PhD
Associate Professor, Biostatistics
UNC Gillings School of Global Public Health
UNC-Chapel Hill
Cancer Therapeutics Research Program
Area of Interest
Precision medicine, high throughput epigenomics (ChIP-seq, ATAC-seq,etc.), multi-study learning, gene signature replicability, missing data methods in deep learning, model-based clustering, alternative splicing (RNA-seq), proteomics, pancreatic cancer and breast cancer
Naim Rashid, PhD, engages in collaborative studies at UNC Lineberger Comprehensive Cancer Center, working with physicians and researchers on problems relating to genomics and clinical studies. He also aids in the design of cancer clinical trials at UNC and elsewhere, serving as trial statistician on a number of active protocols. As a member of the Translational Breast Cancer Research Consortium Statistical Working Group, he develops and review novel clinical trials in breast cancer with oncologists nationwide.
His methodological work spans several areas in genomics and statistics, addressing problems facing basic science, translational, and clinical researchers in cancer. Recent areas of research include precision medicine, multi-study replicability, epigenomics, cancer subtyping, and missing data problems in deep learning.
Awards and Honors
- IBM and R.J. Reynolds Junior Faculty Development Award, UNC-Chapel Hill, 2017
- Barry H. Margolin Dissertation Award (for best doctoral dissertation completed in 2013), UNC-Chapel Hill, 2013
- Training Grant recipient, Genomics and Cancer, 2006-2011
News and Stories
A new clinical trial for metastatic breast cancer opens
A new clinical trial that adjusts treatment as breast cancer evolves is now open and enrolling patients at UNC Lineberger Comprehensive Cancer Center.
UNC Lineberger awarded up to $28M to develop an adaptive clinical trial for metastatic breast cancer
UNC Lineberger will lead the development of a next-generation clinical trial for metastatic breast cancer. The new trial design will adapt treatment plans in near real-time by analyzing changes in each patient’s cancer and matching it to the most promising therapy.
