Dr. Tai Sing Lee Associate Professor in Computer Science Computer Science Department Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh, PA 15213 Office: Mellon Institute Rm 115 (CNBC) Office: Gates-Hillman Center GHC 8017 (Computer Science) Phone: 412-268-1060 Email: tai at cnbc dot cmu dot edu

Faculty member in the Computer Science Department, and the Center for the Neural Basis of Cognition , affliated faculty member in the Machine Learning Department, the Pittsburgh Supercomputing Center , and adjunct faculty member of the Department of Neuroscience, University of Pittsburgh.

Director of Intercolledge Undergraduate Minor in Neural Computation, Carnegie Mellon University. Coordinator of Undergraduate Training fellowship in Computational Neuroscience (Program in Neural Computation).

Research interests: computational neuroscience, computational vision, neurophysiology of the primate visual systems, active and adaptive vision, hierarchical coding and inference, mid-level vision, development of infant vision, learning and adaptation, structure of neural codes.

Current Research Projects.

Courses and Seminars

• Neural Computation (15-386/686), Spring 2010 Spring 2011 Spring 2012
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• Computer Vision (15-385/685) Spring 2011, Spring 2013.
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• Computational Perception (86-375/675) Spring 2012 Fall 2013
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• Computational Neuroscience of Vision (86-712) Fall 2012
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• Advanced Topics in Computational Neuroscience (2002-2008) with Mike Lewicki
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Education and Appointments

• S.B. Engineering Physics, Harvard College (1986) .
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• Ph.D. Engineering Sciences, Harvard University (1993) .
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• Ph.D. Medical Physics and Engineering, Harvard-MIT Division of Health Science and Technology (1993)
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• Postdoc. Neurophysiology, Massachusetts Institute of Technology (1993-96) .
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• Assistant Professor, Computer Science/CNBC, Carnegie Mellon University (1996) .
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• Associate Professor, Computer Science/CNBC, Carnegie Mellon University (2002) .
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• Associate member, Canadian Institute for Advanced Research (2005)
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• Society of Harvard Engineers and Scientists Honor Scholar (1985) .
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• Harvard-MIT HST Fellowship Award (1986) .
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• McDonnell Pew Foundation Young Investigator Research Award (1996) .
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• NSF Faculty Career Development (CAREER) Award (2000).
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Current Postdoctoral Fellows

• Jason Samonds , Ph.D. Electrical Engineering (2004) Vanderbilt University.
• Corentin Massot , Ph.D. Congitive Science, University of Glasgow & McGill University

Current Ph.D. Students

• Xiong Li . Electrical Engineering (visiting)
• Alex Yu . Electrical Engineering (visiting)

Current Undergraduate Research Fellows

• Shashank Singh , Computer Science, CMU
• Dan Howarth , Computer Science, CMU

Former Ph.D. Students

• Stella Yu , Ph.D. Robotics 2002, CMU. Assistant professor in computer science, Boston college, MA.
• Brian Potetz , Ph.D. Computer Science, 2008, CMU. Assistant professor in computer science, University of Kansas.
• Ryan Kelly , Ph.D. Computer Science, 2010, CMU. Google Research New York.
• Tom Stepleton , Ph.D. Robotics 2010, CMU. Google Research Pittsburgh.
• Richard Romero , M.S. Computer Science. Co-Founder, Eizel Technology
• Ryan Poplin , M.S. in Neural Computation.Broad Institute, Boston.

Former Postdoctoral Fellows

• Matthew Smith , Ph.D. Neuroscience (2002), New York University. Assistant Professor in Ophthalmology, University of Pittsburgh.
• Xiaogang Yan , Ph.D. Biomedical Engineering (1990), Zhejiang University, PRC. York University.
• Yugou Yu , Ph.D. Physics (2001) Nanjing University, PRC. Professor in Computational Neuroscience, Fudan University, Shanghai, China.

