If it has cool math and an impactful question, I am interested. Based on my combined medical and statistical training, I tend to gravitate to problems in the analysis of biomedical data; especially, genomics and electronic health data. However, my research interests are varied and include both theoretical and applied aspects of mathematics and statistics.
Electronic Health / Personalized Medicine
- Statistical and machine learning methods for interpreting imperfect diagnostic tests
- Syndromic surveillance for emerging infectious diseases
- How can we infer changes in patient care seeking behavior during a pandemic?
- Statistical methods for the analysis of sequence count data (e.g., 16S microbiome surveys, bulk RNA-seq, and single-cell RNA-seq)
- Differential expression and correlation analysis when working with count compositional data. What kinds of assumptions allow these problems to be identifiable?
- What do zeros in sequence count data represent and how do we deal with them?
- Batch effects and PCR amplification bias
Statistics and Machine Learning
- Efficient and accurate methods for inferring high-dimensional Bayesian models
- Non-Gaussian, non-linear time-series analysis
- Compositional time-series
- Bayesian analysis of partially identified models
- Bayesian decision theory
- Optimal control and sequential and active learning
- Can symmetries in probabilistic models be found computationally and exploited for faster inference?
- When is there a closed form transformation between two families of probability distributions?
- How can you find a transformation that maps one family of probability models into another family (assuming the two families are topologically equivalent)?
- Gaussian process with asymmetric kernels
- How can you identify families of probability models with identical marginal distributions.
- Statistical Methods for Party Planning
Justin D. Silverman is an Assistant Professor of Information Science and Technology, Statistics, and Medicine at Penn State.
Silverman received his Ph.D. in Computational Biology and Bioinformatics from Duke University in 2019. He received his M.D. from Duke University in 2020. He received his B.S. in Physics and Biophysics in 2011 from Johns Hopkins University.
Based on his combined medical and statistical training, Silverman tends to gravitate to problems in the analysis of biomedical data; especially, genomics and electronic health data. However, his research interests are varied and include both theoretical and applied aspects of mathematics and statistics.
Honors and Awards