Merlise Clyde is Professor and Chair of Statistical Science at Duke University. Her research focuses on Bayesian model uncertainty, variable selection, and applications in genomics and environmental science. She develops R packages like BAS for Bayesian modeling. Research: Bayesian model averaging, non-parametric regression, and high-dimensional data analysis with applications in proteomics and health sciences. Awards: Fellow, Institute of Mathematical Statistics (2020) Zellner Medal (2016) Distinguished Service Award (2016)
Rebecca Carter Steorts is an Associate Professor of Statistical Science and Computer Science at Duke University, affiliated with the Trinity College of Arts & Sciences, Biostatistics and Bioinformatics, and the Social Science Research Institute. She directs research in entity resolution, Bayesian methods, and privacy-preserving data analysis with applications to human rights conflicts and healthcare. Her research group develops scalable statistical methods for record linkage, microclustering models, and privacy-preserving data release. Current work focuses on distributed Bayesian entity resolution and inference methods for linked data. Publications demonstrate consistent focus on entity resolution methodologies. Recent work emphasizes distributed computing frameworks, microclustering models, and applications in healthcare and conflict studies. Theoretical contributions include performance bounds and uncertainty quantification in data linkage. NSF CAREER Award (2017) Elected Fellow, International Statistical Institute (2019) Duke Graduate Mentoring Award (2021) Leonard J. Savage Thesis Award Honorable Mention (2013)
César A. Uribe is the Louis Owen Assistant Professor in the Department of Electrical and Computer Engineering at Rice University, part of the George R. Brown School of Engineering. He also serves as a Visiting Professor at the Moscow Institute of Physics and Technology (MIPT). His research focuses on distributed optimization, decentralized control, algorithm analysis, and computational optimal transport, with an emphasis on fundamental limits of distributed optimization and scalable algorithms for networked systems. Uribe holds a BSc in Electronic Engineering from Universidad de Antioquia (2010), MSc in Systems and Control from Delft University of Technology (2013), an MSc in Applied Mathematics from the University of Illinois at Urbana-Champaign (2016), and a PhD in Electrical and Computer Engineering from UIUC (2018). He was a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS) before joining Rice in 2021. His research explores distributed learning algorithms, non-asymptotic analysis of social learning, and optimal transport theory. Key themes include geometric convergence rates in distributed inference, resilient optimization under adversarial conditions, and applications in networked systems such as epidemics and control systems. Uribe has received numerous awards, including the 2020 INFORMS DEI Ambassadors Program Award and the 2019 Yahoo! FREP Award. His work spans over 50 peer-reviewed publications and includes collaborations on distributed algorithms for machine learning, signal processing, and control theory. He actively mentors students in PhD and postdoctoral programs, emphasizing diversity and inclusion in STEM. Current research initiatives include optimal transport methods for network regression, competitive virus spread models over hypergraphs, and PID-based neural network architectures for adaptive control.
Lauren Stadler is an Associate Professor of Civil and Environmental Engineering at Rice University and a Research Faculty member of the NSF-funded NEWT Nanosystems Engineering Research Center. Her research focuses on wastewater-based epidemiology , environmental antibiotic resistance , and wastewater treatment innovation , with applications to public health protection and sustainable water systems. Education: Ph.D. in Environmental Engineering, University of Michigan (2016) M.S.E. in Environmental Engineering, University of Michigan (2012) B.S. in Engineering, Swarthmore College (2006) Research Interests: Stadler’s work integrates environmental microbiology , synthetic biology , and process engineering to address challenges in wastewater surveillance, antibiotic resistance mitigation, and microbial community dynamics. Notable projects include: Developing high-sensitivity assays for SARS-CoV-2 detection in wastewater Tracking viral variants and antibiotic resistance genes in environmental systems Advancing horizontal gene transfer research in soil microbial communities Recent Article Trends: Her recent work emphasizes wastewater-based epidemiology for infectious disease monitoring, including Bayesian modeling of SARS-CoV-2 dynamics and multiplexed pathogen detection systems. She also explores synthetic biology tools to engineer microbial communities for environmental remediation. Awards: 2022 CDC National Wastewater Surveillance System Center of Excellence Award 2019 Gulf Research Program Early Career Fellow 2016 CH2M/AEESP Outstanding Doctoral Dissertation Award Lab & Team: The Stadler Research Group includes PhD students, MS students, and postdoctoral researchers working on wastewater surveillance, microbial ecology, and synthetic biology. Key collaborations involve the Houston Health Department, CDC, and international institutions.
