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: https://bi.snu.ac.kr/~btzhang/
 
   

RESEARCH

LAB

PUBLICATIONS

TEACHING

ACTIVITIES

BIOGRAPHY


Research Overview
How does the biological mind work? How does the brain build a model of the world to instantly and robustly act on it? What kinds of processing and organizational mechanisms allow the brain to learn so rapidly, flexibly, and continuously in a noisy, dynamic, and changing environment? How can we build smart machines that perceive, think, act, and learn like the human brain and mind? In the Biointelligence Lab and the Cognitive Robotics and ArtificialIntelligence Center (CRAIC) we pursue these questions using diverse methods of cognitive modeling, computational neuroscience, robotics, and computer science.

    We build robotic surrogates by "cloning" the embodied and situated mind using wearable sensors, such as smart glasses, watches, brain scanners, body sensors, and mobile devices. We collect human activity data by long-term life-tracking in the real world. Based on these observational data, we reverse-engineer the brain architecture by exploring the plausible neurocognitive models that best explain the mind in working. We also use videos and cartoons to study how the cognitive brain constructs episodic memory and mental imagery from a long sequence of context-rich events while watching dramas and movies.
    Our current efforts center around the deep, recurrent, and sparse hypernetwork architectures and learning algorithms that self-organize their structures instantly, incrementally, and continuously in a self-supervised way by perception-action cycle. The ultimate goal is to discover a large-scale, neurocognitive computational model of the brain that autonomously develops or evolves into human-level machine intelligence in lifelong interactions with the environment. For the ongoing and completed projects, visit BI research. For general information, visit the Biointelligence Laboratory home.

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 Editorship

Graduate Courses Taught

Talks and Tutorials

Position Papers & Monographs [Korean]

Meetings Organized Selected Publications
  • Humans and machines in the evolution of AI in Korea, AI Magazine, 37(2):108-112, 2016. [PDF]
  • Multimodal residual learning for visual QA, J.-H. Kim, S.-W. Lee, D.-H. Kwak, M.-O. Heo, J. Kim, J.-W. Ha, and B.-T. Zhang, In Proceedings of the Advances in Neural Information Processing Systems 29 (NIPS 2016), 2016. (to appear) [arXiv]
  • DeepSchema: Automatic schema acquisition from wearable sensor data in restaurant situations, E.-S. Kim, K.-W. On, and B.-T. Zhang, In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2016), pp. 834-840, 2016. [PDF]
  • Dual-memory deep learning architectures for lifelong learning of everyday human behaviors, S.-W. Lee, C.-Y. Lee, D. H. Kwak, J. Kim, J. Kim, and B.-T. Zhang,  In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2016), 1669-1675, 2016. [PDF]
  • Deep hypernetwork models (in Korean), Communications of the Korean Institute of Information Scientists and Engineers, 33(8):11-24, 2015. [PDF]
  • Team THOR's entry in the DARPA robotics challenge trials 2013, Journal of Field Robotics, 32(3):315-335, 2015. [PDF]
  • Consensus analysis and modeling of visual aesthetic perception, IEEE Transactions on Affective Computing, 6(3):272-285. [PDF]
  • Automated construction of visual-linguistic knowledge via concept learning from cartoon videos, J.-W. Ha, K.-M. Kim, and B.-T. Zhang,  In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015), 2015. [PDF]
  • Ontogenesis of agency in machines: A multidisciplinary review, B.-T. Zhang, AAAI 2014 Fall Symposium on The Nature of Humans and Machines: A Multidisciplinary Discourse, Arlington, VA, 2014. [PDF]
  • Communication as moving target tracking: Dynamic Bayesian inference with an action-perception-learning cycle, B.-T. Zhang, Alignment in Communication. Towards a New Theory of Communication, Chapter 7, pp. 133-147, 2013. [PDF]
  • Information-theoretic objective functions for lifelong learning, B.-T. Zhang, AAAI 2013 Spring Symposium on Lifelong Machine Learning, Stanford University, March 25-27, 2013. [PDF]
  • Sparse population code models of word learningin concept drift, B.-T. Zhang, J.-W. Ha, and M. Kang, In Proceedings of the 34th 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]

             [more]

 

Biointelligence Lab

School of Computer Sci. & Eng.

SNU