Overview
Our research spans a wide spectrum of statistical and analytical domains. We aim to push the boundaries of how data is collected, interpreted, and applied across disciplines. Our primary focus areas include:
Statistical Theory & Methodology
Applied Data Analysis
Machine Learning & Artificial Intelligence
Predictive Modeling & Forecasting
Data Ethics & Policy
Our multidisciplinary approach connects data science with real-world application, driving evidence-based decision-making in government, healthcare, business, and beyond.
Research Divisions
âExplore our core research divisions and the cutting-edge projects they lead:
Data Science and Machine Learning
This division develops and applies advanced algorithms and machine learning models for high-dimensional and complex datasets. Areas of focus include:
Supervised and unsupervised learning methods
Natural language processing and text mining
Large-scale data infrastructure and optimization
Responsible and interpretable AI
Current Projects:
Bias Detection in Algorithmic Decision-Making
AI-Driven Climate Impact Modeling
Deep Learning Frameworks for Medical Imaging Analysis
Public Policy and Statistics
We support government agencies and nonprofits in using data to craft policies that are equitable, efficient, and evidence-based. This division bridges statistical rigor with policy relevance.
Survey design and sampling strategies
Demographic modeling and projections
Policy simulation and impact assessment
Socioeconomic data analysis
Current Projects:
Predictive Analytics for Urban Resource Allocation
Measuring Policy Outcomes in Early Childhood Education
Statistical Equity Metrics for Public Services
Health and Medicine Data
This division applies statistical methods to biomedical research, clinical trials, and public health studies, supporting advancements in population health and personalized medicine.
Biostatistical modeling
Longitudinal and survival analysis
Epidemiological data evaluation
Health disparities and access metrics
Current Projects:
COVID-19 Long-Term Impact Tracking Dashboard
Predictive Modeling for Hospital Readmission Rates
Geospatial Mapping of Health Inequities
Climate and Environmental Data Analytics
We apply advanced statistical modeling and machine learning to environmental and climate data to support sustainability, disaster preparedness, and public health. This division works at the intersection of data science and environmental stewardship.
Climate risk forecasting and early warning systems
Environmental exposure and public health modeling
Carbon emissions tracking and data dashboards
Geospatial analytics for land and water resource management
Current Projects:
Real-Time Wildfire Risk Modeling for Western States
Urban Heat Island Mapping and Equity Assessment
Climate Data Integration for Regional Sustainability Planning
Education Analytics and Learning Outcomes
We use statistical tools to examine education systems, assess learning outcomes, and inform evidence-based education policy. This division partners with schools, government agencies, and education technology firms.
Student performance prediction and intervention modeling
Equity analysis in standardized testing and outcomes
Longitudinal education data integration and visualization
Quantitative evaluation of teaching strategies and curricula
Current Projects:
Postsecondary Success Forecasting for First-Generation Students
Data-Driven Evaluation of Remote Learning in Kâ12
District-Level Analytics for Resource Allocation Equity
Featured Publications
Our findings are regularly published in peer-reviewed journals, conference proceedings, and research reports. We believe in open access to knowledge and strive to make our work publicly available whenever possible.
Equity-Aware Statistical Models for Urban Planning
Predictive Power of Ensemble Models in Health Outcome Forecasting
A Framework for Interpretable Machine Learning in Public Policy
Bayesian Approaches to High-Dimensional Forecasting
