Networks and Learning
Chuanyi Ji Associate Professor Ph.D’92 Caltech, MS’86 Univ. of Pennsylvania, BS’83 (Honors) Tsinghua Univ.School of Electrical and Computer EngineeringGeorgia Institute of Technology Rm 5165 Centergy Building, Georgia Tech Atlanta, GA30332firstname.lastname@example.org 404-894-2393 (Office) 404-894-7883 (Fax)
Our research lies in both basic and applied areas of networks, adaptive learning, and large-scale measurements.
Our research intends to investigate fundamental issues and to develop engineering solutions for modeling, managing and controlling heterogeneous and large networks; to develop and apply algorithmic and analytical approaches in adaptive learning, statistics and information theory.
We seek understanding, validation of algorithms and theory, from real data when possible.
- Dynamic Model and Resilience of Power and Communication to Large-Scale External Disruptions:
· Y. Wei, C. Ji, F. Galvan, S. Couvillon, G. Orellana, “Dynamic Modeling and Resilience for Power Distribution,” SmartGridComm2013, Vancouver, Oct. 2013.
· Y. Wei, C. Ji, F. Galvan, S. Couvillon, G. Orellana, J. Momoh, “Learning Geo-Temporal Non-Stationary Failure and Recovery of Power Distribution,” Special issue on Learning in Non-stationary and Evolving Environments, IEEE Trans. on Neural Networks, Vol. 25, No.1, 229-240. Jan., 2014.
(An article about this work at IEEE Spectrum by Tekla Perry, 2012)
· Y. Wei, C. Ji, F. Galvan, S. Couvillon, G. Orellana, J. Momoh, “Non-Stationary Random Process for Large-Scale Failure and Recovery of Power Distribution,” preprint at arxiv.org.
· S. Erjongmanee and C. Ji, “Large-Scale Network Service Disruptions: Dependencies and External Factors,” IEEE Trans. on Network and Service Management, Vol. 8, No. 4, Dec. 2011.
- Networks and Learning: Hierarchical Dependency (Graphical) Models, Scalability, and Network Management
· S. Jeon and C. Ji, “Joint Estimation of Information and Distributed Link-Scheduling in Wireless Networks: Mean-Field Approximation and Graphical Models,” preprint at arxiv.org.
· S. Jeon and C. Ji, “Randomized and Distributed Self-Configuration of Wireless Networks: Two-Layer Markov Random Fields and Near-Optimality,” IEEE Trans. Sig. Proc. Vol. 58, No.9, pp. 4859-4870, Sept. 2010.
· R. Narasimha, S. Dihidar, C. Ji and S. McLaughlin, “Scalable Diagnosis in IP Networks Using Path-Based Measurement and Inference: A Learning Approach,” Special Issue on Network Technologies for Emerging Broadband Multimedia Services, Elsevier Journal of Visual Communication and Image Representation, Vol. 21, No. 2, 175-191, Feb. 2010.
· G. Liu and C. Ji, “Scalability of Network-Failure Resilience: Analysis Using Multi-Layer Probabilistic Graphical Models,” IEEE/ACM Trans. Networking, Vol. 17, No. 1, pp. 319-331, Feb. 2009.
· G. Liu and C. Ji, “Resilience of All Optical Networks Under In-Band Cross-Talk Attacks: A Probabilistic Graphical Model Approach,” IEEE Journal of Selected Area of Communications (JSAC), Part Supplement, Vol.25, No. 3, pp. 2-17, April 2007. (Also US patent US Patent # 7903790, “Optical Network Evaluation Systems and Methods” March. 2011)
· G. Liu, C. Ji and V. Chan, “On the Scalability of Network Management Information for Inter-Domain Light Path Assessment,” IEEE/ACM Trans. Networking, Vol. 13, No. 1, pp. 160-172, Feb. 2005.
