Mark V. Culp
Contact
Information
West Virginia University
Department of Statistics
PO Box 6330
Morgantown, WV 26506
Education
Ph.D. Statistics
B.S. Compter Science and Mathematics
Professional
Experience
Associate Professor
Assistant Professor
Non-clinical Statistician
Computer Programmer
Theory and
Methods
Publications
Phone: 304 293-9995
E-mail: [email protected]
Web: http://www.stat.wvu.edu/~mculp/
University of Michigan
Canisius College
Department of Statistics
Department of Statistics
2007
2001
West Virginia University
West Virginia University
Pfizer
SAIC
2013-present
2007-2013
2004-2005
2000-2002
11. Culp M., Ryan K. “On Joint Harmonic Functions and Their Supervised Connections.” Journal
of Machine Learning Research. 14:3721–3752, 2013.
10. Culp M. “On Semi-supervised Learning with the Joint Trained Elastic Net.” Journal of Computational Graphics and Statistics. 22(2):300–318. 2013.
9. Culp M. “On propagated scoring for semi-supervised additive models.” Journal of the American
Statistical Association (theory and methods). 106(493): 248–259, 2011.
8. Culp M. “spa: A Semi-Supervised R Package for Semi-Parametric Graph-Based Estimation.”
Journal of Statistical Software. 40(10), 2011.
7. Culp M., Johnson K., Michailidis G. “The adaptive learning rate for regularized stochastic
boosting.”Journal of Computational Graphics and Statistics. 20(4):937-955, 2011.
6. Culp M., Johnson K., Michailidis G. “The ensemble bridge algorithm: a new modeling tool for
drug discovery problems.” Journal of Chemical Information and Modeling. 50(2): 309-316, 2010.
5. Culp M., Michailidis G., Johnson K. “On multi-view learning with additive models.” Annals
of Applied Statistics. 3(1),2009.
4. Culp M., Michailidis G. “A co-training algorithm for multi-view data with applications in data
fusion.” Journal of Chemometrics. 23(6):294–303. 2009.
3. Culp, M., Michailidis G. “An iterative algorithm for extending learners to a semi-supervised
setting.” Journal of Computational Graphics and Statistics. 17(3):545–571, 2008.
2. Culp, M., Michailidis G. “Graph-based semi-supervised learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence. 30(1):174–179, 2008.
1. Culp, M., Michailidis G., Johnson K. “ada: An R package for stochastic boosting.” Journal of
Statistical Software. 17(2), 2006.
Interdisciplinary 9. Curley B.,Thomas R., Curley A., Truong Q., Culp M., Hu Y., Almubarak M.“Patient underPublications
standing and impression of hematology/oncology fellows.” American Journal of Medical Science.
348(3):262, 2014.
8. DeCann B. , Ross A., Culp M., “On Clustering Human Gait Patterns.” ICPR 22, 2014.
7. McLaughlin S., Ice R., Rajulapati A., Kozyulina P., Ryan L., Kozyreva V., Loskutov Y., Culp
M.,Weed S., Ivanov A., and Pugacheva E. “Depletion of NEDD9 leads to inactivation of MMP14
by TIMP2 and prevents invasion and metastasis of breast cancer cells.” Molecular Cancer Research. 2014.
6. Rodgers-Melnick E., DiFazio S., Culp M. “Predicting whole genome protein interaction networks
from primary sequence data in model and non-model organisms using ENTS.” BMC Genomics.
14:608, 2013.
5. Ice R., McLaughlin S., Culp M., Eddy E., Livengood R., and Pugacheva E. “NEDD9 depletion
destabilizes Aurora A kinase and heightens the efficacy of Aurora A inhibitors: implications for
treatment of metastatic solid tumors.” Cancer Research. 73(10):3168–3180. 2013
4. Weed S., Evans J., Ammer A., Jett J., Bolcato C., Breaux J.,Martin K., Culp M., and Gannett
P. “Src Binds Cortactin Through a SH2 Domain Cystine-mediated Linkage” Journal of Cell
Science. 125: 6185–6197, 2013.
3. Lu H., Cukic B., Culp M. “Software Defect Prediction using Semi-Supervised Learning with
Dimension Reduction.” IEEE/ACM International Conference on ASE. 314–317, 2012
2. Lu H., Cukic B., Culp M. “An Iterative Semi-supervised Approach to Software Fault Prediction”
7th International Conference on Predictive Models in Software Engineering (Promise). 2011 .
