Naim Rashid, PhD

About

Dr. Naim Rashid is an associate professor in the Department of Biostatistics with a joint appointment at the Lineberger Comprehensive Cancer Center. 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. 

Dr. Rashid also engages in collaborative studies at Lineberger, 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 reviews novel clinical trials in breast cancer with oncologists nationwide.  

Each spring, Dr. Rashid also teaches BIOS 735, a doctoral-level course covering topics such as writing efficient and reproducible code, implementing various optimization and numerical integration algorithms, and applying multiple machine learning methods.

Naim Rashid in the Gillings News

Honors and Awards

Delta Omega Faculty Award
2021, University of North Carolina at Chapel Hill

IBM and R.J. Reynolds Junior Faculty Development Award
2017, University of North Carolina at Chapel Hill

Barry H. Margolin Dissertation Award (for best doctoral dissertation completed in 2013)
2013, University of North Carolina at Chapel Hill

Training Grant recipient
2006-2011, Genomics and Cancer

Representative Courses

Intermediate Linear Models (BIOS 663), Spring 2016-2018

Statistical Computing (BIOS 735), Spring 2019-2022

Research Activities

Precision medicine, high throughput epigenomics (ChIP-seq, ATAC-seq,etc.), high-throughput transcriptomics (RNA-seq) proteomics, pancreatic cancer, breast cancer, multi-study learning, missing data methods in deep learning, model-based clustering, scalable mixed models

Service Activities

Department Service:

2017- Genomics Joint Group Meeting (organizer), Department of Biostatistics
2016- Computing Committee, Department of Biostatistics
2016- Masters Examinations Applications Subcommittee, Department of Biostatistics
2015- Protocol Review Committee, Lineberger Comprehensive Cancer Center
2015- Doctoral Examinations Applications Subcommittee, Department of Biostatistics (chair since 2021)

University Service:

2017- Faculty Council, Gillings School of Global Public Health Representative

Organizer for:

Invited Session in 2019 ENAR meeting on Replicability in Big Data Precision Medicine 

Member of:

Translational Breast Cancer Research Consortium, Statistical Working Group

ENAR 

American Statistical Association 

Key Publications

High-Dimensional Precision Medicine From Patient-Derived Xenografts. Rashid, N.U., Luckett, D. J., Chen, J., Lawson, M. T., Wang, L., Zhang, Y., Laber, E. B., Liu, Y., Yeh, J. J., Zeng, D., et al. (2020). Journal of the American Statistical Association , 116(535), 1140-1154.

Modeling Between-Study Heterogeneity for Improved Replicability in Gene Signature Selection and Clinical Prediction. Rashid NU, Li Q, Yeh JJ, Ibrahim JG (2019). Journal of the American Statistical Association.

Purity Independent Subtyping of Tumors (PurIST), A Clinically Robust, Single-sample Classifier for Tumor Subtyping in Pancreatic Cancer. Rashid NU, Peng XL, Jin C, Moffitt RA, Volmar KE, Belt BA, Panni RZ, Nywening TM, Herrera SG, Moore KJ, Hennessey SG (2020). Clinical Cancer Research, 26(1), 82-92.

Mammalian period represses and derepresses transcription by displacing clock–bmal1 from promoters in a cryptochrome dependent manner. Y.-Y. Chiou, Y. Yang, N.U. Rashid, R. Ye, C. P. Selby, and A. Sancar (2016). Proceedings of the National Academy of Sciences.

Some statistical strategies for DAE-seq data analysis: variable selection and modeling dependencies among observations. N.U. Rashid, W. Sun, and J.G. Ibrahim (2014). Journal of the American Statistical Association, 109(505), 78–94.

Virtual microdissection identifies distinct tumor and stroma specific subtypes of pancreatic ductal adenocarcinoma. R. A. Moffitt, R. Marayati, E. L. Flate, K. E. Volmar, S. G. H. Loeza, K. A. Hoadley, N.U. Rashid, L. A. Williams, S. C. Eaton, A. H. Chung, et al.  (2015). Nature genetics, 47(10), 1068.

Staff/Administrative Duties

Associate Director - Biostatistics for Research in Genomics and Cancer T32

Education