Associate Head, Department of Computer Science
Fellow of the ISCB (International Society for Computational Biology), 2017
I am a computer scientist, data scientist,
and perhaps even a statistician.
I work on algorithmic problems in computational biology with the aim of developing methods that biologists will use and that will have transformative accuracy and scalability. Part of this work involves mathematics (to understand the theoretical guarantees of the methods I develop, and of other methods), but part of it is also empirical (to understand performance on data). So implementation and testing is very important. All of my methods are a combination of graph algorithms and machine learning or statistical learning. My work in machine learning in particular involves the development of novel ensemble methods, using phylogenetic estimation to guide the design of the ensemble. The machine learning I do is largely unsupervised or semi-supervised learning, largely because there is very limited reliable labeled data in my field; as a result, I do not work in deep learning. Mathematical proofs are part of what I do, but my focus on empirical performance (on data, in other words) drives my research.
My current work is on
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 several allocations.
I also very much like collaborating with biologists, and have
worked with the Avian Phylogenomics Project and the
Thousand Plant Transcriptome project, among others.
I am seeking new grad students available: 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 historical linguistics. Strong programming skills, mathematical intuition, and interest in collaboration are necessary. If you are interested in working with me, you should take my graduate course CS 581: Algorithmic Genomic Biology which I will teach in Fall 2020.
Interested in working with me?
Postdoc positions at UIUC Computer Science. These are flexible postdocs that can be used with anyone in the CS department. If you want to teach, then these positions will be funded 50% by the department and 50% by the research faculty mentor. In exchange for departmental funding, these postdocs will teach 1 course per year, based on department needs and the candidate's interest; if the candidate wants to teach more, they will have the opportunity to do so.
Computational Phylogenetics: An introduction to designing methods for phylogeny estimation, published by Cambridge University Press (and available for purchase at Amazon and as an E-book at Google Play). Errata are posted as I find them. The image of the Monterey Cypress is there because of the NSF-funded CIPRES project, whose purpose was to develop the methods and computational infrastructure to improve large-scale phylogeny estimation. Why I wrote this book.
I dedicated the book to
my PhD advisor
who died in 1994; see
(published in the Journal of Computational Biology,
10 Jun 2009)
that I co-authored
with Dan Gusfield, David Shmoys, and Jan Karel Lenstra
Bioinformatics and Phylogenetics:
Seminal Contributions of Bernard Moret, published by
This book is a
Festschrift for Bernard Moret,
who retired from EPFL in December 2016.
The book contains a collection of self-contained chapters
that can be used for an advanced course in
computational biology and bioinformatics.
"Plus de détails, plus de détails, disait-il à son fils, il n'y a d'originalité et de vérité que dans les détails..." -- Stendhal, Lucien Leuwen (a quote much loved by my stepfather, Martin J. Klein, and an essential guide for all scholarship).
Elegant swimwear and other clothing (from Amaio)