Dr. Joshua Alley is an Assistant Professor in the School of Politics and International Relations at University College Dublin (UCD), and a core member of the Connected_Politics Lab. His research focuses on international relations, particularly alliance politics, the political economy of security, and civil conflict. He holds a PhD from Texas A&M University (2020) and a BA from Gettysburg College (2015), with postdoctoral experience at the University of Virginia. His research investigates how alliances influence military spending, democratic foreign policy dynamics, and public opinion on political violence. Key works include analyses of U.S. military alliances' financial impacts, elite influence on public attitudes, and the effectiveness of nuclear threats. His teaching includes modules on international relations, security, and research methods. Alley’s work appears in journals like International Studies Quarterly , Journal of Conflict Resolution , and Security Studies . He actively contributes to public discourse through media outlets like The Irish Independent and maintains a GitHub repository for open-access research replication.
Dafne Zorzetto is a Postdoctoral Research Associate in the Data Science Institute at Brown University, collaborating with Roberta De Vito. She holds a PhD in Statistics from the University of Padova and has conducted research at Harvard University's Department of Biostatistics under Francesca Dominici. Her work focuses on Bayesian Nonparametric methods and Causal Inference, with applications in environmental epidemiology and public health. Education: PhD in Statistics, University of Padova (2020–2023) MSc in Statistical Sciences, University of Padova (2018–2020) BSc in Statistics for Economics and Business, University of Padova (2015–2018) Her research interests include Bayesian Nonparametric models, causal inference methodologies, and their applications in environmental health. She has developed novel approaches for addressing unmeasured confounding using negative controls and characterizing heterogeneous causal effects through dependent Dirichlet mixtures. She has presented her work at conferences such as the ISBA World Meeting and the New England Statistics Symposium. Her contributions include organizing academic events like 'Explain like I’m an Undergrad' to foster statistical communication among early-career researchers. Awards: Young researcher travel award (2022 ISBA World Meeting) Zorzetto mentors students across institutions, including Harvard College and Bocconi University, focusing on statistical research and methodology.
Andrea Hupman is an Associate Professor in the Department of Supply Chain & Analytics at the University of Missouri–St. Louis's College of Business Administration. She teaches business analytics, decision analysis, and predictive modeling, earning the 2017 Gitner Excellence in Teaching Award. Research Focus: Hupman develops decision-analytic frameworks for operations management, including risk-averse classification algorithms, drone logistics optimization, and behavioral modeling of supply chain decisions. Her work integrates predictive analytics with economic and psychological insights. Awards: IEEE Systems Journal Best Paper Award (2019) William A. Chittenden Award (2016) INFORMS New Faculty Colloquium Participant (2015) Doctoral Advising: Has supervised PhD dissertations on vaccine supply chain optimization, software effort estimation, and maintenance scheduling.
Alfonso J. Martinez is an Assistant Professor of Psychology at Fordham University's Department of Psychology. His research focuses on advanced statistical methodologies in psychometrics, particularly Bayesian approaches, latent variable modeling, and diagnostic classification systems. He contributes to improving measurement techniques in educational and psychological assessments through innovative model development and validation. His work addresses challenges in personality assessment, rapid guessing behavior in testing, and the application of structural equation modeling (SEM) across diverse contexts. He actively publishes in high-impact journals, with a focus on methodological advancements and their practical implications for assessment design. While no specific academic awards are listed, his prolific publication record reflects dedication to advancing statistical methodologies in psychology. His teaching and professional affiliations include involvement with academic organizations central to psychometric research and educational measurement.
