Princeton University

School of Engineering & Applied Science

Sun-Yuan Kung

Professor of Electrical Engineering

Room: B230 Engineering Quadrangle
Phone: 609-258-3780


  • Ph.D., Stanford University, 1977
  • M.S., Electrical Engineering, University of Rochester, 1974
  • B.S., Electrical Engineering, National Taiwan University, 1971

Major advances in information technologies must arise from close collaboration between application, hardware/architecture, algorithm, CAD, and system design. It is critical to consider, for instance, how to deploy in concert an ever-increasing number of transistors with acceptable power consumption, and how to make hardware user-friendly yet effective for applications. Over the past 30 years, my research group has studied the development of signal processing systems. Our research findings have played direct and indirect roles in the construction of a number of useful signal processing systems. In this dynamic discipline, however, emerging applications always dictate new system metrics on power consumption, speed, and performance. This inspires novel intellectual challenges on system designs. Two vital applications that have emerged in recent years are multimedia and genomics. They form an important basis of a new paradigm of information processing that relies heavily on intelligent search/processing.

Multimedia technologies will profoundly change the way we access information. They will also provide new challenges for machine learning research. We investigate various issues relevant to intelligent multimedia communication application, and develop machine learning tools for various adaptive and content-based technologies for MPEG-4 applications such as compression, indexing, and retrieval of visual information. This work has natural and promising extensions to internet search engines, document analysis, and biometric authentication.

The field of bioinformatics represents a natural convergence of life sciences and information technologies. Modern large-scale DNA devices such as microarrays have rapidly and steadily generated enormous amounts of genomic data. We study advanced machine learning tools that could reveal salient information embedded in genomic data and facilitate classification and prediction of tumors and their responses to drug therapies.

From the application perspective, machine learning is both effective and instrumental for distilling useful information embedded in the wealth of the available data. We study tailor-designed machine learning tools for specific applications including feature extraction, clustering, and classification. Machine learning tools can also be adopted to facilitate integration of data collected from experiments in different levels of biological systems or fusion of diversified multimedia data such as text, speech, image, video, and graphics.

In addition to application studies, we also investigate theoretical aspects of machine learning, based on the basic principle of learning by example. These theoretical foundations include statistical learning, optimization, and algebraic theory. In the past half century, machine learning techniques have evolved from simple linear classifiers to neural networks and, recently, to kernel-based approaches. The promise of the kernel approach hinges upon its new representation vector space, leading to a divergent data structure. It also theoretically assures the linear separability of training data in the reproducible kernel space. Moreover, it provides a unified treatment of heterogeneous genomic data, including vectors, sequences, and graphs.

We investigate both categories of kernel-based learning techniques:

1. Unsupervised learning for cluster discovery and graph partition. Kernel approaches extend the conventional K-means (designed for clustering vectors in Euclidean space) to any objects that can be characterized by pairwise relationship (sequences or graphs). The result may be applied to kernel-based (e.g., kernel K-means), and graph-based clustering algorithms. The latter have potential applications to genomics (e.g., interaction networks, metabolic networks, or signaling pathways) and multimedia (e.g., search engines and World Wide Web/social networks).

2. Supervised cluster discovery and supervised classification/prediction. Kernel approaches provide a unified framework between Fisher and support vector machine (SVM) classifiers. This ultimately leads to a unifying hybrid classifier, which includes Fisher’s discriminant analysis (FDA) and SVM as special cases. The unified classifier offers necessary flexibility for improving prediction performance.

