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          About NIAMS
          Binod Regmi, Ph.D.





Binod Regmi, Ph.D.


Postdoctoral Fellow
    Biodata Mining and Discovery Section Image
              Headshot of a short-haired man wearing a suit, smiling. 
            Contact Information

                                                  301-402-6776
                
                                                9000 Rockville Pike
                  
            50 South Drive
      
                  
            Building 50, Room 1139
      
                                   Bethesda MD 20892
                
                                                binod.regmi@nih.gov 
                Summary
          Binod Regmi, Ph.D., completed his master’s degree in statistics and analytics and his doctoral degree in biological science at the University of Arkansas, Fayetteville. His Ph.D. research project focused on the computational aspect of body shape and genomic variation of species that maintain adaptive diversity.

After completing his Ph.D. in 2019, Dr. Regmi joined the dilated cardiomyopathy working team at the Wexner Medical Center, Ohio State University. In the fall of 2020, he joined the NIAMS at the NIH.
        
                  Research Statement
          
            Dr. Regmi is interested in understanding the individual genomic makeup and epigenetic architecture that can be tailored, as personalized medicine, to achieve more effective treatment of human diseases. His previous research investigated the familial association between rare variants of dilated cardiomyopathy and heart structure and function as measured by cardiac magnetic resonance and electrocardiogram techniques.

In the Biodata Mining and Discovery Section at the NIAMS, Dr. Regmi has participated in several data mining projects related to autoimmune and skin diseases. He has contributed significantly to building bioinformatics tools and pipelines for workflow management, data processing, and visualization. He has written an R package called richPathR which is a valuable tool for mining and visualizing large numbers of gene sets. Recently, under the supervision of Dr. Hong-Wei Sun and Dr. Stephen Brooks, he has focused on building tools and algorithms related to statistical learning. These help researchers extract patterns from large genomic, transcriptomic, and epigenetic data. 

            Education
              
            **University of Arkansas**
Ph.D., Biology (2019)
M.Sc., Statistics and Analytics (2018)

**University of Louisiana Lafayette**
M.Sc., Biology (2012)             
                        Experience
              
            **Wexner Medical Center, Ohio State University**
Postdoctoral Researcher (2019-2020)


Last Updated: February 2023


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