Shan Huang is a Professor in the Department of Statistics at the University of South Carolina , College of Arts and Sciences. His research focuses on measurement error modeling, latent variables, and nonparametric statistics, with a particular emphasis on methodologies for non-Gaussian data and robust inference. Research Interests include: Developing inferential methods for data with measurement errors Model misspecification and diagnostic techniques Nonparametric approaches for mean, mode, and density estimation Analysis of heavy-tailed, directional, and compositional data Recent Publications highlight advancements in Bayesian networks, modal regression with mixture distributions, and directional data modeling. His work addresses challenges in group testing data, coarsened data, and error-prone nodes. Teaching includes courses such as Advanced Statistical Inference, Latent Variable Models, and Nonlinear Statistical Models. His students have contributed to multiple publications marked with asterisks.
Hanamori Skoblow is a Research Fellow at the Center for Family Policy and Research, University of Missouri, with a PhD in Human Development and Family Science. Her research examines biopsychosocial factors in aging, including social ties, cognitive functioning, and caregiving dynamics across the life course. Education : PhD in Human Development & Family Science, University of Missouri (2024) MS in Human Development & Family Science, University of Missouri (2021) BA in Psychology, Beloit College (2014) Dr. Skoblow's work integrates quantitative methods to investigate how early-life socioeconomic factors, relational dynamics, and self-perceptions of aging influence late-life health outcomes. Her research employs dyadic modeling, meta-analytic techniques, and longitudinal datasets to uncover mechanisms linking psychosocial resources to cognitive resilience. Her publications explore diverse aspects of gerontology including caregiver stress allocation, couples' health behaviors, inflammation pathways, and socioeconomic determinants of cognitive aging. Methodological strengths include structural equation modeling and meta-regression. Awards and Honors : P.E.O. Scholar Award (2023) Gerontological Society of America Policy Intern (2022) Marilyn Coleman Outstanding Graduate Student Scholarship University Medal (supervised student, 2023) Dr. Skoblow mentors students in the Families in Later Life Lab and collaborates on NIH-funded projects examining social determinants of health. She maintains affiliations with the Gerontological Society of America and International Association for Relationship Research.
Professor Dot Dumuid holds the position of Enterprise Fellow at the University of South Australia's Alliance for Research in Exercise, Nutrition and Activity (ARENA), located at City East Campus. Her research develops novel analytical models to optimize 24-hour time allocation across sleep, screen time, and physical activity for holistic health improvement. Research focuses on: Compositional data analysis frameworks for time-use epidemiology Multidimensional health impacts of movement behaviors Algorithmic optimization of daily activity patterns Her publication corpus demonstrates consistent focus on physical behavior compositions and their health implications, predominantly using longitudinal datasets and machine learning approaches. Recent methodological innovations include Bayesian multilevel compositional modeling. Honors include: SA Cardiovascular Research Network Excellence Award (2024) Women's Research Excellence Award (2024) National Heart Foundation Innovation Award (2018) She leads international collaborations as President of the Compositional Data Association and Treasurer of the International Network of Time-Use Epidemiologists.
Dr. Pedro Quintana-Ascencio is a Professor at the University of Central Florida's Department of Biological Sciences. His research explores disturbance and spatial structure in plant communities, emphasizing fire ecology, rare species conservation, and demographic modeling in Florida's Lake Wales Ridge ecosystems. He integrates long-term data collection, field experiments, and modeling to study population viability and restoration strategies. His recent publications focus on fire-dependent species management, amphibian responses to prescribed burns, and statistical approaches for ecological data. Recurring themes include demographic modeling of endangered plants, climate-change impacts on disturbance regimes, and wetland restoration in agricultural landscapes. He directs research on slash-and-burn agriculture in Mexican tropical forests and grazing-fire interactions in Florida wetlands, providing insights into colonization dynamics and ecosystem recovery.
