2010 M.S. in Computational Biology Alumnus
Having worked for a few years and gained plenty of business experience, I didn’t want the generic MBA degree, but rather a quantitative foundation with which I could pursue any business or technical career. The M.S. in Computational Biology program provided me with this opportunity and allowed me to explore this hot research field.
The program solidified my core skills in programming and statistics, and introduced me to the fields of machine learning, bioinformatics, and algorithms.
It was very satisfying to see concepts from my quantitative economics background applied in new and interesting ways. For example, the same Brownian motions equations used for options valuations were applied to chemical reactions. Regression models from econometrics were used instead for handwriting recognition. In one class, I would learn of data mining algorithms used by Google. In another, they would reappear for finding motifs in genomes.
The coursework gave breadth, but I wanted to conduct research for real depth. In the Langmead lab, my research focused on predicting the free energy of binding between proteins and ligands using inference algorithms. This research has very practical applications in drug design. To work appropriately, drugs must bind effectively to their target, so accurate estimates for these affinities are critical in early screens.
The Langmead lab operates at the intersection of biology and machine learning, which few others outside Carnegie Mellon University do. Doing research provided valuable one-on-one time with a professor, and it forced me to quickly get up to speed with the latest publications. It was fascinating working with the lab.
Overall, this program was a great chance to shift my career in a different direction. After completing the program, I plan to pursue work in industry.