Working Groups
Biostatistics Working Groups
There is a wide variety of research, practice, and educational activities happening in the Department of Biostatistics. Some of those activities are 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, and seminars.
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. Working Groups also focus on key issues of statistical practice and education, providing a forum for discussion among individuals with expertise in and passion for those areas.
For additional information, please read more about our research areas and visit the websites of our Working Groups listed below.
Bayesian Learning & Spatio-Temporal modeling (BLAST)
Bayesian and spatio-temporal models provide principled approaches to dealing with complex structures underlying modern large-scale data, yet major methodological and computational challenges remain in their practical deployment. The Bayesian Learning & Spatio-Temporal Modeling (BLAST) Working Group explores ideas and innovations necessary to meet these challenges. Application areas of interest include, but are not limited to, precision medicine, environmental health, disease epidemiology, genetics, healthcare analytics, etc.
For further information and/or to sign up for our mailing list, visit our website and contact group leaders.
Causal Inference
The Causal Inference Working Group is comprised of a multi-disciplinary group of students, postdoctoral fellows, 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.
We host open group meetings bi-monthly, with speakers.
Epidemiology & Biostatistics of Aging (EBA)
The bi-weekly Epidemiology & Biostatistics of Aging (EBA) Research-in-Progress meetings involve trainees, faculty, and others in the Johns Hopkins Schools of Public Health, Medicine, and Nursing interested in the power of quantitative thinking to improve health outcomes in aging. The meetings serve to develop trainees’ critical thinking and oral communication skills, as well as knowledge of issues integral to aging research and career development.
Genomics Data Science
The Genomic Data Science Working Group welcomes all students, postdoctoral fellows, and faculty who are interested in learning more about areas of research in Genomic Data Science. Our working group is designed to discuss emerging research topics related to genomic data science and statistical genomics. It is a mix of presentations from students and postdocs along with external speakers. We aim to provide a welcoming, intellectually stimulating, and inclusive experience for everyone.
The meeting schedule is posted in the #working_group_genomics channel in the JHU Biostat Slack workspace.
Resources for Improving Statistical Education (RISE)
The Resources for Improving Statistical Education (RISE) working group is dedicated to fostering excellence and innovation in statistical pedagogy within the Department of Biostatistics. RISE provides a collaborative forum for faculty, students, and staff to explore best teaching practices and emerging technologies that enhance learning in biostatistics.
RISE meetings are held monthly and open to all members of the Department. Each session features a brief presentation on a relevant topic, followed by a facilitated discussion aimed at sharing insights and practical solutions. Topics of interest include:
- Leveraging AI for teaching and learning
- Designing effective assessments
- Training teaching assistants as educators
- Developing inclusive and impactful course content
Statistical Practice & Research Collaboration (SPARC)
The Statistical Practice and Research Collaboration (SPARC) Working Group serves as a learning and exchange hub in this area for our students, postdoctoral fellows, and faculty, as well as statisticians from other Johns Hopkins and Bloomberg School of Public Health departments and centers.
SPARC aims to harness the extensive knowledge and experience of our faculty and collaborators to improve our statistical practices and research collaborations, and to prepare our students for a successful career in academia, government, or industry.
Survival, Longitudinal And Multivariate data (SLAM)
The Survival, Longitudinal And Multivariate data (SLAM) Working Group is a forum for discussion of state of the art research in survival, longitudinal, and multivariate data, and statistical inference.
Established in 1997 under a different name, SLAM activities usually take the following formats: seminar presentations; short courses offered by faculty members; student project presentations.
Statistical Methods & Applications for Research in Technology (SMART)
The Statistical Methods & Applications for Research in Technology (SMART) working 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.
Wearable and Implantable Technology (WIT)
The Wearable & Implantable Technology (WIT) Working Group is focused on adapting existing methods when possible and developing new methods when necessary to analyze data generated by wearable sensors used in health studies. Such sensors produce very large, high-resolution, and high-heterogeneity data that capture a variety of health-related characteristics including objectively measured physical activity, blood glucose, and blood pressure. Such data are increasingly integrated with ecological momentary assessment (fast recall-based information obtained from digital apps) and are changing both the scientific and biostatistics research environments.
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.
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.
Statistical Genetics
The mission of the Statistical Genetics Working Group (SGWG) is to build a forum for discussions and collaborations among Johns Hopkins researchers who are interested in cutting edge developments in methodological and theoretical statistics, and related computations, motivated from modern large-scale genetic studies. The group currently meets every alternative week and invites a mix of internal and external speakers for seminar presentations. SGWG also holds join meetings with other working groups in the Biostatistics Department on topics of mutual interest.