﹛

Byoung-Tak Zhang




Professor
School of Computer Science and Engineering &
Cognitive Science, Brain Science, and Bioinformatics
Seoul National University
Seoul 151-744, Korea
Office: Room 302-323 (New Engineering             Building)
Phone: +82-2-880-1833 (office),
                     880-1847 (secretary)
Fax: +82-2-875-2240
E-mail: btzhang@bi.snu.ac.kr
URL: http://bi.snu.ac.kr/~btzhang/
 
   

RESEARCH

LAB

PUBLICATIONS

TEACHING

ACTIVITIES

BIOGRAPHY


Research Overview
My long-term research goal is (i) to understand the natural (biological and/or cognitive) information processing principles underlying human learning, memory, and intelligence, and (ii) to use these principles to develop innovative computational architectures and algorithms for building synthetic intelligence systems solving practical problems. This natural computation approach to machine learning and intelligence has, thus, aspects of both science and technology. I strive to address the problems based on real-life settings, and solve them using principled methods so that we make long-term progress in the advancement of technology.
    My current research is centered around the hypernetwork model. It is a random hypergraph structure for learning higher-order probabilistic relationships from data. The model is inspired by biological networks that evolve via variation and selection at the molecular level. The working hypothesis in the hypernetwork approach to synthetic intelligence is that associative learning through massive interaction of a large number of memory units, like molecules in biological networks, is a foundational element for robust, flexible information processing and decision-making in human brain and other natural biological systems.
    The research on hypernetworks has so far focused on developing the mathematical models and learning algorithms based on molecular evolutionary computation (MEC), i.e. evolutionary computation using molecules. In my group we work on novel applications and comparative analysis of the algorithms with other cutting-edge machine learning techniques. The hypernetwork models are also being implemented in electronic hardware and in vitro DNA computing. More details on these and related projects currently going on in my Biointelligence Laboratory can be found by visiting the pages:

Theory: Hypernetwork Models of Learning and Memory
Applications: Data Mining in Life Sciences and Life Blogs
Technology: Molecular Evolutionary Computation in Silico and in Vitro

Journal Editorships

Graduate Courses Taught

Talks and Tutorials

Position Papers & Monographs  [Korean]

