Academic Programs in Computational Biology
Why study computational biology at Carnegie Mellon University?
We have a unique approach to computational biology -
- Our educational programs graduate over 50 excellent students per year who come to CMU from around the world.
- Students in all of our degree programs are expected to complete a challenging rotation of courses from the Carnegie Mellon School of Computer Science in addition to acquiring a rigorous biological grounding.
- Our graduates have gone on to work in top pharmaceutical, data analysis, and bioinformatics firms.
You can learn about our department’s education and research vision at our About Us page.
Ph.D. in Computational Biology
The Joint CMU-Pitt Ph.D. Program in Computational Biology (CPCB) provides interdisciplinary training in using quantitative and computational approaches to tackle scientific questions that lie at the interface of the life, physical, engineering, and computer sciences. CPCB trainees are taught and mentored by leading experts at two of the foremost computer science and biomedical research institutions in the world.
M.S. in Computational Biology
The MSCB program seeks to train the world’s best Computational Biologists at the Master’s level. The curriculum provides both breadth and depth of training in Computational Biology and is built on a solid foundation of Biology, Computer Science, Statistics, and Machine Learning (Data Sciences). Interested students are also given opportunities to pursue research. Our graduates are prepared for rewarding jobs in industry or to pursue their doctoral degrees at top universities.
M.S. in Automated Science
The Masters of Science in Automated Science: Biological Experimentation (MSAS) program trains practitioners in the design, implementation, and application of laboratory automation and artificial intelligence in scientific research. Students train with world-class faculty, including those from the top-ranked School of Computer Science. Graduates become leaders in the emerging paradigm of Automated Science – the combination of robotic scientific instruments, Machine Learning, and Artificial Intelligence for iteratively building predictive models from experimental data and selecting new experiments to improve them. Students have opportunities to do research projects with Carnegie Mellon Faculty, to do summer internships with relevant companies, and to do collaborative projects with industrial sponsors.
Our graduates are prepared for rewarding jobs in industry or to pursue their doctoral degrees at top universities.
Undergraduate Program in Computational Biology
Students completing the BS in Computational Biology will be ideally prepared for Ph.D. programs in any of a range of biomedical areas, including Computational Biology, Systems Biology, or Quantitative Biology. Students who choose to complete pre-med requirements will be very well-prepared to attend medical school; the next generation of physicians will need to better understand the computational approaches needed for automated medical testing, automated medical imaging, and the revolution in personalized medicine.
Additionally, undergraduate students can pursue the Concentration in Computational Biology (for School of Computer Science undergraduates) or the Minor in Computational Biology (for undergraduates outside of the School of Computer Science)
Pre-College Program in Computational Biology
Launched in 2019, the three-week Pre-College Program in Computational Biology is the first and only computational biology educational program in the United States designed for high school students.
In the program, our students (most of whom will be rising high school seniors) learn both the computational and laboratory skills needed in modern biology. Traditionally, these skills have been taught as part of disjoint courses, but our pre-college program highlights the vital interplay between generating biological datasets in the lab and analyzing these datasets computationally. On a typical day, students spend half the day in a wet lab, and the other half of the day programming algorithms to analyze biological data, including the data that they generate!