Co-Chief Scientist, the
C3.ai Digital Transformation Institute,
Illinois wiki
Associate Head, Department of Computer Science
Special advisor to the Head of Computer Science
Founder Professor of Computer Science
Member, Bioinformatics and
Computational Biology Group
Member, Carl R. Woese Institute for Genomic Biology
Affiliate, National Center for Supercomputing Applications
Affiliate, Coordinated Sciences Laboratory
Affiliate, Unit for Criticism and Literary Theory
Affiliate, departments of Electrical and Computer
Engineering; Bioengineering; Mathematics; Statistics;
Evolution, Ecology, and Behavior;
Entomology; and Plant Biology.
Fellow of the ISCB (International Society for Computational Biology), 2017
Fellow of the ACM (Association for Computing Machinery), 2015
PhD (Mathematics) University of California at Berkeley, 1991
B.S. (Mathematics) University of California at Berkeley, 1984
biosketch
Wikipedia page
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I stand with the African-American community and
all my friends and colleagues who are outraged
by the killing of George Floyd and other African-American
men and women.
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I contributed to a paper about
1 Billion Years of Green Plant Evolution,
published in
Nature
by the One Thousand Plant Transcriptomes Initiative.
(PDF)
I published a
bibliometrics paper
in
Quantitative Science Studies, published by
MIT press,
in which we identify
model misspecification as a problem for a prior
publication
published in Science.
I will teach
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in Fall 2023.
See this page for information
about applying to the NSF CAREER program.
Research Overview:
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,
metagenomics, and
historical linguistics.
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 2023.
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If you are a graduate student,
please read this first and then contact me.
If you are an undergraduate, please see this page.
If you are looking for a postdoc position, see
this.
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 手机vnp的服务器怎么填,
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
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who died in 1994; see
this memorium
(published in the Journal of Computational Biology,
10 Jun 2009)
that I co-authored
with Dan Gusfield, David Shmoys, and Jan Karel Lenstra
about Gene.
Bioinformatics and Phylogenetics:
Seminal Contributions of Bernard Moret, published by
Springer.
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.
Current Funding:
IIBR Informatics: Advancing Bioinformatics Methods using Ensembles of Profile Hidden Markov Models,
funded by NSF grant 2006069,
beginning August 15, 2023.
This project (joint with Jian Peng) will
extend the theory foundations of ensembles of profile HMMs,
and use them for protein structure and function prediction.
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.
Metagenomics,
funded by NSF grant 1513629.
This
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
Phylogenomic Analyses,
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).
We are
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.
Recent NSF funding has supported
work in phylogenomics, described
here.
This is still an area of very active research in my group.
I also recently benefited from support
of the John P.
Simon Guggenheim Foundation, and
earlier support from
the
David and Lucile Packard Foundation,
the
Radcliffe
Institute for Advanced Study at Harvard University,
the
Program
for Evolutionary Dynamics at Harvard University,
and Microsoft Research, New England.
The Founder Professorship is
funded through the
Grainger Engineering Breakthroughs Initiative, which is supporting development of
research in Big Data and Bioengineering at UIUC.
I am grateful to the
National Science Foundation for its
continuous support since 1994.
See
this page
for completed projects funded by NSF, starting in 2001.
"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).
For prospective students and postdocs
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Teaching
Recent Symposia and Software Schools
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Publications
Complete vita and publication list
手机vpn加速软件 and 手机怎么挂npv
Guidelines for reading and writing scholarly papers
Seminar Talks (2015-present)
My F1000 recommendations
Ethics in science
Academic Integrity
REU in Computational Phylogenetics
Data Science Institutes
Contact info
Elegant swimwear and other clothing
(from Amaio)
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