Departmental Affiliations
Hongkai Ji, PhD, MA, ME, develops data science and statistical methods for analyzing high-throughput and single cell genomic data in order to study gene regulation.
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Research Interests
Big data; Machine learning; Genomics; Computational biology; Bioinformatics; Single cell genomics; Gene expression; Gene regulation; Epigenome; ChIP-seq; RNA-seq; ATAC-seq; DNase-seq; TCR-seq; DNA motif; Transcription factor; Cancer; Immunology; Infectious disease; Development; Statistical modeling; Bayesian methods; Hierarchical models; Data integration; Data mining; Markov Chain Monte Carlo; Computing
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Experiences & Accomplishments
I am interested in developing statistical and computational methods for analyzing big and complex data, particularly high-throughput genomic data. I apply these tools to study gene regulatory programs in development and diseases. My research group develops methods for analyzing genome sequences, transcriptome, regulome, epigenome, and single-cell genomic data. We also develop user-friendly software tools, database and web servers to deliver the state-of-the-art data analysis methods to scientific community. We collaborate with biomedical investigators to apply our tools to decode gene regulatory circuitry in stem cell, cancer and other diseases.