DescriptionDuring the past decade, the rapid advancement of high-throughput technologies has reshaped modern biomedical research by vastly extending the diversity, richness, and availability of data across genetics, genomics, medical imaging, and health records. This data deluge presents both enormous opportunities and challenges that require a high degree of computational expertise, which has not been traditionally prioritized in biological pedagogy or research. Therefore, this moment presents a unique opportunity for computer scientists to make scientific discoveries with true biomedical significance. This progress may manifest through primary analysis of basic and clinical datasets, secondary analysis of burgeoning public resources on millions of patients and phenotypes, or development of novel machine learning algorithms for diagnosis or precision treatment recommendations. We will particularly focus on ways we bring our computational training to bear on applied biomedical problems. For concreteness, we will describe several specialized skills for computational biology, including familiarly with key bioinformatic tools, data analysis, visualization, and large-scale computing systems. We will describe how we closely collaborate with domain experts in laboratory technology, basic biology, and clinical practice in order to maximize our impact on human health. We will also highlight several opportunities for undergraduate research experimental in biomedical data science. Finally, we will discuss our respective transitions from purely computational backgrounds to our current positions as computational biologists, highlighting a non-traditional pathway towards biology that will only grow in prominence and impact.