Associate Professor, Biostatistics
UNC Gillings School of Global Public Health
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
Immune system B-cells can help predict HER2-positive breast cancer treatment response
Researchers report specific immune system cells can help them determine whether HER2-positive breast cancer will respond to treatment.
UNC Lineberger pancreatic cancer researchers receive five-year, $4.6 million grants from National Cancer Institute
The NCI awarded $9.3 million in support of two five-year research projects from researchers in the UNC Lineberger Pancreatic Cancer Center of Excellence in partnership with other institutions.
Study shows new machine learning method may lead to optimal cancer treatment decisions
Researchers at the University of North Carolina and North Carolina State University have developed a computational framework to generate evidence-based optimal cancer treatment decisions informed by a patient’s genomic biomarkers.