· C. Ji and A. Elwalid, “Measurement-Based Network Monitoring: Achievable Performance and Scalability,” Special Issue on Recent Advances in Fundamentals of Network Management, IEEE Journal of Selected Areas of Communications (JSAC), Vol. 20, No. 4, pp. 714-725, May 2002
· R. Narasimha, S. Dihidar, C. Ji and S. W. McLaughlin, “Scalable Fault Detection in IP Networks using Graphical Models: A Variational Inference Approach,” Proceeding of International Conference Communication, Glasgow, June 2007, pp. 147-152.
· R. Narasimha, S. Dihidar, C. Ji and S. W. McLaughlin, “A Scalable Probe-Based Approach for Congestion Detection Using Message Passing,” ICC, Istanbul, June 2006, pp. 640-645. 2006.
· Parlos, C. Ji, T. Parisini, M. Bagliettigo, A. Atiya and KC Claffy, Guest Editorial at Special Issue on “Intelligent Systems in Computer Networks, Telecommunication and Internet Technologies,” IEEE Trans. Neural Networks, 2005.
· V. Chan, A. Elwalid, C. Ji and Y. Yemini, Guest Editorial in Special Issue on “Recent Advances in Fundamentals of Network Management,” IEEE Journal of Selected Areas of Communication, Vol. 20, No. 4, pp. 653-654, May 2002.
- Large-Scale Network Disruptions and Attacks: Learning, Information Theory, and Large Data Sets
· C. Ji, S. Li, D. Leytchipayia and P. Barford, “Community Networks for Information Sharing,” in Use of Risk Analysis in Computer Aided Persuasion, NATO Science for Peace and Security Series, edited by E. Duman and A. Atiya, May 2011, Vol. 88, pp. 247-255.
· Z. Chen and C. Ji, “An Information-Theoretical View of Network-Aware Attacks,” IEEE Trans. Information Forensics and Security, Vol. 4, No. 3, pp. 530-541, Sept. 2009.
· Z. Chen and C. Ji, “Optimal Worm Scanning Method using Vulnerable Host Distributions,” International Journal of Network Security, Special Issue on Computer and Network Security, Vol. 2, No. 1-2, pp. 71-80, 2007.
· Z. Chen and C. Ji, “Spatial Temporal Modeling of Malware Propagation in Networks,” Special Issue of Adaptive Learning Systems in Networking, IEEE Trans Neural Networks, Vol. 16, No.5, pp. 1291-1303, Sept. 2005.
· Z. Chen, C. Ji, and P. Barford, “Spatial Temporal Characteristics of Internet Malicious Sources,” INFOCOM Mini-Conference, Phoenix, AZ, April 2008, pp. 2306-2314.
· Z. Chen and C. Ji, “Vulnerable Host Distribution and Malware Scanning,” INFOCOM, Alaska, May 2007, pp. 116-124.
- Proactive Network Anomaly Detection, Learning, and Measurements
· M Thottan, G. Liu and C. Ji, “Anomaly Detection Approaches for Communication Network,” in Algorithms for Next Generation Networks, edited by G. Cormode and M. Thottan, Springer London, 239-261, Feb. 2010.
· M. Thottan and C. Ji, “Anomaly Detection in IP Networks,” Special Issue of Signal Processing in Networking, IEEE Trans. Signal Processing, vol. 51, No. 8, pp. 2191-2204, Aug. 2003
· M. Thottan and C. Ji, “Using Network Fault Prediction to Enable IP Traffic Management,” Journal of Network Management, Vol. 9, No. 3, pp. 327-346, 2001.
· M. Thottan and C.Ji, “Statistical Detection of Enterprise Network Problems,” Journal of Network and Systems Management, 7(1): pp. 27-45, March, 1999.
· M. Thottan and C. Ji, “Proactive Anomaly Detection Using Distributed Intelligent Agents,” IEEE Network, Special Issue on Network Management, Vol. 12, No. 7, pp. 21-27, Sept ./Oct. 1998.
· C. Hood and C. Ji, “Intelligent Agents for Proactive Network Fault Detection,” IEEE Internet Computing, Vol. 2, No.2, p. 65-72, March/April, 1998.
· C. Hood and C. Ji, “Proactive Network Fault Detection,” IEEE Trans. Reliability, Vol. 46, No.3, pp. 333-341, Sept. 1997.
