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Aziz Khan is a staff scientist at the Stanford Cancer Institute, where he develops reproducible pipelines and machine learning methods for integrative analysis of multi-omics data at bulk and single-cell resolution to understand tumor evolution and chromatin regulatory dynamics of tumor growth.Aziz completed his PhD in Bioinformatics at Tsinghua University, China in 2016 followed by a three year postdoctoral training at the University of Oslo, Norway. During PhD and Postdoc his primary research emphasis was on regulatory genomics and epigenomics. He developed computational methods, tools, and resources to understand the (epi)genomic control of gene regulation in development and disease.Apart from research, he is advocating for open science, open-source, preprints, and reproducibility in research. He is a contributor for Bioconda and also developed several open-source tools and resources such as JASPAR. He is ASAPbio and eLife Community Ambassador and co-founded ECRcentral (ecrcentral.org), a community initiative for early-career researchers.
gene regulation, cancer regulatory genomics and epigenomics, integrative analysis of multi-omics data, machine learning