The Scientific Core brings together methodological expertise from different fields under one supervisory umbrella to improve communication and to leverage the distributed intellectual resources of HPC associates. The Scientific Core services are in the form of mentorship and consultancy.
Co-Directors of the Scientific Core
- Lingxin Hao , Director of HPC, a sociologist who has extensive experience in interdisciplinary research on social demographic and population health issues with colleagues in different schools within Hopkins and between different institutions, using cutting-edge methodologies.
- Stephane Helleringer, a social demographer with successful interdisciplinary collaborations in research on developing new methods to measure demographic trends in countries with limited vital registration and measuring the effects of social networks on health.
The three methodological arms under the Scientific Core provide mentorships and consultations to HPC associates in the expertise areas below.
The Population-based Methods Arm
Lead: Dr. Mei-Cheng Wang , (Bloomberg School of Public Health) a renowned Biostatistician who has developed numerous new models and methods for truncation, length-bias and prevalent sampling, and life course analysis and has served as principal investigator for multiple NIH R01 grants and Biostatistics Cores of large-scale program projects to develop statistical methods for longitudinal and survival data in health-related studies.
Member: Dr. Elizabeth Ogburn, (Bloomberg School of Public Health) a biostatistician with expertise in the foundations of causal inference for new settings and complex data, network analysis, and environmental health.
The Data-integration Methods Arm
Nilanjan Chatterjee (Bloomberg School of Public Health and School of Medicine) is a big data statistician, having developed new methods for making population-level inferences from multiple data sources, including big data.
Yingyao Hu (Krieger School of Arts and Sciences), an established econometrician, specializes in methods of measurement error and data integration and applies them to population research.
The Data-intensive Methods Arm
Lead: Dr. Tak Igusa, (Whiting School of Engineering) an engineering professor with joint appointments in International Health and Applied Mathematics and Statistics, with a strong track record in collaborative efforts in applying system science methods to public health research, including an NICHD-funded pediatric obesity center.