Member, Genomics and Computational Biology Graduate Group

email jon@mcauliffe.com

Academic CV: pdf

My areas of interest include

• bioinformatics and computational biology
• computational statistics
• statistical and probabilistic methods in computer science
• statistical learning theory
• kernel methods
• analysis of large/high-dimensional data sets
• nonparametric and semiparametric estimation
• prediction and optimization problems in finance

Publications and talks

2007

“Machine learning and modern biological data.”
Introductory Overview Lecture, Joint Statistical Meetings, Salt Lake City.
Slides: pdf

2006

Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe.
“Discussion of ‘Support vector machines with applications’.”
Statistical Science 21(3), August 2006: 341-346.
pdf | Statistical Science home

Jon D. McAuliffe, David M. Blei, Michael I. Jordan.
“Nonparametric empirical Bayes for the Dirichlet process mixture model.”
Statistics and Computing 16(1), March 2006: 5-14.
abstract | Statistics and Computing home

Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe.
“Convexity, classification, and risk bounds.”
Journal of the American Statistical Association 101(473), March 2006: 138-156.
abstract | JASA home

2005

Jon D. McAuliffe, Michael I. Jordan, Lior Pachter. “Subtree power analysis and species selection for comparative genomics.”
Proc. of the National Academy of Sciences 102(22), 31 May 2005: 7900-5.
abstract | pdf | supporting information | PNAS home

Prasad Gyaneshwar, Oleg Paliy, Jon McAuliffe, Adriane Jones, Michael I. Jordan, Sydney Kustu. “Lessons from Escherichia coli genes similarly regulated in response to nitrogen and sulfur limitation.”
Proc. of the National Academy of Sciences 102(9), 1 Mar 2005: 3453-8.
abstract | pdf | PNAS home

Prasad Gyaneshwar, Oleg Paliy, Jon McAuliffe, David L. Popham, Michael I. Jordan, Sydney Kustu. “Sulfur and nitrogen limitation in Escherichia coli K-12: specific homeostatic responses.”
Journal of Bacteriology 187(3), Feb 2005: 1074-1090.
abstract | pdf | Journal of Bacteriology home

2004

Jon McAuliffe, Lior Pachter, Michael I. Jordan. “Multiple-sequence functional annotation and the generalized hidden Markov phylogeny.”
Bioinformatics 20(12), 12 Aug 2004: 1850-60.
pdf | Bioinformatics home

Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe. “Discussion of boosting papers.” Annals of Statistics 32(1), Feb 2004: 85-91.
pdf | Annals of Statistics home

Jon McAuliffe, Lior Pachter, Michael I. Jordan. “SHADOWER: A generalized hidden Markov phylogeny for multiple-sequence functional annotation.” Research in Computational Molecular Biology 8 (RECOMB 2004).
Poster abstract: pdf | RECOMB 2004 poster home

2003

Benjamin I. P. Rubinstein, Jon McAuliffe, Simon Cawley, Marimuthu Palaniswami, Kotagiri Ramamohanarao, Terence P. Speed.
“Machine learning in low-level microarray analysis.”
SIGKDD Explorations 5(2), Dec 2003: 130-9.
pdf | SIGKDD Explorations home

Rebecca W. Corbin, Oleg Paliy, Feng Yang, Jeffrey Shabanowitz, Mark Platt, Charles E. Lyons, Jr., Karen Root, Jon McAuliffe, Michael I. Jordan, Sydney Kustu, Eric Soupene, Donald F. Hunt. “Toward a protein profile of Escherichia coli: comparison to its transcription profile.”
Proc. of the National Academy of Sciences 100(16), 5 Aug 2003: 9232-7.
abstract | html | pdf | PNAS home

Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe.
“Large margin classifiers: convex loss, low noise, and convergence rates.”
Advances in Neural Information Processing Systems 16 (NIPS 2003).
pdf | NIPS Proceedings home

Dario Boffelli, Jon McAuliffe, Dmitriy Ovcharenko, Keith D. Lewis, Ivan Ovcharenko, Lior Pachter, Edward M. Rubin. “Phylogenetic shadowing of primate sequences to find functional regions of the human genome.”
Science 299(5611), 28 Feb 2003: 1391-4.
abstract | html | pdf | online supplement | Perspective | Science home

For a year thereafter I investigated statistical recommender systems at Amazon.com; as an example, see Purchase Circle “uniquely popular” community-based item rankings.

I spent the summer before graduate school at Vindigo, researching MDL-based approaches to optimal compression for handheld software as well as graph algorithms for dynamically generated navigational directions.

Savage's approach to research, via Mosteller:

1. As soon as a problem is stated, start right away to solve it. Use simple examples.
2. Keep starting from first principles, explaining again and again what you are trying to do.
3. Believe that this problem can be solved and that you will enjoy working it out.
4. Don't be hampered by the original problem statement. Try other problems in its neighborhood; maybe there's a better problem than yours.
5. Work an hour or so on it frequently.
6. Talk about it; explain it to people.

A few quotes I find so right-on that I will risk the embarrassment of having a list of quotes on my home page.

• Good judgment comes from experience. Experience comes from bad judgment.
  —Jim Horning

• Dealing with failure is easy: work hard to improve. Success is also easy to handle: you've solved the wrong problem. Work hard to improve.
  —Alan J. Perlis

• I have yet to see any problem, however complicated, which, when looked at in the right way, did not become still more complicated.
  —Poul Anderson

• The difference between theory and practice: in theory, there's no difference between theory and practice; in practice, there is.
  —Jan L. A. van de Snepscheut

• The most exciting phrase to hear in science is not “Eureka!” but “That's funny...”
  —Isaac Asimov

• Don't worry about people stealing your ideas. If your ideas are any good, you'll have to ram them down people's throats.
  —Howard Aiken

• The secret to creativity is knowing how to hide your sources.
  —Albert Einstein

• Men never do evil so cheerfully and completely as when they do it from religious conviction.
  —Blaise Pascal

I don't know Web design, but I know someone who knows it.