Nonclinical Scholarship Award

Nonclinical Statistics


Student Research Award
APPLICATION DEADLINE: May 1, 2027

We are pleased to announce the Best Nonclinical Student Research Award honoring up to 3 student winners with research funding: $5,000 for first place $3,000 for second place and $2,000 for third place.

 

Eligibility: Students or pre-employment recent graduates with outstanding research impacting nonclinical biostatistics.

Relevant Disciplines: Biostatistics, Statistics, Data Science, Engineering, and associated fields.

Entries: Each submission should include a description of the merits and impact of the work, not exceeding 1,000 words. The description should clearly articulate the significance of the contribution, its relevance to the field, and the extent of its scholarly or practical impact. In addition, each entry should be accompanied by detailed supporting evidence, such as a published paper, an accepted manuscript, a draft paper, a dissertation chapter, an R package, or any other appropriate documentation demonstrating the relevance and quality of the research.

Evaluation Criteria: Key considerations will include the originality of the research question, the significance and relevance of the topic, the clarity of the objectives, and the strength and appropriateness of the methodology.

Application Submission: Applications should be emailed to John Kolassa at kolassa@stat.rutgers.edu

Winners will receive complimentary admission to June 2027 Nonclinical Biostatistics Conference to be held at the University of Pennsylvania and will have the opportunity to present their work to industry professionals.

 

Congratulations to past award recipients! 

  • Yudi Mu, University of California Riverside (2026)
    A Statistical Framework for Cell-Type Proportion Estimation and Inference in Bulk Transcriptomic Studies
  • Seokjun Choi, University of California Santa Cruz (2024)
    Tolerance Interval Construction via Hierarchical Dirichlet Processes

  • Zhili Qiao, Iowa State University (2022)
    Poisson Hurdle Model-Based Method for Clustering Microbiome Features