Dr. Yardimci develops machine learning and statistical methods to better understand chromatin organization in cancerous and healthy cells. He aims to build models that improve our understanding of chromatin organization in regulating essential processes like transcriptional regulation and methods that will enable detection of aberration of chromatin organization in cancerous cells. In building such methods, he leverages a variety of population and single-cell genomics assays and high resolution microscopy images. Our side interests involve developing computational methods that aid experimental design of high throughput genomics assays, and assessing the quality and reproducibility of datasets from such experiments.

Trained as a computer scientist, Dr. Yardımcı obtained his Ph.D. from Duke University's Computational Biology program. He received his postdoctoral training from the Genome Sciences Department at the University of Washington. During his training, he has been heavily involved in NIH ENCODE consortium and 4D Nucleome project and published papers for analyzing and modeling a variety of genomics data for garnering biological insights into transcriptional regulation.