Carey E. Priebe
http://www.ams.jhu.edu/~priebe

Carey E. Priebe
cep@jhu.edu
cv , bio , links to refereed journal publications

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

--- Clark Hall Room 301D

Johns Hopkins University Center for Imaging Science
Johns Hopkins University Human Language Technology Center of Excellence
Johns Hopkins University Department of Electrical and Computer Engineering
Johns Hopkins University Department of Computer Science

* Fall 2017: Statistical Theory (553.730)

Tue & Thu @ 3 @ Gilman 377
TA = Jingyi = jingyi.zhu@jhu.edu = Fri @ 3 @ Hodson 303

* Be sure to get the 2007 "Updated Printing" version of the Bickel & Doksum textbook!

Welcome!
Course Announcement
Syllabus
Grading
* Homework #1 due thursday sep 07

• David Donoho's 50YearsDataScience.pdf ... a useful perspective

"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

* Spring 2018: Topics in Statistical Pattern Recognition (553.735)

HUB Nature
(on sabbatical in england CY 2016: spring = The Alan Turing Institute ; fall = The Isaac Newton Institute)
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