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Department of Biostatistics

Research and Practice

Research Working Groups

Research in the Department of Biostatistics is organized into the following Working Groups comprised of faculty, postdoctoral fellows, and students. Groups meet regularly in a variety of intellectual meeting formats including research-in-progress sessions, journal club, topical seminars and working discussions. These span population modeling methodologies, “big data” methodologies and applications in both statistical genomics and advanced research technologies such as neuroimaging and wearable computing, causal inference, and the department’s major application areas of environmental health and epidemiology and aging. Faculty and students disseminate their work through publications, software, blogs, and other avenues. For additional information, please read more about our research areas and visit the websites of each of our Working Groups listed below.

Our research is characterized by a commitment to statistical science, its foundations and methods, and the application of statistical science to the solution of public health and biomedical problems. Research that occurs at the interface of quantitative reasoning and important public health and biomedical questions is particularly potent. We are fortunate to have the opportunity to build our research efforts on the foundation of first-rate biomedical discoveries made here at Johns Hopkins.

Small Area Estimation and Spatial Statistics (SAESS)

Bayesian Learning & Spatio-temporal modeling (BLAST)

Bayesian and spatio-temporal models provide principled approaches to dealing with complex structures underlying modern large-scale data, but yet major methodological and computational challenges remain in their practical deployment. The BLAST group explores ideas and innovations necessary to meet these challenges.

Learn more about BLAST.

Causal Inference Working Group

Causal Inference

This group is comprised of a multi-disciplinary group of students and faculty from Johns Hopkins University, who are interested in the application and development of statistical methods for drawing causal inferences about intervention effects from partially-controlled studies, or from randomized controlled trials with complications such as non-compliance or missing data. 

Learn more about Causal Inference.

Genomics Working Group

Genomics

The Center for Computational Biology (CCB) is a multidisciplinary center dedicated to research on genomics, genetics, DNA sequencing technology, and computational methods for DNA and RNA sequence analysis.

Learn more about Genomics.

Environmental Epidemiology and Biostatistics

Environmental Epidemiology & Biostatistics

The Environmental Biostatistics Working Group meets regularly to discuss statistical and scientific research problems in the area of the environment and health. It is an interdisciplinary group of researchers from around the University in areas including environmental health, medicine, atmospheric modeling, epidemiology, and biostatistics.  

Epidemiology and Biostatistics of Aging Working Group

Epidemiology & Biostatistics of Aging

The Epidemiology and Biostatistics of Aging training program prepares pre-doctoral and postdoctoral fellows in the methodology and conduct of significant clinical- and population-based research in older adults.

Learn more about the Epidemiology and Biostatistics of Aging Training Program.

Survival, Longitudinal, and Multivariate Data (SLAM) Working Group

Survival, Longitudinal & Multivariate Data (SLAM)

This working group is a forum for discussion of state of the art research in Survival, Longitudinal And Multivariate data (SLAM). The working group was established in 1997 with a different name and was renamed in 2005 as “SLAM” to emphasize research on survival, longitudinal & multivariate data and statistical inference. 

Learn more about SLAM.

Statistical Methods and Applications for Research in Technology (SMART) Working Group

Statistical Methods & Applications for Research in Technology (SMART)

The SMART group works on Statistically principled methods for new technologies with special emphasis on brain imaging (e.g. fMRI, high resolution MRI, CT), wearable computing (e.g. hip accelerometers, heart monitors), and Biosignals (e.g. EEG, EKG, ECoG). The underlying principle is to develop methods that are automated, fast, scalable, and robust (AFSR.) Our analytic approaches are sometimes focused only on one subject, but typically we are investigating large populations observed at one or multiple time points. 

Learn more about SMART.

Pain Data Science

Understanding the mechanisms underlying the transition to chronic pain is key to mitigating the dual epidemics of chronic pain and opioid use in the U.S. As part of the NIH funded Acute to Chronic Pain Signatures (A2CPS) Consortium, we have established a Data Integration and Resource Center (DIRC) at Johns Hopkins University and collaborating institutions that works to integrate imaging, omics, behavioral, and clinical measures to develop biosignatures for the transition to chronic pain.

Learn more about Pain Data Science.

Brain

Functional Neuroimaging

The functional neuroimaging group is a multi-disciplinary group of students and faculty from Johns Hopkins University that work on developing new methods for using neuroimaging technology to measure brain function.

Contact Martin A. Lindquist for more information.