Founder Professor of Computer Science
Institute for Genomic Biology, Biocomplexity Theme
National Center for Supercomputing Applications
Affiliate in the departments of Electrical and Computer
Engineering, Mathematics, Statistics, Animal
Biology, Entomology, and Plant Biology
PhD (Mathematics) University of California at Berkeley, 1991
B.S. (Mathematics) University of California at Berkeley, 1984
Fellow of the ISCB (International Society for Computational Biology), 2017
Fellow of the ACM (Association for Computing Machinery), 2016:
For contributions to mathematical theory, algorithms, and software for large-scale molecular phylogenetics and historical linguistics
Research Positions Available:
If you are an undergraduate seeking a research position,
please see this page.
I have openings in my group for graduate
students (PhD or MS)
to work on developing computational methods for
large-scale multiple sequence alignment,
phylogeny estimation, metagenomics, and even
skills, mathematical intuition, and interest in collaboration
If you are already a graduate student at UIUC, please contact me
If you are interested in applying to UIUC for graduate
school and would like to work with me,
please read this first and then contact me.
Unfortunately, I do not have any funding available for postdoctoral
researchers. However, if you have your own source of
funds and have published
papers directly related to my research, I'll be
glad to talk with you about working together.
My research combines
mathematics, computer science,
statistics, in order to develop
algorithms with improved accuracy for
large-scale and complex estimation problems in
phylogenomics (genome-scale phylogeny estimation),
multiple sequence alignment,
I am a big fan of
Blue Waters, and have benefitted from two allocations.
Click here for the 2017 annual report for my Blue Waters allocation
on algorithms for big data phylogenomics, proteomics, and metagenomics.
Click here for more
about my research.
Spring 2018, CS 581: Algorithmic Computational Genomics
CS 581 is a course on applied algorithms, focusing on the use of discrete mathematics, graph theory, probability theory, statistics, machine learning, and simulations, to design and analyze algorithms for phylogeny (evolutionary tree) estimation, multiple sequence alignment, genome-scale phylogenetics, genome assembly and annotation, and metagenomics. Each of these biological problems is important and unsolved, so that new methods are needed. Hence, this course will provide opportunities for computer scientists, mathematicians, and statisticians, to do original and important research that can have an impact on biology. Every year, two or more students from this course have done final projects that were subsequently published in major scientific journals; you can be one of them!
Please see the course website
for more about this course
(I am on the 2017 list of teachers ranked as excellent by their students for the course!).
Current NSF Funding:
My research in
funded by three grants from
the National Science Foundation.
Multiple sequence alignment,
funded by NSF grant ABI-1458652,
beginning August 2015.
This project will develop
new methods for multiple sequence alignment,
building on our SATé, PASTA, and UPP methods.
funded by NSF grant III:AF:1513629.
is a collaborative
grant with the University of
Maryland, for new methods for metagenomic dataset analysis,
building on our TIPP method for
taxon identification of reads in a metagenomic sample.
Graph-Theoretic Algorithms to Improve
funded by NSF grant CCF-1535977.
I am the overall PI, and this
project is collaborative with
Satish Rao (UC Berkeley PI) and Chandra Chekuri (UIUC).
developing new theoretical computer
science and discrete algorithms for
improving the estimation
of large species and gene trees, and specifically enabling
statistical methods to scale to ultra-large datasets.
"Plus de détails, plus de détails, disait-il à son
n'y a d'originalité et de vérité que dans les
-- Stendhal, Lucien Leuwen (a quote much loved by my stepfather,
J. Klein, and an essential guide for all scholarship).
for Google Scholar Citations
(i10-index 134 and h-index 55).