My research interests are fundamentally rooted in the area of machine learning. For a number of
years now, I have primarily focused on methods for learning large margin classifiers to address
various perception tasks, such as object classification in video, sonar classification, hyperspectral
anomaly detection and acoustic classification. More recently, I have ventured into the world of
social network analysis and the plethora of machine learning challenges associated with inferring
the structure and attributes of social networks in the digital realm. My core interests in the theoretical
aspects of statistical machine learning are driven by the following fundamental questions:
How can machines efficiently acquire context from a human expert over time to
solve a pattern recognition task?
How can a machine identify deficiencies in its own understanding of the environment
in order to request more context from the user?
How does one estimate the generalization performance of a model efficiently?
What approaches allow a designer to impose explicit constraints on the computational
complexity of a classifier during learning?
These questions have motivated my work in incremental and active learning, novelty detection, efficient
leave-one-out error estimation and sparse kernel machine learning.
CURRENT PROJECTS
Inferring Dynamic Social Networks from Email Archives
Collaborators: Lise Getoor and Galileo Namata, University of Maryland-College Park
Micropower Pattern Recognition in Analog VLSI
Collaborator: Shantanu Chakrabartty, Michigan State University
"Approximate Leave-One-Out Error Estimation for Learning with Smooth, Strictly Convex Margin
Loss Functions" 2004 IEEE Workshop on Machine Learning for Signal Processing [PDF]
"SVM Incremental Learning, Adaptation and Optimization"
2003 International Joint Conference on Neural Networks [PDF]
PUBLICATIONS
C. P. Diehl, G. Namata, L. Getoor, "Relationship Identification for Social Network Discovery," AAAI 2008 Enhanced Messaging Workshop. [PDF]
(Note: this paper is a shortened version of the AAAI 2007 paper.)
C. P. Diehl, G. Namata, L. Getoor, "Relationship Identification for Social Network Discovery," AAAI 2007. [PDF]
A. Banerjee, P. Burlina, C. P. Diehl, "A Support Vector Method for Anomaly Detection in Hyperspectral Imagery,"
IEEE Transactions on Geoscience and Remote Sensing, 44(8), August 2006, pp. 2282-2291. [PDF]
C. P. Diehl, L. Getoor, G. Namata, "Name Reference Resolution in Organizational Email Archives,"
2006 SIAM International Conference on Data Mining. [PDF]
L. Getoor, C. P. Diehl, "Link Mining: A Survey," ACM SIGKDD Explorations, 7(2), December 2005. [PDF]
G. Cauwenberghs, A. Andreou, J. West, M. Stanacevic, A. Celik, P. Julian, T. Teixeira, C. Diehl, L. Riddle,
"A Miniature, Low-Power, Intelligent Sensor Node for Persistent Acoustic Surveillance,"
Proceedings of the SPIE, 5796, May 2005, pp. 294-305. [PDF]
C. J. Costello, C. P. Diehl, A. Banerjee, H. Fisher, "Scheduling an Active Camera to Observe People,"
Proceedings of the 2nd ACM International Workshop on Video Surveillance and Sensor Networks,
October 2004. [PDF]
R. J. Vogelstein, K. Murari, P. H. Thakur, G. Cauwenberghs, S. Chakrabartty, C. P. Diehl, "Spike Sorting with
Support Vector Machines," Proceedings of the 26th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society, San Francisco, CA, September 2004. [PDF]
C. P. Diehl, "Approximate Leave-One-Out Error Estimation for Learning with Smooth, Strictly Convex
Margin Loss Functions," Proceedings of the 2004 IEEE Workshop on Machine Learning for Signal
Processing.[PDF]
I-J. Wang, C. P. Diehl, F. J. Pineda, "A Statistical Model of Proteolytic Digestion,"
Proceedings of the 2003 IEEE Computer Society Bioinformatics Conference, pp. 506-508. [PDF]
C. P. Diehl, G. Cauwenberghs, "SVM Incremental Learning, Adaptation and Optimization,"
Proceedings of the 2003 International Joint Conference on Neural Networks, Special Session on
Incremental Learning, pp. 2685-2690, Invited Paper. [PDF]
M. Saptharishi, C. S. Oliver, C. P. Diehl, K. S. Bhat, J. M. Dolan, A. Trebi-Ollennu, P. K. Khosla, "Distributed
Surveillance and Reconnaissance Using Multiple Autonomous ATVs: CyberScout," IEEE Transactions
on Robotics and Automation: Special Issue on Advances in Multirobot Systems, vol. 18, no. 5, October 2002. [PDF]
C. P. Diehl, J. B. Hampshire II, "Real-time Object Classification and Novelty Detection for Collaborative
Video Surveillance,"Proceedings of the 2002 International Joint Conference on Neural Networks, vol. 3,
pp. 2620-2625 [PDF]
M. Saptharishi, K. S. Bhat, C. P. Diehl, J. M. Dolan, P. K. Khosla, "CyberScout: Distributed Agents for
Autonomous Reconnaissance and Surveillance," Proceedings of the Conference on Mechatronics and
Machine Vision in Practice, September 2000, pp. 93-100 [PDF]
M. Saptharishi, K. Bhat, C. Diehl, C. Oliver, M. Savvides, A. Soto, J. Dolan, P. Khosla, "Recent Advances in
Distributed Collaborative, Surveillance," Proceedings of the SPIE, vol. 4040, pp. 199-208 [PDF]
C. P. Diehl, M. Saptharishi, J. B. Hampshire II, P. K. Khosla, "Collaborative Surveillance Using Both Fixed
and Mobile Unattended Ground Sensors," Proceedings of the SPIE, vol. 3713, July 1999, pp. 178-185 [PDF]
C. P. Diehl, B. E. Henty, N. Kanodia, D. D. Stancil, "Wireless RF Distribution in Buildings Using Heating
and Ventilation Ducts," Proceedings of 8th Annual Virginia Tech Symposium on Wireless Personal
Communications, Blacksburg, VA, 10-12 June 1998, pp. 61-70 [PDF]