About Us
Health Analytics Network is a closely knit group of researchers with interdisciplinary expertise. HAN works with different organizations and academic institutions to conduct research, training, and networking. Our focus areas include: Health and Environment, Policy and Systems, Data Fusion and Disaggregation, Predictive Modeling and Analysis. HAN supports consortia projects, workshops and outreach activities, and training events for widespread dissemination.
Vision and Mission
At HAN, our vision is to use the power of data for addressing health and environmental problems based on inter-disciplinary observations and rigorous studies of different sources of vulnerability, heterogeneity and uncertainty.
To achieve our mission, at HAN, we apply some key approaches that include, but are not limited to, the following:
- fusion of data from different sources, which we synthesize together to produce models for broader understanding of complex phenomena
- gain insights into population dynamics of interest by statistical analysis of the heterogeneity among the underlying subpopulations
- generative models to reconstruct stochastic interactions among individuals and groups to predict rare events or emergent behavioral patterns
- modeling of socioeconomic and environmental factors at local "small area" levels to identify and characterize community-specific disparities
- risk communication and systems level preparedness for disasters and extreme events towards resiliency and equity of vulnerable populations
The Laboratory for 'Health and Environmental Equity through Data' (HEED-lab) is a collaborator of HAN.
Areas of expertise:
Public Health Data Science
- Population Heterogeneity Modeling
- Air Pollution
- Carcinogenic Exposures
- Environmental Extreme Events
- Behavioral Risk Estimation
- Public Health Disparities
Precision Bioinformatics
- Prediction of Rare Events
- Human Phenome Analysis
- Single Cell Analysis
- Cancer Informatics
- Multi-omic Integration
Health Policy and Systems
- Healthcare Quality and Safety
- Disasters and Emergencies
- Social and Environmental Determinants of Health
- Polysubstance Use
Computational Statistics
- Data Fusion
- Augmented Reality
- Small Area Estimation
- Non-stationary Spatial Models
- Generative Deep Learning
- Agricultural and Environmental Statistics
- Skew Mixture Models
Platform for Modeling of Structural Phenotypes
structural degeneration in optic neuropathies such as glaucoma is characterized by neuroretinal rim (NRR) thinning of the optic nerve head and other clinical parameters.
Computational Advances in Data Fusion Methods
Data fusion is the process of integrating multiple data sources to produce better inference than that provided by any individual source. The statistical file-matching problem aims to characterize.
Calculating Probabilities of Environmental Extremes
Environmental researchers often encounter the problem of determining the probability of extreme events marked by exceedance of a high threshold of a variable of interest such as rainfall or air pollution.
A new algorithm for small area estimation
While essential for policy-making, it reliable local estimates are difficult to compute from a survey due to the limited sample size of a typical "small area". Drs. Saumyadipta Pyne and Shaina Stacy, and collaborators.
Team
Dr. Saumyadipta Pyne
Founder and President
Dr. Meghana Desai
Co-Founder and Vice President
Dr. Saurav Guha
Research Associate
Dr. Sumanta Ray
Research Associate
Dr. Vishal Deo
Research Associate