Modern technologies have led to an explosion of biomedical transcriptomic data and revolutionized how biologists think about and study biology. Whether we look at the explosion in genome sequencing, high-throughput drug assays, or rapid diagnostic testing, we have more data than ever before.


We focus on utilizing modern techniques like high-throughput sequencing along with computational tools to make sense of vast amounts of data. This takes the form of a blend of biological understanding, computer science, and statistics. Work in our stream is a mix of analysis of raw data, understanding biological context, and the development of statistical and machine learning approaches to better utilize the resulting data.


Big-data-output technologies are more than just essential tools for modern biomedical research. Similar techniques are rapidly becoming integrated with clinical diagnosis and treatments in the field of personalized medicine. The large amount of data we can generate now not only represents an amazing resource for current tools and research but a largely-untapped resource waiting for new techniques or methods to lead to new breakthroughs. 


The boom in bioinformatics and computational biology is deeply connected to progress in both biology and computer science. We need not only inquisitive biologists and clever computer scientists, but we also need people to bridge the two disciplines, to connect breakthroughs in computer science and computing technology with a deep understanding of complex biological problems. 


All students are exposed to a set of core computational biology topics including transcriptomic analysis, evolutionary biology, and machine learning. In the process, students learn basic scripting or programming skills (Python or R) to utilize state-of-the-art analysis toolkits for data processing, alignment, and analysis. Some students analyze genuine experimental data sets developing an understanding of how these large-scale experiments connect back to core biological concepts and conclusions and how to communicate those conclusions visually and verbally. Other students may choose to develop new analytical approaches using large-scale databases, statistics, or deep learning to understand how organisms behave and evolve.