Utkarsh Dang

Dr. Utkarsh Dang is an Associate Professor at Carleton University with a cross-appointment in the Department of Health Sciences. A biostatistician and data scientist by training, he leads an interdisciplinary program that fuses advanced statistical methodology with pressing clinical questions in neuromuscular disease, precision medicine, and health-outcomes research.

Education

• PhD – University of Guelph

Research Interests

Dr. Dang’s scholarship is organized around four synergistic pillars:

• Health-outcomes & precision medicine: Quantifying phenotypic, genotypic, and treatment-response variability in Duchenne and Becker muscular dystrophy.
• Clinical-trial innovation: Design and analysis of Phase II/III trials (notably the vamorolone program) with focus on biomarker-integrated endpoints.
• Statistical learning & clustering: Development of novel mixture-model and change-point methodologies for high-dimensional biological data.
• Bioinformatics & phylogenetics: Algorithmic advances for evolutionary tree inference and large-scale omics integration.

Recent Research Trajectory (2020-2025)

Across 40+ publications, Dr. Dang has concentrated on translational studies in Duchenne muscular dystrophy, dissecting therapeutic effects of dissociative steroids (vamorolone), gene-therapy interactions, and robust biomarker signatures derived from 1,018-plex serum proteomics. Methodologically, his group has delivered open-source R packages (mixSPE, markophylo) that extend multivariate count-data modeling and phylogenetic Markov-chain Monte Carlo techniques.

Funding & Scientific Awards

Current research is continuously funded by competitive grants including:

• Natural Sciences and Engineering Research Council of Canada (NSERC)
• U.S. National Institutes of Health (NIH)
• U.S. Department of Defense (DOD)
• Foundation to Eradicate Duchenne

Student & Team Mentorship

Dr. Dang welcomes graduate trainees (Master’s & PhD) and post-doctoral fellows with interests in biostatistics, clinical-trial analytics, and computational biology. His lab integrates expertise from statistics, health sciences, and computational biology to tackle high-impact problems in precision neurology.