SUDHANSHU SHEKHAR
Statistical Geneticist | Computational Biologist | Cell and Molecular Biologist
Postdoctoral Research Associate
Hyejung Won Lab,
University of North Carolina at Chapel Hill, NC
Visiting Researcher
Mary-Ellen Lynall Lab
University of Cambridge, UK
Welcome! I am Sudhanshu Shekhar, a Scientist specializing in Computational and Systems Biology. I am committed to advancing personalized healthcare by bridging computational biology, statistics, and experimental biology. Currently a Postdoctoral Research Associate at UNC-Chapel Hill and a Visiting Researcher at the University of Cambridge, I focus on unraveling the genetic and environmental factors driving neuronal health and complex neuropsychiatric disorders.
With over a decade of hands-on experience in interdisciplinary research, I bring a unique blend of computational expertise and molecular biology proficiency, enabling me to tackle scientific challenges from multiple angles. My background includes molecular biology, alternative splicing, and the study of circular RNAs (circRNAs), equipping me to explore complex gene regulatory mechanisms. This expertise is paired with advanced skills in computational biology and statistical genetics, supported by practical benchwork capabilities across genetics, cell biology, and biochemistry. Together, these skills make me a valuable asset in cross-functional research teams focused on scientific breakthroughs.
My research journey began with an early passion for discovery in my undergraduate and master’s studies, leading me to Purdue University’s Interdisciplinary Life Science (PULSe) program. There, I immersed myself in Genomics, Computational Biology, and RNA Biology, culminating in a Ph.D. in Computational and Systems Biology with Dr. Peristera Paschou. My research aims to uncover novel gene-disease associations and understand regulatory mechanisms through tools like GWAS, TWAS, eQTL, and pQTL. I’m proficient in R and Python programming and leverage high-performance computing clusters (HPC) to analyze large-scale multi-omic datasets.
Currently, my work integrates alternative splicing, circRNA, and MPRA analyses to elucidate the regulatory roles of genetic variants in health and disease. This focus on molecular and computational precision enhances my contributions to understanding gene-environment interactions, such as the influence of endocrine-disrupting chemicals on neuronal health.
My experience also encompasses inclusive mentorship and STEM outreach to underserved communities, where I aim to inspire future scientists. I look forward to fostering a research-driven, inclusive environment that encourages all students to excel in molecular biology and bioinformatics.
