Carey E. Priebe

Carey E. Priebe
cv , bio , links to refereed journal publications

Department of Applied Mathematics and Statistics
Whiting School of Engineering
Johns Hopkins University
Baltimore, MD 21218-2682

Johns Hopkins University Center for Imaging Science

Johns Hopkins University Human Language Technology Center of Excellence

--- Whitehead Hall Room 201
--- Clark Hall Room 301D

Statistical Theory I (550.630) Fall 2014 --- Be sure to get the 2007 "Updated Printing" version of the textbook!

"[GH Hardy] also, notoriously, wanted to keep mathematics pure, whereas I believe that the uses, 'the applications', are as important as the theorems proved. Neither proof nor application is, however, as clear and distinct an idea as might be hoped." -- Ian Hacking, Why Is There Philosophy of Mathematics At All?

"[...] statistics is not just a branch of mathematics. It is an inductive method, defined by its applications to the sciences and other areas of human endeavor, where we try to glean information from data." -- RJ Little, JASA 2013 (Fisher Lecture)

"The wealth of your practical experience with sane and interesting problems
will give to mathematics a new direction and a new impetus."
--- Leopold Kronecker to Hermann von Helmholtz

2014 NSF BRAIN EAGER: Discovery and characterization of neural circuitry from behavior, connectivity patterns and activity patterns NSF HUB Mapping poster
Carey E. Priebe named 2013 Erskine Fellow ...
Carey E. Priebe named recipient of 2011 McDonald Award for Excellence in Mentoring and Advising
Carey E. Priebe named recipient of 2010 ASA SDNS Distinguished Achievement Award
Carey E. Priebe named 2008 National Security Science and Engineering Faculty Fellow:
Fusion and Inference from Multiple and Massive Disparate Data SourcesNSSEFF Press ReleaseWashington PostBaltimore Sunabstract (pdf)
Carey E. Priebe named recipient of 2008 Pond Award for Excellence in Teaching

Research Interests:

• computational statistics, kernel and mixture estimates, statistical pattern recognition, statistical image analysis, dimensionality reduction, model selection,
• statistical inference for high-dimensional and graph data.

Upcoming Conferences & Workshops
Current & Former Graduate Students