Dr. Qian (Michelle) Zhou is an Associate Professor of Statistics in the Department of Mathematics and Statistics at Mississippi State University (office Allen 454, phone 662-325-7160). Her research develops advanced statistical methods addressing fundamental questions about model misspecification in clinical and genetic studies. Research interests include: Model diagnosis and selection Risk prediction and biomarker evaluation Survival and longitudinal data analysis Developing robust statistical procedures Her work creates methods for survival analysis, longitudinal data, risk prediction, and model diagnostics that accommodate complications in clinical/genetic studies. Recent publications focus on copula models, survival analysis methods, and agricultural statistics applications. She maintains active research profiles on Google Scholar and MathSciNet. Before joining MSU, Dr. Zhou was an Assistant Professor at Simon Fraser University (2012-2015) and Postdoctoral Fellow at Harvard T.H. Chan School of Public Health (2009-2012). She earned her Ph.D. from University of Waterloo in 2009.
Dr. Brenda Vo is Senior Lecturer in Statistics at UNE's School of Science and Technology, specializing in Bayesian statistical methods for biological and health applications. Her research develops agent-based models for infectious disease dynamics and analyzes agricultural decision-making patterns. Completed PhD at QUT in Computational Bayesian Statistics, developing methods to quantify cell population dynamics. Current projects include ABM approaches for Chlamydia infection modeling, GP distribution simulation in NSW, Q fever epidemiology, and multicultural health interventions. Supervises PhD students in statistics applications to sensor arrays, weed ecology, and migration studies. Collaborates internationally on Vietnam-Australia research initiatives.
David Spade is an Associate Professor in the Department of Mathematical Sciences at the University of Wisconsin-Milwaukee, with office location in the Engineering and Mathematical Sciences building (room E459). He holds active roles as an Undergraduate Advisor and member of the Statistics Research Group. His research centers on theoretical and computational statistics, specializing in Markov chain Monte Carlo methods, Bayesian inference, and phylogenetic analysis. Key interests include convergence diagnostics for Gibbs and Metropolis-Hastings samplers, statistical modeling of biological systems (notably Daphnia motion), and applications in genomics and cancer research. His work bridges rigorous statistical theory with interdisciplinary biological problems. Analysis of his 2016-2025 publications reveals dominant trends in MCMC convergence theory (mixing time, geometric ergodicity, drift-minorization), phylogenetic inference, and biological modeling. His research demonstrates consistent focus on computational statistics with expanding applications in ecology, evolutionary biology, and medical research, particularly through collaborations with biologists. No scientific awards were documented in the provided materials. As an Undergraduate Advisor, he mentors statistics students within the department's academic framework. Dr. Spade actively contributes to the Statistics Research Group, fostering collaborative projects in statistical methodology development and interdisciplinary applications.
Serkan Hosten is a Professor in the Department of Mathematics at San Francisco State University's College of Science and Engineering. His research focuses on Commutative Algebra, Combinatorics, and Algebraic Statistics. He serves as an adviser for the Applied Math BS program and maintains active seminar engagements at UC Berkeley. His research explores the intersection of algebraic geometry with statistical optimization, leveraging combinatorial structures to solve problems in information theory and machine learning. Recent publications demonstrate consistent work in toric geometry, matroid theory, and computational algebra. Publications over the past decade show a strong emphasis on: (1) geometric methods in statistical inference, (2) combinatorial optimization frameworks, (3) algebraic approaches to machine learning, and (4) computational aspects of commutative algebra. This interdisciplinary work bridges pure mathematics with data science applications.
Jessica Bradshaw is an Associate Professor in the Department of Psychology at the University of South Carolina's McCausland College of Arts and Sciences. Her research examines early identification and intervention for autism spectrum disorder (ASD), mapping neurodevelopmental pathways through behavioral, eye-tracking, and physiological methods. Research quantifies the emergence of social behavior, visual attention, and motor skills in infants at risk for ASD, with particular focus on birth to 5 months. Investigations identify pivotal developmental transitions and aberrant pathways leading to ASD, translating findings to early detection protocols and naturalistic developmental interventions. Publications establish neonatal autonomic regulation as a predictor of ASD symptoms, characterize early skill profiles across genetic likelihoods, and develop home-based eye-tracking methodologies. Her work advances understanding of how context and social content shape infant attention during critical developmental windows. Laboratory investigations at the Early Social Development Lab employ multi-method approaches to measure social communication development. Current projects examine motor development correlates of social communication and contextual influences on sustained attention.
Rui Fan is an Assistant Professor in the Department of Economics at Rensselaer Polytechnic Institute, with additional affiliations to the Lally School of Management. His research centers on nonstationary time series analysis applied to economics and finance, including systemic risk assessment, financial forecasting, and causal inference methodologies. He holds a Ph.D. in Economics from the University of Illinois at Urbana-Champaign (2018), an M.S. in Statistics (2015), an M.A. in Economics from Xiamen University (2011), and a B.A. in Economics from Sichuan University (2008). Research Focus: Fan's work spans four key areas: (1) developing systemic risk indicators for financial markets; (2) analyzing the impact of economic shocks (e.g., Fed policy changes, COVID-19) on market stability; (3) advancing instrumental variable estimation techniques; and (4) creating statistical methods for nonlinear nonstationary data common in finance.
Dr. Dawn G. Gregg is a Full Professor and Discipline Director for Information Systems at the CU Denver Business School. She also serves as Director of Assurance of Learning and previously held roles including Associate Dean of Programs and Administrative Director of the Bard Center for Entrepreneurship. She founded Developing Minds Software and holds a PhD in Information Systems from Arizona State University, alongside an MBA and MS in Information Systems. Education : PhD Information Systems, Arizona State University MS Information Systems, Arizona State University MBA, Arizona State University West BS Mechanical Engineering, University of California at Irvine Research Focus : Dr. Gregg explores human-technology interaction, technology adoption, decision support systems, and sustainability in IS. Her work integrates psychology and decision science to study user engagement with digital platforms, including geospatial reasoning and review systems. Current research emphasizes sustainability frameworks in IS research. Article Trends : Her recent work addresses online review dynamics, geospatial decision-making, and digital trust. Key themes include user perception of digital information quality, the impact of demographics on technology use, and the design of effective decision-support tools. Awards : Outstanding Service Award (2018) Excellence in Faculty Mentoring (2018) CU Denver Outstanding Educational Program Award (2010) Dean's Scholar Award (2008) Outstanding Tenure-Track Teacher (2005) Advising & Grants : Dr. Gregg has advised students like Michael Erskine (2007 award winner) and led projects funded through competitions like the Bard Center Business Plan Competition (2008). No specific grants are listed, but her work reflects interdisciplinary collaboration with industry and academia. Labs & Teams : Her research often involves cross-functional teams studying digital platforms, decision-making tools, and educational gaming (e.g., Emerge2Maturity simulation).
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.
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. 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 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 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.
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.
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 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.