Professor Sandeep Singh is an Assistant Professor in the Mechanical, Aerospace and Nuclear Engineering Department at Rensselaer Polytechnic Institute (RPI). He joined RPI in 2022 after completing his Ph.D. at Texas A&M University, where his research focused on manifold-based trajectory optimization for space missions. Prior to his doctoral studies, he worked at the Indian Space Research Organization (ISRO) as a scientist and program manager for the ASTROSAT mission's mechanical design. His research interests include astrodynamics, low-thrust trajectory design, autonomous navigation, and space mission design leveraging manifold theory and machine learning. Education: Ph.D., Texas A&M University, 2022 B. Tech., Indian Institute of Space Science and Technology, 2013 Research Interests: Optimal low-thrust trajectory design Manifold theory applications in space mission planning Autonomous navigation using CNN frameworks and probabilistic regression Orbit maintenance and exploration in celestial mechanics Spacecraft guidance and control systems Scientific Recognition: John V. Breakwell Student Award (American Astronautical Society) Multiple university fellowships during doctoral studies Professional Roles: RPI Representative to Universities Space Research Association Member of the AIAA Astrodynamics Technical Committee Referee for international journals/conferences Labs/Teams: Active involvement with the Aerospace Systems and Controls Lab (ASCLAB) at RPI.
Balgobin Nandram is a Professor of Statistics at the Department of Mathematical Sciences , Worcester Polytechnic Institute (WPI). With a PhD in Statistics from the University of Iowa (1989) and a Master's from Imperial College London (1981), his career spans academic leadership, global research collaborations, and methodological innovation. BS, Mathematics & Physics, University of Guyana (1977) BA, Mathematics Education, University of Guyana (1979) MS, Statistics, Imperial College London (1981) PhD, Statistics, University of Iowa (1989) His research focuses on Bayesian statistics applied to survey methodology, small area estimation, categorical data analysis, and nonignorable missing data. He has developed computational methods for health statistics and data science, with significant applications at the National Center for Health Statistics (NCHS) and agricultural surveys. While his 2010-2002 refereed publications highlight Bayesian hierarchical models for BMI data and COPD mortality mapping, his invited presentations (2018-2015) emphasize logistic regression in small areas, multinomial count analysis, and projective inference. These works bridge theoretical advances with practical applications in public health and survey research. 2003 : Fellow, American Statistical Association (ASA) 2006 : SPAIG Award (WPI-NCHS partnership) 2014 : Visiting Global Scholar, Kyungpook National University 2004 : Sigma Xi membership As an advisor, he has mentored PhD students at WPI, the University of the Philippines, and Kyungpook National University, though specific advisees are not named. His grants include CDC collaborations on health monitoring and NCHS research fellowships. He leads international research teams in South Korea, the Philippines, and India, including partnerships with Yonsei University, DLSU, and Banaras Hindu University. His work at NCHS (1999-2000) and subsequent sabbatical (2003/2004) solidified his role in health statistics.
Mohamedou Ould Haye is an Associate Professor in the School of Mathematics and Statistics at Carleton University. He holds a PhD in Mathematics and Statistics from Université Lille 1, France (2001), followed by postdoctoral research at Lille and HEC Montreal. He joined Carleton in 2003 as an Assistant Professor and was promoted to Associate Professor in 2007. His research focuses on Stochastic Processes , Time Series Analysis , Limit Theorems , and Long Memory Processes , with applications to forecasting and dependence modeling. He has contributed extensively to methodologies in seasonal long-memory data , nonstationarity detection , and empirical process analysis . His publications span over two decades, addressing topics such as frequency-domain testing for long-range dependence , confidence interval estimation for linear processes , and robust regression techniques . He teaches advanced courses in time series analysis, stochastic processes, and statistical theory. While no formal advisees are listed, his teaching portfolio includes courses like Statistical Methods for Business and Probability Theory . His work bridges theoretical advancements with practical statistical challenges in time series and dependent data analysis.
