Interdisciplinary training: Statistical Genetics/Genomics and Computational Biology

This is a competing renewal application of the Interdisciplinary Training Program in Statistical Genetics and Genomics and Computational Biology at the Harvard School of Public Health (HSPH). Trainees will be pre-doctoral students in the Departments of Biostatistics and Epidemiology. The Program proposes to support 9 predoctoral students in their G1 and G2 years. This is the only program at HSPH that provides interdisciplinary training in quantitative genomics. The goal of the Program is 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 computational biology with applications in molecular biology, genetic epidemiology, and genomic medicine. We are committed to train trainees to become future quantitative leaders to develop and apply scalable statistical, machine learning (ML) and artificial intelligence (AI) methods to manage, analyze, and interpret massive genetic and genomic data in basic sciences, epidemiological and clinical studies, 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. Trainees receive quantitative training in genomic data science and reproducible research. The training program involves active participation by 31 interdisciplinary faculty members who are recognized scientific leaders, including biostatisticians, computational biologists, genetic epidemiologists, and molecular biologists, and clinical genomicists. It combines elements of training in coursework, lab rotations in statistical genetics and genomics, computational biology and bioscience, directed methodological and collaborative research. It also offers ample career development opportunities in a vibrant and nurturing interdisciplinary environment to help trainees gain skills in scientific communication, teaching, grant and paper writing, teamwork, collaboration, and leadership. Trainees will be provided with extensive individualized mentoring tailored towards their career objectives and are required to develop Individual Development Plans. Trainee progress and mentor-trainee relationships are closely monitored to ensure that those who are struggling can be quickly identified and receive timely support. The Program evaluation involves feedback from current and past trainees, faculty and an advisory committee. We aim to recruit and retain outstanding trainees who will become future leaders in statistical genomics and computational biology. The current average of time to degree is 4.8 years with a 100% completion rate. All graduates of the Program go on to STEM-related careers, of which 52% go on to careers in academia or government, and 48% go on to careers in industry.