Former Undergraduate Research Fellows (and their next steps)

• Ben Poole , Computer Science, CMU, Computer Science Ph.D. program, Stanford University
• Grace Lindsay , Neuroscience, University of Pittsburgh. Fellow in Bernstein Center for Computational Neuroscience, Freiburg. Ph.D. program in Computational Neuroscience, Columbia University.
• Amber Xu , Electrical Engineering, CMU
• Andrew Noh , Electrical Engineering, CMU. Google.
• Ian Lenz , ECE, CIT CMU. Ph.D. program in EECS, Cornell University.
• Carl Doersch , Computer Science, CMU. Machine Learning Department Ph.D. program, CMU.
• Andrew Maas , Computer Science, CMU. Stanford Ph.D. program in Computer Science
• Ankit Khambhati , ECE, CMU. Ph.D. program in Biomedical Engineering, UPenn.
• Lei Liu , Neuroscience. M.D. Program Temple Medical School
• My Nguyen , Biology. Master, Heinz School of Public Policy, Carnegie Mellon
• Ken Shan , Mathematics. Ph.D. in Computer Science, Harvard University, assistant professor in computer science and cognitive science, Rutgers, State U. of New Jersey.
• Matt Easterday , Computer Science/Philosophy. Ph.d. in Human Computer Interaction, Carnegie Mellon University. Now Assistant Professor, Northwestern University.
• Mary Berna , Mechanical Engineering. Ph.D. program in Robotics, Carnegie Mellon.
• Cindy Yang , Biology. Ph.D. program in Neuroscience, UCSF.
• Scott Marmer , Electrical Engineering. J.D. Harvard Law School
• Alexandria Marino , Psychology. M.D./Ph.D. program, Yale Medical School
• Tom DuBois , Computer Science.
• Elise Cassidente , Computer Science. Law School
• Khary Mendez , Computer Science. Software Engineer.
• Iain Proctor , Computer Science.

