Carl Kingsford, Ph.D.

Research Description

Carl directs a computational biology group in the Computational Biology Department in the School of Computer Science at Carnegie Mellon University. His group works on creating efficient computational methods to enable petabase-scale genomics, which is the analysis of hundreds of thousands of genome sequencing experiments, in order to help researchers make use of these vast data collections. Recently, for example, his group has developed novel algorithms, data structures, and software for fast measurement of gene expression and for fast search of thousands of sequencing experiments. He typically explores graph and optimization approaches to create these algorithms.

His work allows genomic analyses — such as the prediction of gene function — to be completed more accurately, more quickly, and with fewer computational resources. Due to their speed, his algorithms enable much larger experiments to be performed and analyzed and many more researchers to make use of huge collections of sequencing data. This speed will only become more important as sequencing becomes even more commonplace.

Carl is very active in the open-source software community, and he is the author of many open-source, widely used genomics software packages. He is the recipient of an Alfred P. Sloan Research Fellowship in computational and evolutionary molecular biology and a National Science Foundation CAREER Award. His work is also supported by other grants from the National Science Foundation and the National Institutes of Health. Carl holds a PhD from Princeton University and also trained at Duke University.

More information:

Google Scholar Profile

Lab Group on GitHub

related links

Data-Driven Discovery Science Carnegie Mellon University, School of Computer Science Back