Honors and Awards

  • Best Paper Award, IEEE Signal Processing Society (1996)
  • Distinguished Lecturer, IEEE Signal Processing Society, (1994)
  • Honorary Professorship, Central China Science & Technology University, (1994)
  • Technical Achievement Award, IEEE Signal Processing Society, (1992)
  • Fellow of IEEE, for contribution to VLSI signal processing and neural networks, (1988)

Selected Publications

  1. Y. Wu, V. Stankovic, Z. Xiong, and S.-Y. Kung, “On practical design for joint distributed source and network coding”, IEEE Trans. Information Theory, Vol. 55, No. 4, pp. 1709—1720, Apr. 2009;
  2. S.Y. Kung, Kernel Approaches to Unsupervised and Supervised Machine Learning, (Keynote Paper, PCM 2009), Muneesawang et al. (Eds.): Springer-Verlag LNCS 5879, pp. 1–32, 2009;
  3. M.W. Mak and S.Y. Kung, "Fusion of Feature Selection Methods for Pairwise Scoring SVM", Neurocomputing, special issue for ICONIP'06, Volume 71, Issues 16-18, October 2008, Pages 3104-3113;
  4. M.W. Mak, J. Guo, and S.Y. Kung, “PairProSVM: Protein Subcellular Localization Based on Local Pairwise Profile Alignment and SVM”, IEEE/ACM Trans. on Computational Biology and Bioinformatics, 2008;
  5. S.Y. Kung and M.W. Mak, “Feature Selection for Self-Supervised Classification with Applications to Microarray and Sequence Data”, IEEE Journal of Selected Topics in Signal Processing, Special Issue on Genomic and Proteomic Signal Processing, 2008;
  6. M.W. Mak, K.K. Yiu and S.Y. Kung, "Probabilistic Feature-Based Transformation for Speaker Verification over Telephone Networks", Neurocomputing, special issue on Neural Networks for Speech and Audio Processing, vol. 71, pp. 137–146, 2007;
  7. K.K. Yiu, M.W. Mak and S.Y. Kung, "Environment Adaptation for Robust Speaker Verification by Cascading Maximum Likelihood Linear Regression and Reinforced Learning", Computer Speech and Language, vol. 21, pp. 231-246, 2007.;
  8. Yunnan Wu, K. Jain, and S.-Y. Kung, “A Unification of Network Coding and Tree-Packing (Routing) Theorems”, joint special issue of the IEEE Trans. on Information Theory and the IEEE/ACM Trans. on Networking on Networking and Information Theory, 2006;
  9. Yunnan Wu and S.-Y. Kung, “Distributed utility maximization for network coding based multicasting: a shortest path approach,” IEEE J. on Selected Areas in Communications, special issue on Nonlinear Optimization of Communication Systems, submitted, Vol. 24, No. 8, August 2006;
  10. S.Y. Kung, M. W. Mak, and I. Tagkopoulos. Symmetric and asymmetric multi-modality biclustering analysis for microarray data matrix. J. of Bioinformatics and Computational Biology, 4(3), June 2006, pp. 275-298;
  11. K.K. Yiu, M.W. Mak, M.C. Cheung, and S.Y. Kung, "Blind Stochastic Feature Transformation for Channel Robust Speaker Verification, " J. of VLSI Signal Processing, Vol. 42, Issue 2, pp. 117-126, February 2006;
  12. K.Y. Leung, M.W. Mak, M.H. Siu, and S.Y. Kung, "Adaptive Articulatory Feature-Based Conditional Pronunciation Modeling for Speaker Verification, Speech Communications, Volume 48, Issue 1, pp. 71-84, January 2006;
  13. Xinying Zhang, A. F. Molisch and S. Y. Kung, "Variable-phase-shift-based RF-baseband codesign for MIMO antenna selection", IEEE Trans. Signal Processing, Vol. 53, No. 11, pp. 4091-4103, November, 2005;
  14. Yunnan Wu, P. A. Chou, S.-Y. Kung, “Minimum-energy multicast in mobile ad hoc networks usi ng network coding,” IEEE Trans. on Communications, Vol. 53, No. 11, pp. 1906-1918, November 2005;
  15. S. Y. Kung, X. Zhang and C. Myers, "A recursive QR approach to adaptive equalization of time-varying ISI MIMO channels", T. Kailath 70-th Birthday Special Issue in The journal Communications in Informations and Systems, Editor: Ali H. Sayed, Vol. 5, No. 2, pp. 169-196, 2005.