Rate My Professor Jonathan Fischer

Fischer's research specializes in biostatistics and computational biology, with a focus on developing innovative statistical methods for analyzing complex genomic and transcriptomic data. His notable contributions include robust, annotation-free approaches to alternative splicing detection in large-scale RNA-seq datasets and adaptive non-parametric tests for identifying dependence structures in high-dimensional data. Key publications encompass 'Robust and annotation-free analysis of alternative splicing across thousands of RNA-seq experiments' (eLife, 2022, with Gonzalo Benegas and Yun S. Song), 'Likelihood-based deconvolution of bulk gene expression data using single-cell data' (Nature Biotechnology, 2021, with Dan D. Erdmann-Pham and others), 'Robust and adaptive non-parametric tests for detecting general dependence structures' (Cell Reports Methods, 2025, with Fan Zhou and Yun S. Song), and 'RNA-controlled nucleocytoplasmic shuttling of mRNA decay factors determines the dynamics of the yeast transcriptome' (Nature Communications, 2022). His Google Scholar profile indicates 387 citations, underscoring his influence in statistical genomics. At UF, Fischer teaches essential graduate courses such as PHC 6051 (Introduction to Biostatistical Methods) and PHC 6052, mentors master's students, and serves on the Genetics and Genomics Curriculum Committee. In 2025, he was inducted as a Faculty Member into Delta Omega, the national honorary society for public health students, alumni, and faculty.