Publications

• Samonds, J.M., Potetz, B., Tyler, C., Lee, T.S. (2013) Recurrent connectivity can account for the dynamics of disparity processing in V1 Journal of Neuroscience, 33(7):2934 –2946.
• Yan, XG, Khambhati, A., Liu, L., Lee, T.S. (2012) Neural dynamics of image representation in the primary visual cortex. Journal of Physiology, vol 106, 5-6: 250-265.
• Samonds, J.M., Potetz, B., Lee, T.S. (2012) Relative luminance and binocular disparity preferences are correlated in macaque primary visual cortex, matching natural scene statistics. Proceedings of the National Academy of Sciences (PNAS), 109 (16): 6313-6318.
• Samonds, J.M., Lee, T.S. (2011) Neuronal interactions and their role in solving the stereo correspondence problem. In Vision in 3D Environments, Ed. Laurence Harris, Michael Jenkin, Cambridge University Press.
• Li, X., Lee, T.S., Liu, Y. (2011) Hybrid Generative-Discriminative Classification using Posterior Divergence IEEE Conference in computer vision and pattern recognition (CVPR). 2713-2720.
• Kelly, R.C., Smith, M.A., Kass, R.E., T.S. Lee (2010) Accounting for network effects in neuronal responses using L1 regularized point process models NIPS -- Advances in Neural Information Processing Systems, 23: 1099-1107. .
• Kelly, R.C., Smith, M.A., Kass, R.E., T.S. Lee (2010) Local field potentials indicate network state and account for neuronal response variability. J. Computational Neuroscience. 29:567-579. .
• Potetz, B., Lee, T.S. (2010) Scene statistics and 3D surface perception. In Computational Vision: From Surfaces to Objects. Chapman Hall. Ed. C. W. Tyler. Chapman & Hall/CRC, chapt 1, pp. 1-25, (2010).
• Samonds, J.M., Potetz, B., Lee, T.S., (2009) Cooperative and competitive interactions facilitate stereo computations in macaque primary visual cortex J. Neuroscience 29(50):15780-15795, 2009. .
• Stepleton, T., Ghahramani, Z., Gordon G., Lee, T.S. (2009) The Block Diagonal Infinite Hidden Markov Model Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics. AND Journal of Machine Learning Research: 5: 544-551.
• Lee, T.S. (2009) Computational approaches in visual perception Encyclopedia of Perception, Ed. E.B. Goldstein et al., SAGE Press.
• Lee, T.S., Stepleton, T, Potetz, B and Samonds J. (2008) Neural coding of scene statistics for surface and object inference In Object Categorization: perspectives from human and machine vision, Ed. Sven Dickinson, Ales Leonardis, Bernt Schiele, Michael Tarr, Cambridge University Press.
• Lee, T.S. (2008) Contextual influences in visual processing. Encyclopedia of Neuroscience, Ed. M.D. Binder, N. Hirokawa and U. Windhorst, Springer-Verlag. in Press.
• Potetz, B. and Lee, T.S. (2008) Efficient belief propagation for higher order cliques using linear constraint nodes. Computer Vision and Image Understanding. 112(1): 39-54.
• Smith, M.A., Kelly, R.C., Lee, T.S., (2007) Dynamics of response to perceptual pop-out stimuli in macaque V1. J. Neurophysiology 98: 3436-3449.
• Kelly, R.C., Smith, M.A., Samonds, J.M., Kohn, A., Bonds, A.B., Movshon, J.A., Lee, T.S., (2007) Comparison of recordings from microelectrode arrays and single electrodes in visual cortex. J. Neuroscience 27: 261-264. .
• Samonds, J., Potetz, B., Lee, T.S., (2007) Neurophysiological evidence of cooperative mechanisms for stereo computation. NIPS -- Advances in Neural Information Processing Systems 19, 1201-1208, MIT Press.
• Lee, T.S., Yuille, A (2006) Efficient coding of visual scenes by grouping and segmentation: theoretical predictions and biological relevance. in Bayesian Brain, probabilistic approaches to neural coding. Ed. K. Doya, S. Ishii, R. Rao, A. Pougeti. MIT Press, 141-185.
• Potetz, B., Lee, T.S. (2006) Scaling Laws in Natural Scenes and the Inference of 3D Shape. NIPS -- Advances in Neural Information Processing Systems 18, 1089-1096, MIT Press .
• Yu, Y., Romero, R., Lee, T.S. (2005) Preference of sensory neural coding for 1/f signals. Physics Review Letters, 94 , 108103, 1-4.
• Yu, Y., Lee, T.S. (2005) Adaptive contrast gain control and information maximization Neurocomputing, 65-66(2005): 111-116.
• Yu, Y., Potetz, B., Lee, T.S. (2005) The role of spiking nonlinearity in contrast gain control and information transmission. Vision Research, 45(2005): 583-592.
• Stepleton, T., Lee, TS (2005) Using Co-occurrence and Segmentation to Learn Feature-based Object Models from Video. WACV/MOTION 2005 , 129-134
• Deco, D., Lee, T.S. (2004) The role of early visual cortex in visual integration: a neural model of recurrent interaction. European Journal of Neuroscience 20: 1089-1100.
• Kelly, R. and Lee, T.S. (2004) Decoding V1 Neuronal Activity using Particle Filtering with Volterra Kernels. Advances in Neural Information Processing Systems 15, MIT Press. . Ed. Thurn, S., Lawrence, KS, Bernhard, S. 1359-1366.
• Kelly, R. and Lee, T.S. (2004) Decoding visual input based on V1 neuronal activities with particle filtering. Neurocomputing. . 58-60: 849-855.
