levels, and coordinates the Master's program in Data Analytics for Economics and Management. His academic career includes tenured roles as Assistant Professor at the University of Modena and Associate Professor at the University of Melbourne. Research interests include data integration, composite likelihood procedures, and high-dimensional data inference. He recently explored model selection for intractable likelihoods in econometric and environmental contexts. He actively contributes to digital education innovation through projects like EDUNEXT, focusing on AI integration in teaching methodologies. He holds a PhD from the University of Minnesota's School of Statistics (2008). Office location: BZ E2.05, Piazza Università 1, Bolzano.

Marcus ChambersView profile

Professor

• Econometrics
• Time Series Analysis
• Continuous Time Models
• +2 more

Marcus Chambers is a Professor of Economics at the University of Essex. He joined the faculty in 1989 after completing his PhD at Essex. His research specializes in econometrics, particularly continuous and discrete time models, cointegration, temporal aggregation, and bias reduction methods. He received the Philip Leverhulme Prize (2001-2003) and has held editorial positions at the Journal of Econometrics and Journal of Time Series Analysis. Current research examines locally exact discrete time representations, frequency domain estimation with mixed data, and jackknife methods for near-unit root processes.

Abhimanyu GuptaView profile

Professor

• Econometrics
• Economic History
• Spatial Econometrics
• +3 more

Professor Abhimanyu Gupta is an academic at the University of Essex specializing in econometrics and economic history. His research spans theoretical and applied econometrics, with a focus on spatial econometrics and statistical theory. PhD (London School of Economics, 2013) MSc (London School of Economics, 2008) BA (University of Delhi, 2006) His key research areas include: Spatial Autoregressive Models High-Dimensional Parameter Estimation Credit Market Network Analysis Robust Time Series Inference Recent publications analyze spatial interaction functions, financial networks, and nonparametric prediction methods. Awards include grants from the Leverhulme Trust and British Academy . He supervises PhD students in economics and teaches econometric methods courses.

Jesse WilsonView profile

Assistant Professor

• Biomedical Optics
• Nonlinear and Ultrafast Optics
• In Vivo Multiphoton Histology
• +3 more

Dr. Jesse Wilson is an Assistant Professor of Electrical and Computer Engineering at Colorado State University, affiliated with the Walter Scott, Jr. College of Engineering. His research focuses on leveraging ultrafast and nonlinear optical phenomena for biomedical imaging, particularly in cancer diagnosis and metabolic analysis. Wilson earned his B.S., M.S., and Ph.D. in Electrical and Computer Engineering from Colorado State University, with postdoctoral training at Duke University. B.S. 2004: Electrical and Computer Engineering & Computer Science, Colorado State University M.S. 2007: Electrical and Computer Engineering, Colorado State University Ph.D. 2010: Electrical and Computer Engineering, Colorado State University His research interests include biomedical optics, multiphoton histology, nonlinear and ultrafast optics, and metabolic imaging. Recent work emphasizes label-free imaging techniques, such as transient absorption microscopy and coherent Raman spectroscopy, to study mitochondrial redox states and cancer metabolism. Key advancements include optical scattering robustness in Raman imaging and machine learning-driven image analysis. His articles explore cutting-edge methods like low-frequency coherent Raman imaging, transient absorption microscopy, and hyperspectral unmixing. These studies address challenges in biomedical imaging, such as improving resolution in scattering media and quantifying cellular metabolism non-invasively. Awards: JenLab Young Investigator Award (SPIE), Ruth Kirchstein Fellowship (NCI) Wilson’s work bridges engineering and medicine, with applications in early cancer detection and real-time histopathology. His lab develops novel optical instruments and computational tools for clinical translation, though specific grants or lab facilities are not explicitly detailed in the provided text.

