Program

The Interdisciplinary Training Program in Statistical Genetics/Genomics and Computational Biology aims to train the next generation of quantitative genomic scientists to have a strong understanding of, and commitment to, cutting-edge methodological and collaborative research in statistical genetics/genomics and bioinformatics/computational biology with applications in genetic epidemiology, molecular biology and genomic medicine. We are committed to train trainees to become future quantitative leaders to develop and apply advanced, scalable statistical, machine learning (ML) and artificial intelligence (AI) methods to manage, analyze, integrate, and interpret massive genetic and genomic data in basic sciences, epidemiological and clinical data, to advance interdisciplinary research, and to effectively communicate and collaborate with domain-specific genetic and genomic researchers. In this renewal, the Program will enhance training in AI genomics research.

The proposed training program has the following interrelated primary goals:

• To provide high quality training in novel and scalable statistical, ML and AI methods that are necessary to manipulate and analyze massive molecular data, epidemiological, clinical data, and to advance health science research and reproducible research;

• To provide trainees with sufficient depth and breadth of knowledge and practical experience in molecular biology, genetic epidemiology, and clinical genomics through coursework, lab rotations and research projects, so that they can communicate and work effectively with subject-matter experts;

• To help trainees develop the strong methodological and collaborative research skills needed to conduct successful interdisciplinary research by collaborating effectively with biologists, clinical scientists, and public health researchers, identifying important quantitative problems from real world ‘omics research, quantitatively formulating problems, developing analytic methods and interpreting results.
• To help trainees develop strong leadership, communication skills and core competences, engage them in a stimulating, safe, nurturing and interactive environment, and provide them with a range of career development opportunities, e.g., seminars, trainee-run retreats, conferences, and grant-writing workshops.

The training grant directors are Xihong Lin (leading the statistical genetics and genomics element) and Curtis Huttenhower (leading the bioinformatics and computational biology element). 

Stipend and tuition support for this training program is funded through a National Institutes of Health grant (T32 GM74897).