1. Burkholder A., Warner T., Culp M., Landenberger R.. “Seasonal trends in separability of
leaf reflectance spectra for Ailanthus altissima and four other tree species.” Photogrammetric
Engineering and Remote Sensing. 77(8): 793–804, 2011.
Current Grants
PI ‘CAREER: Statistical Methodology in Multi-view Learning with Large Data” NSF CAREER
(July 2013-June 2018). Funded.
PI “Validating the Representativeness of Samples from Sequestered Biometric Data Sets.” NSF
I/UCRC CITeR (July 2014-December 2016). Funded.
Co-I “COBRE for signal transduction and cancer sponsoring (bioinformatics and biostatistics,
Phase II-III ).” NIH/NCRR. (July 2007- June 2015). Funded.
Co-I “West Virginia CTR administered by the CTSI CRDEB (biostatistics)” NIH/CTR (July
2012- June 2017). Funded.
Completed Grants Co-I “The role of NEDD9 protein in proliferation and metastasis of breast cancer.” Susan G.
Komen for the Cure (January 2010-June 2013). Funded.
Co-I “Characterization of neural crest gene expression using a new transgenic Xenopus model”
NIH/ R03 (July 2012-June 2013). Funded.
PI “Stratified vs. Convenience Sampling for the Statistical Design of Biometric Collections.” NSF
I/UCRC CITeR: Phase II (January 2013-December 2013). Funded.
PI “Stratified vs. Convenience Sampling for the Statistical Design of Biometric Collections.” NSF
I/UCRC CITeR (January 2012-December 2012). Funded.
Co-PI “Modeling Iris Codes- New Approaches to Iris Individuality and Classification.” NSF
I/UCRC CITeR (January 2012-December 2012). Funded.
PI “Generalized Additive Models for Biometric Fusion and Covariate Analysis.” NSF I/UCRC
CITeR (January 2011-December 2011). Funded.
Co-PI “Identification Technology Research and Co-Design Initiative” NIJ (July 2010 - June 2013).
Funded.
Talks
University of Virginia, Statistics Department
The Ohio State University, Statistics Department
University of Minnesota, Statistics Department
North Carolina State University, Statistics Department
Iowa State University, Statistics Department
Michigan State University, Statistics Department
West Virginia University, Statistics Department
AT&T
Pfizer
Conferences
Joint Statistical Meetings
Quality & Productivity Research Conference
IMS New Researchers Conference
Teaching
Experience
2014
2013
2012
2012
2009
2007, 2005
2007
2007
2007
2014, 2013 (JCGS), 2011 (Late Breaking),
2011,2009 (Chemometrics), 2008, 2007, 2006
2010, 2009
2008
WVU
WVU
WVU
WVU
WVU
WVU
WVU
Stat
Stat
Stat
Stat
Stat
Stat
Stat
791E
745
555
545
543
531
316.
Statistical Machine Learning
Data Mining
Categorical Data Analysis
Applied Regression Analysis
Microarray Data Analysis
Sampling Theory and Methods
Forensic Statistic.
S13
F09, F10, F11, F12, F13, F14
S08, S09, F09, F10, F11, F12, F13, F14
S10, S11, S12,S13,S14
S08, S09, S10, S11, S12
F07, F08
F07, F08
Editorial Service
Associate Editor: StatBlog for the Stat Journal, 2013-present
Associate Editor: JASA/TAS, Book Reviews, 2011-2013
Reviewer: JASA (T&M), JMLR, JSS, JCGS, JSCS, IEEE TPAMI, IEEE TSE, IEEE TIP, IEEE
TNNLS, NSF Panel for Statistics
Software
Development
cFTF: R package for multi-view data analysis (Chemometrics publication).
ada: R package for stochastic boosting (JSS publication).
spa: R package for semi-supervised graph-based estimation (JSS publication).
Membership/
Affiliations
Member of the Biostatistics Core of the WVU CTSI.
Member of the Bioinformatics/Biostatistics Core at the WVU MRBCC.
Affiliated with the NSF/ CITeR, Industry/University Cooperative Research Center (I/UCRC).
Last Updated
October 15, 2014