• Yu, Y. and Lee, T.S. (2004) Nonlinear dynamics of spike generation account for contrast adaptation. Proceedings of the Computational Neuroscience conference. . Spain.
• Yu, Y., Liu, F., Wang W., Lee, T.S. (2004) Optimal synchrony state for maximum information transmission. NeuroReport 15(10): 1605-1610.
• Lee, T.S., Mumford, D. (2003) Hierarchical Bayesian inference in the visual cortex. Journal of Optical Society of America, A. . 20(7): 1434-1448.
• Yu, Y., Lee, T.S. (2003) Dynamical mechanisms underlying contrast gain control in single neurons. Physics Review, E.. 68(1): 1901-1907.
• Lee, T.S. (2003) Neural basis of attentive perceptual organization. . In Perceptual Organization in Vision: Behavioral and Neural Perspectives Ed. M. Behrmann, C. Olson and R. Kimchi, Lawrence Erlbaum Associates, 431-457.
• Lee, T.S. (2003) Analysis and synthesis of visual images in the brain: evidence for Pattern theory. In Mathematical methods in computer vision, Lecture notes in Mathematics and its Application. Ed. P. Olver and A. Tannenbaum. Springer-Verlag, 87-106.
• Potetz, B., Lee, T.S. (2003) Statistical correlations between 2D images and 3D structures in natural scenes. Journal of Optical Socity of America, A. . 20(7): 1292-1303.
• Lee, T.S. (2003) Computations in the early visual cortex. J. Physiology (Paris) , 97(203), 121-139.
• Romero, R.D., Yu, Y., Afshar, P., Lee, T.S. (2003) Adaptation of the temporal receptive fields of Macaque V1 neurons Neurocomputing (52-54): 135-140.
• Yu, Y., Lee, T.S. (2003) Adaptation of the transfer function of the Hodgkin-Huxley (HH) neuronal model. Neurocomputing (52-54): 441-445.
• Lee, T.S., Yang, C., Romero, R.D., and Mumford, D. (2002) Neural activity in early visual cortex reflects behavioral experience and higher order perceptual saliency. Nature Neuroscience 5(6) . 589-597.
• Lee, T.S. (2002) The nature of illusory contour computation. Neuron 33(5) 667-668.
• Lee, T.S. (2002) Top-down influence in early visual processing: A Bayesian perspective. Behaviors and Physiology 77(4-5): 645-650.
• Romero, R., Lee, T.S. (2002). Spike train analysis for single trial data using Hidden Markov Model Neurocomputing 44-46: 597-604, Elsevier Press.
• Deco, G., Lee, T.S. (2002). An unified model of spatial and object attention based on inter-cortical biased competition. Neurocomputing 44-46: 769-774, Elsevier Press
• Yu, S., T.S. Lee, T. Kanade (2002) A hierarchical Markov Random Field Model for figure-ground Segregation Lecture Notes in Computer Science, 2134, pp 118-133, Springer-Verlag.
• Lee, T.S., Nguyen, M. (2001). Dynamics of subjective contour formation in early visual cortex. Proceedings of the National Academy of Sciences, U.S.A. , 98(4) 1907-1911.
• Romero, R.D., Lee T.S. (2001) Estimation of temporal kernels for cells in V1. Proceedings of Annual Conference in Computational Neuroscience .
• Lee, T.S., Yu, S. (2000) An information-theoretic framework for understanding saccadic behaviors. Advances in Neural Information Processing Systems 12: 834-840 , Ed. S.A. Solla, T.K. Leen, K-R. Muller. MIT Press.
• Yu, S., Lee T.S. (2000) What V1 neurons tell us about saccadic suppression. Neurocomputing Elsevier Press, 32-33, 271-277.
• Cassidente, E., Yan X.G, Lee T.S. (2000) A Bayesian decision approach to evaluate local and contextual information from spike trains. Neurocomputing , 32-33, 1013-1020 Elsevier Press.
• Yan, X.G. and Lee, T.S. (2000). Informatics of spike trains in neuronal ensemble. Proceedings of the International Conference of Biomedical Engineering . 5978-655226, 1-6.
• Lee, T.S., D. Mumford, R. Romero and V.A.F. Lamme (1998). The role of primary visual cortex in higher level vision. Vision Research 38, 2429-2454.
• Lee, T.S., D. Mumford, S.C. Zhu and V.A.F. Lamme (1997). The role of V1 in shape representation. Proceedings of the Annual Conference of Computational Neuroscience 96 .
• Lee, T.S. (1996). Image representation using 2D Gabor wavelets. IEEE Transection of Pattern Analysis and Machine Intelligence. Vol. 18, No. 10, October, 959-971.
• Lee, T.S. (1995). Neurophysiological evidence for image segmentation and medial axis computation in primate V1. Computational Neuroscience . (Ed. Jim Bower). Academic Press. 373-378.
• Lee, T.S. (1995). A Bayesian framework for understanding texture segmentation in the primary visual cortex. Vision Research 35, 2643-2657..
• Zhu, S.C., Lee, T.S., and Yuille, A. (1995). Region competition: unifying snakes, region growing and MDL for image segmentation. Proceedings of the Fifth International Conference in Computer Vision 416-425.
• Lee, T.S. (1994). Representational strategy in the visual cortex. Proceedings of First International Conference in Image Processing, Texas,1994. 2: 590-595.
• Lee, T.S., D. Mumford, A. Yuille (1992). Texture segmentation by minimizing vector-valued energy functionals: the coupled-membrane model. Lecture Notes in Computer Science 588, 165-173, Springer-Verlag.