· C. Hood and C. Ji, ” Proactive Network Fault Detection”, Proceedings of IEEE INFOCOM, 1149-1157, Kobe, Japan, Mar. 1997
- Wavelet Models of Network Traffic
· S. Ma and C. Ji, “Modeling Heterogeneous Network Traffic in Wavelet Domain,” IEEE/ACM Trans. Networking, Vol.9, Issue 5, 634-649, Oct. 2001
· X. Tian, J. Wu and C. Ji, ”Approximation Capability of Independent Wavelet Models to Heterogeneous Network Traffic,” Proc. IEEE INFOCOM, New York, 1999, Vol. 1, pp. 446 – 454.
· S. Ma and C. Ji, ”Wavelet Domain Modeling of VBR Video Traffic”, Proceedings of IEEE INFOCOM, Vol. 1, 201-208, San Francisco, California, Mar. 1998, Vol. 1, 201-208.
· S. Ma and C. Ji, “Wavelet Models for Video Time Series,” Proceedings of Neural Information Processing Systems: Natural and Synthetic (NIPS), Denver, Colorado, Nov. 1997, pp. 915-921.
· S. Ma and C. Ji, “Combinations of Weak Classifiers for Supervised Learning: A Survey,’’ in Pattern Recognition and String Matching edited by D. Chen and X. Cheng, Kluwer Academic Publisher, July 2002, pp. 379-388.
· S. Ma and C. Ji, “Performance and Efficiency: Recent Advances in Supervised Learning,” Proceedings of The IEEE, Vol. 87, No. 9, pp. 1519-1535, Sept/Oct, 1999.
· S. Ma and C. Ji, “A Unified Approach on Fast Training of Feedforward and Recurrent Networks Using EM Algorithm,” IEEE Trans. Signal Processing, Vol .46, No. 8, pp. 2270-2274, Aug., 1998.
· S. Ma and C. Ji, “Fasting Training of Recurrent Networks Based on the EM-Algorithm,” IEEE Trans. Neural Networks, Vol. 9, No.1, pp. 11-26, Jan. 1998.
· C. Ji and D. Psaltis, “The Capacity of Two Layer Feedforward Neural Networks with Binary Weights,” IEEE Trans. Information Theory, Vol. 44, No.1, pp. 256-268, Jan. 1998.
· C. Ji and D. Psaltis, “Network Synthesis Through Data-Driven Growth and Decay,’’ Neural Networks, Vol. 10, No.6, pp. 1133-1141, Aug. 1997.
· A. Atiya and C. Ji, “How Initial Conditions Affect Generalization Performance in Large Networks,’’ IEEE Trans. Neural Networks, Vol. 8, No.2, pp. 448-451, Mar. 1997.
· S. Ma, C. Ji and J. Farmer, “An Efficient EM-based Training Algorithms for Feedforward Neural Networks,” Neural Networks, Vol. 10(2), pp. 243-256, Mar. 1997.
· C. Ji and S. Ma, “Combinations of Weak Classifiers,” IEEE Trans. Neural Networks, Special Issue on Neural Networks and Pattern Recognition, Vol. 8, No.1, pp. 32-42, Jan. 1997.
· C. Ji, R. Snapp and D. Psaltis, “Generalizing Smoothness Constraint from Discrete Data,’’ Neural Computation, Vol. 2, pp. 188-197, 1990
· C. Ji and S. Ma, ”Combined Weak Classifiers”, Proceedings of Neural Information Processing Systems: Natural and Synthetic (NIPS), 494-500, Denver, Colorado, 1996
· C. Ji, “Generalization Error and The Expected Network Complexity,” Proceedings of Neural Information Processing Systems: Natural and Synthetic (NIPS), Denver, Colorado, 1993, pp. 367-374.
· C. Ji and D. Psaltis, “The Information Capacity and the Universal Sample Bound for Generalization,” Proceedings of Neural Information Processing Systems: Natural and Synthetic (NIPS), Denver, Colorado, 1991, 928-935.