Hadi AbbaszadehpeivastiView profile

Researcher

• Optimization Methods
• Machine Learning
• Convergence Rate Analysis
• +4 more

Hadi Abbaszadehpeivasti is a researcher at the Department of Econometrics and Operations Research within the Tilburg School of Economics and Management at Tilburg University. His work focuses on optimization methods, machine learning, and convergence rate analysis using semidefinite programming techniques. Education: Master’s Degree in Statistics (Normal and Generalized Gamma Estimation), Sabanci University (2019–2020) Master’s Degree in Stochastic Kriging Meta-Modeling for Simulation Optimization, Sharif University of Technology (2012–2014) His research interests include convergence rate analysis of first-order optimization algorithms, performance estimation in non-convex settings, and applications of semidefinite programming to machine learning. He explores conditions like the Polyak–Łojasiewicz inequality for linear convergence and works on saddle-point problems and ADMM variants. Recent publications span convex-concave optimization, gradient descent-ascent methods, and ADMM convergence analysis, with a focus on non-asymptotic guarantees and exact worst-case bounds. His studies often bridge theoretical insights with practical algorithm design. His work also involves collaborations with researchers like Etienne de Klerk and Moslem Zamani, contributing to operations research and computational optimization theory.

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.

Lisa VoigtView profile

Lecturer

• Astrophysics
• Cosmology
• Gravitational Lensing
• +3 more

Dr. Lisa Voigt is a Lecturer in the School of Mathematics, Statistics and Actuarial Science (SMSAS) at the University of Essex since October 2019. Prior to this, she held post-doctoral positions at University College London (2006–2013) and Imperial College London (2006). She holds a PhD in Astrophysics from the University of Cambridge (2004) and an MPhys in Physics from the University of Oxford (2000). Her research focuses on cosmological studies, particularly gravitational lensing and galaxy shape measurements. Key areas include cosmic shear analysis, PSF calibration, and the application of statistical and computational methods to observational astrophysics. Her work addresses systematic biases in lensing measurements and leverages machine learning techniques like CNNs for precision analysis. Publications span 20 years, from foundational studies on galaxy clusters and X-ray gas dynamics to cutting-edge analyses of dark energy using weak lensing. Her articles emphasize algorithm development (e.g., im3shape) and benchmark challenges (GREAT08/GREAT10) to advance cosmological parameter accuracy. No scientific awards or grants are explicitly listed. She teaches within SMSAS and supports academic activities at the Colchester Campus.

Markus ÅdahlView profile

Associate Professor

• Spatial Statistics
• Ambulance Call Forecasting
• Prehospital Care Optimization
• +4 more

Markus Ådahl serves as Associate Professor in the Department of Mathematics and Mathematical Statistics at Umeå University, Sweden. His research focuses on developing advanced statistical methodologies for emergency medical services optimization, with primary office location at MIT-huset, plan 3, Matematik och matematisk statistik, MIT.B.349, Umeå. His work specializes in spatial statistics and machine learning applications for prehospital care systems. Key research areas include spatio-temporal modeling of ambulance demand, composite likelihood intensity estimation, and large-scale forecasting of emergency calls using log-Gaussian Cox processes. This research directly addresses operational challenges in northern Sweden's emergency response infrastructure through data-driven approaches. Recent publications demonstrate consistent focus on ambulance call pattern analysis, with methodological innovations in regularized semi-parametric modeling (2023) and large-scale spatio-temporal forecasting (2020). These studies establish critical frameworks for optimizing ambulance resource allocation in geographically complex regions. Ådahl currently leads the long-term research initiative Data-driven optimization of prehospital care via statistics and machine learning (2018-2028), which integrates statistical modeling with machine learning to enhance emergency medical service efficiency. While no formal advisees are listed in available materials, this project coordinates cross-disciplinary collaboration between statisticians, healthcare providers, and emergency response authorities.

Jonathan D. MahnkenView profile

Professor

• Categorical data analysis
• Statistical consulting
• Analysis of insurance claims data
• +4 more

Jonathan D. Mahnken is a Professor in the Department of Biostatistics & Data Science at The University of Kansas. His research focuses on statistical methodologies with applications in medical research, including categorical data analysis, study design, and survival analysis. He holds a Ph.D. in Biometry from the University of Texas Health Science Center and has extensive experience in clinical trial design and epidemiological studies. Education: B.A. in Mathematics, Concordia University, Seward, NE M.S. in Preventive Medicine, University of Texas Medical Branch, Galveston, TX Ph.D. in Biometry, University of Texas – Health Science Center, Houston, TX His research interests emphasize practical statistical solutions for healthcare challenges, such as optimizing clinical trial designs, analyzing insurance data, and addressing biases in screening programs. He has contributed to studies on stroke recovery algorithms, antihypertensive medication usage in dialysis patients, and smoking cessation interventions. His work frequently integrates survival analysis and categorical data techniques to address real-world medical problems. Notable publications span journals like Annals of Internal Medicine and Journal of Biopharmaceutical Statistics , with a focus on applying statistical rigor to improve patient outcomes and healthcare practices. His awards include the Delta Omega Honorary Society in Public Health and NIH Loan Repayment Program support. Dr. Mahnken is an Accredited Professional Statistician and contributes to initiatives at The University of Kansas Cancer Center. His expertise bridges statistical theory and applied medical research, addressing critical questions in public health and clinical practice.