Conferences Involved
Selected Publications
  • Sparse population code models of word learning in concept drift, B.-T. Zhang, J.-W. Ha, and M. Kang, In Proceedings of Annual Meeting of the Cognitive Science Society (CogSci 2012), 2012. [PDF]
  • Neural correlates of episodic memory formation in audio-visual pairing tasks, C.-Y. Lee, B.-J. Lee, J. S. Kim, and B.-T. Zhang, In Proceedings of Annual Meeting of the Cognitive Science Society (CogSci 2012), 2012. [PDF]
  • A DNA assembly model of sentence generation, J.-H. Lee, S. H. Lee, W.-H. Chung, E. S. Lee, T. H. Park, R. Deaton, and B.-T. Zhang, BioSystems, 106:51-55, 2011. [PDF]
  • In vitro molecular pattern classification via DNA-based weighted sum operation, H.-W. Lim, S.H. Lee, K.-A. Yang, J.Y. Lee, S.-I. Yoo, T. H. Park, and B.-T. Zhang, BioSystems, 100(1):1-7, 2010. [PDF]
  • Teaching an agent by playing a multimodal memory game: challenges for machine learners and human teachers, B.-T. Zhang, AAAI 2009 Spring Symposium: Agents that Learn from Human Teachers, pp. 144-149, 2009. [PDF]
  • Hypernetworks: A molecular evolutionary architecture for cognitive learning and memory, B.-T. Zhang, IEEE Computational Intelligence Magazine, 3(3):49-63, August 2008.[PDF][LINK]
  • The use of gold nanoparticle aggregation for DNA computing and logic-based biomolecular detection, I.-H. Lee, K.-A. Yang, J.-H. Lee, J.-Y. Park, Y. G. Chai, J.-H. Lee, and B.-T. Zhang, Nanotechnology, 19:395103, 2008. [PDF]
  • An evolutionary Monte Carlo algorithm for predicting DNA hybridization, J.-S. Kim, J.-W. Lee, Y.-K. Noh, J.-Y. Park, D.-Y. Lee, K.-A. Yang, Y.G. Chai, J.-C. Kim, and B.-T. Zhang, BioSystems, 91(1):69-75, 2008. [PDF]
  • A global minimization algorithm based on a geodesic of a Lagrangian formulation of Newtonian dynamics, J.-S. Kim, J.-C. Kim, J.-M. O, and B.-T. Zhang, Neural Processing Letters, 26(2):121-131, 2007. [PDF]
  • Discovery of microRNA-mRNA modules via population-basedprobabilistic learning, J.-G. Joung, K.-B. Hwang, J.-W. Nam, S.-J. Kim, and B.-T. Zhang, Bioinformatics, 23(9):1141-1147 2007. [PDF]
  • Molecular basis for the recognition of primary microRNAs by the Drosha-DGCR8 complex, J. Han, Lee Y, K.H. Yeom, J.-W. Nam,  I. Heo, J.-K. Rhee, S. Y. Sohn, Y. Cho, B.-T. Zhang and V.N. Kim, Cell, 125:887-901, 2006. [PDF]
  • DNA hypernetworks for information storage and retrieval, B.-T. Zhang and J.-K. Kim, Proceedings of the Twelfth International Meeting on DNA Computing (DNA 12), pp. 283-292, 2006. [PDF]
  • Molecular learning of wDNF formulae, B.-T. Zhang and H.-Y. Jang, Proceedings of the Eleventh International Meeting on DNA Computing (DNA 11), pp. 185-195, 2005. [PDF]
  • Multi-objective evolutionary optimization of DNA sequences for reliable DNA computing, S.-Y. Shin, I.-H. Lee, D. Kim, and B.-T. Zhang, IEEE Transactions on Evolutionary Computation, 9(2):143-158, 2005. [PDF]
  • Bayesian model averaging of Bayesian network classifiers over multiple node-orders: application to sparse datasets, K.-B. Hwang and B.-T. Zhang, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 35(6):1302-1310, 2005. [PDF]
  • Molecular programming: evolving genetic programs in a test tube, B.-T. Zhang and H.-Y. Jang, The Genetic and Evolutionary Computation Conference (GECCO 2005), vo.l 2, pp.1761-1768, 2005. [PDF]
  • A Bayesian algorithm for in vitro molecular evolution of pattern classifiers, Byoung-Tak Zhang and Ha-Young Jang, Proceedings of the Tenth International Meeting on DNA Computing (DNA10), pp.294-303, 2004. [PS]
  • Self-organizing latent lattice models for temporal gene expression profiling, Zhang, B.-T, Yang, J. and Chi, S. W., Machine Learning, 52(1/2):67-89, 2003. [PDF]
  • System identification using evolutionary Markov chain monte carlo, Zhang, B.-T. and Cho, D.-Y., Journal of Systems Architecture, 47(7):587-599, 2001. [PDF]
  • Bayesian methods for efficient genetic programming, Zhang, B.-T., Genetic Programming and Evolvable Machines, 1(3):217-242, 2000. [PDF]
  • Molecular algorithms for efficient and reliable DNA computing, Zhang, B.-T. and Shin, S.-Y., Proceedings of the Third Annual Genetic Programming Conference (GP-98), pp. 735-742, Morgan Kaufmann, 1998.
  • Evolutionary induction of sparse neural trees, Zhang, B.-T., Ohm, P., and Mühlenbein, H., Evolutionary Computation, 5(2):213-236, 1997. [PDF]
  • Balancing accuracy and parsimony in genetic programming, Zhang, B.-T. and Mühlenbein, H., Evolutionary Computation, 3(1):17-38, 1995. [PDF]
  • Accelerated learning by active example selection, Zhang, B.-T., International Journal of Neural Systems, 5(1):67-75, 1994. [PDF]
  • Evolving optimal neural networks using genetic algorithms with Occam's razor, Zhang, B.-T. and Mühlenbein, H., Complex Systems, 7(3):199-220, 1993. [PDF] [LINK]
  • Neural networks that teach themselves through genetic discovery of novel examples, Zhang B.-T. and Veenker, G., Proceedings of the 1991 IEEE International Joint Conference on Neural Networks (IJCNN'91), vol. 1, pp. 690-695, 1991. [PDF]

             [more]

 

Biointelligence Lab

School of Computer Sci. & Eng.

SNU