Haipeng XingView profile

Professor

• Financial Statistics
• Time Series Analysis
• Change-Point Detection
• +2 more

Haipeng Xing is Professor of Applied Mathematics and Statistics at Stony Brook University, specializing in financial statistics and time series analysis. His research develops statistical methods for change-point detection, financial market modeling, and risk analytics with applications in quantitative finance and econometrics. His publications demonstrate expertise in: High-frequency financial data modeling and volatility measurement Structural break detection in credit markets Stochastic control approaches for trading strategies Multivariate GARCH models with regularization Statistical methods for financial risk management He has authored influential textbooks on statistical models for financial markets and time series analysis.

Kalanka JayalathView profile

Associate Professor

• Bayesian Statistics
• Spatial Statistics
• Survival Analysis
• +3 more

Kalanka Jayalath is an Associate Professor and Program Director of Statistics at the University of Houston-Clear Lake (UHCL), within the College of Science and Engineering. He holds a Ph.D. in Statistical Science from Southern Methodist University (SMU), an M.S. in Statistics from Sam Houston State University, and a BSc in Mathematics from the University of Peradeniya, Sri Lanka. His research focuses on Bayesian statistics, spatial statistics, survival analysis, and applications of statistical methods in engineering, archaeology, and sports analytics. Dr. Jayalath has authored books and peer-reviewed articles, including Statistics and Probability with Applications for Engineers and Scientists and contributions to spatial analysis of archaeological sites. His work bridges theoretical statistics with practical applications, such as analyzing cricket performance using machine learning and improving variance homogeneity tests. He has secured grants from the University of Houston-Clear Lake’s Faculty Development Fund (FDF) for research on spatial data modeling and graphical statistical tests. His presentations span international conferences, including the Conference of Texas Statisticians and the International Conference on Statistical Distributions and Applications (ICOSDA). Dr. Jayalath serves on editorial boards for journals like Austin Statistics Journal and reviews for Journal of Statistical Computation and Simulation . His professional affiliations include the American Statistical Association and the International Statistical Engineering Association.

Dongfeng WuView profile

Professor

• Probability Modeling in Cancer Screening
• Bayesian Inference
• Statistical Decision Theory
• +5 more

Dongfeng Wu, PhD is a tenured Professor in the Department of Bioinformatics and Biostatistics at the University of Louisville's School of Public Health and Information Sciences (SPHIS). He holds positions in both academic and research capacities, focusing on probability modeling and statistical inference in cancer screening. His work integrates Bayesian methods, statistical decision theory, and epidemiological analysis to address challenges in medical research. Education: B.S. in Probability and Statistics, Peking University (1986–1990) M.S. in Probability and Statistics, Peking University (1990–1993) M.S. in Computer Science, University of California, Santa Barbara (1997–1999) Ph.D. in Statistics, University of California, Santa Barbara (1995–1999) Research Interests: Dr. Wu specializes in developing statistical methodologies for optimizing cancer screening protocols, including dynamic scheduling algorithms, lead time estimation, and over-diagnosis analysis. His research extends to Bayesian inference, time series analysis, and applications in lung, breast, and colorectal cancer screening. Key contributions include a published book on probability modeling in screening and collaborative work with institutions like the National Lung Screening Trial (NLST). Funding: He leads the NIH/NCI-funded project R15CA242482 (2019–2023), totaling $435,309, focused on dynamic screening scheduling using statistical methods and software development. Teaching: At SPHIS, Dr. Wu teaches advanced courses such as Bayesian Inference, Clinical Trials, Survival Analysis, and Biostatistical Methods. Recent courses include PHST691-Bayesian Inference and PHST683-Survival Analysis. Advising: He has mentored multiple graduate students, including Farhin Rahman (Ph.D. expected 2022), Ruiqi Liu (Ph.D. 2017), and Xiaohong Li (Ph.D. 2017), focusing on topics like parameter estimation in screening and RNA-seq data analysis.