Some Invited Talks


  1. Keynote at CVPR Workshop on Causality in Vision, June, 2021.
    Perceptual Causality: Learning and Inferring Causality from Visual Observations and Interactions
  2. Keynote at China3D Vision Symposium, June, 2021.
    Task-oriented 3D Scene Understanding
  3. Keynote at CICAI CAAI International Conference on AI, June, 2021.
    Explainable AI: How Machines Gain Justified Human Trust
  4. Keynote at ACL Asia, December, 2020
    Explainable AI: How Machines Gain Justified Human Trust
  5. Keynote at Beijing Academy of AI, October, 2019.
    Towards General AI: From "Big Data" to "Big Task"
  6. Keynote at World AI Conference, Shanghai, August, 2019
    Dark, Beyond Deep: a Paradigm Shift in AI Research
  7. Keynote at CVPR Workshop on Explainable AI, Long Beach, CA, June 2019
    Explainable AI: How Machines Gain Justified Trust from Humans
  8. Openning Speech at CVPR Workshop on 3D Scene Understanding, Long Beach ,CA, June 2019
    Some Thoughts and Principles for 3D Scene Understanding
  9. Keynote at CVPR Workshop on vision Meets Cognition, Long Beach ,CA, June 2019
    VR Platforms for Training and Evaluating Autonomous Agents: Small-Data for Big-Tasks
  10. Keynote at ACM TURC Conference, Chengdu, May, 2019
    Artificial Intelligence: The Era of Big Integration
  11. Keynote at Tsinghua AI Summit, Sanya Math Forum, March, 2019
    Explainable AI: How Machines Gain Justified Trust from Humans
  12. Lecture at the Symposium Honoring David Mumford, CMSA, Harvard University, August, 2018
    Artificial Intelligence: The Era of Big Integration
  13. Lecture at the Rama Chellapa 65 Birthday Symposium, June, 2018
    Building a "telescope" to Look Into Very High-Dimensional Image Universe
  14. Distinguished Lecture at Hongkong PolyU, June, 2018
    Artificial Intelligence: The Era of Big Integration
  15. Keynote at Int'l Conf. on Image Processing, Beijing, September, 2017
    A Tale of Three Probabilistic Families: Descriptive, Generative and Discriminative Models [ppt slides]
  16. Symposium at NLPR, Beijing,September, 2017
    Dark, Beyond Deep: A Paradigm Shift for Computer Vision
  17. Workshop on Human-Machine Interaction, Beijing,September, 2017
    A Conitive Architecture for Human-Machine Teaming.
  18. Keynote at Int'l Workshop on Vision Meets Cognition, at CVPR, Hawaii, June, 2017.
    Dark, Beyond Deep
  19. Invited Talk at the Int'l Workshop on Vision and Language, at CVPR, Hawaii, June, 2017.
    Vision + Language: Why and How
  20. Invited Talk at Microsoft Faculty Summit, Seattle, June 2017.
    Dark, Beyond Deep
  21. Invited Talk at Hongkong Chinese University, June, 2017.
    Dark, Beyond Deep
  22. Invited Talk at SNAP Inc. April, 2017.
    Advanced Topics in Vision and AI Research
  23. Special Workshop for Zhu and associates, Microsoft Research Asia, September, 2016.
    A Cognitive Architecture for Human-Robot Teaming
  24. Computer Science Coloquium, Princeton University, Nov. 2015.
    A Cognitive Architecture for Human-Robot Collaborations based on Spatial, Temporal and Causal And-Or Graph.
  25. Keynote at the AI symposium on Human-Robot Interactions, Nov. 2015.
    A Cognitive Architecture for Human-Robot Collaborations based on Spatial, Temporal and Causal And-Or Graph.
  26. CVPR workshop on Vision Meets Cognition : Function, Physics, Intent and Causality, June 2015.
    Rethink Vision from the Perspective of an Agent: Task-centered Representation, inference and learning. [ppt slides]
  27. CVPR workshop on Language and Vision , June. 2015.
    Joint Video-Text Parsing for Understanding Scenes and events and Query Answering [ppt slides]
  28. CogSci Workshop on Physical and Social Scene Understanding, July 2015.
    Understanding Scenes and Events by Reasoning Physics and Causality.
  29. Distinguished Lecture series, Dept. of Computer Sceience and Engineering, UCSD, Nov. 2014.
    Scene Understanding by Reasoning Functionality, Physics, Intents and Causality.
  30. National Cheng Kung University, Taiwan, September 2014.
    Understanding Scenes and Events by Joint Spatial, Temporal, and Causal Inference
  31. Academia Sinica, Taiwan, September, 2014
    Lecture 1: Pursuing a Unified Statistical Model for Regimes of Image Patterns.
    Lecture 2: Understanding Scenes and Events by Joint Spatial, Temporal, and Causal Inference
  32. National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Science, July 2014
    Beyond What and Where: Reasoning Function, Physics, Intent and Causality [ppt_slides]
  33. Institute of AI and Robotics, Xi'an Jiaotong University, July 2014
    Beyond What and Where: Reasoning Function, Physics, Intent and Causality
  34. CVPR workshop on Vision Meets Cognition: Function, Physics, Intent and Causality, June 2014
    Beyond What and Where: Spatial, Temporal and Causal Parsing with Commonsense Knowledge
  35. Stanford Workshop on AI and Knowledge, April, 2014.
    Understanding Video and Text by Joint Spatial, Temporal, and Causal Inference [ppt_slides]
  36. UC Berkeley Vision Seminar, April, 2014.
    Beyond What and Where: Joint Video-Text Inference with Commonsense Reasoning
  37. Computer Science and Engineering Department Distinguished Speaker series, SUNY Buffalo, Feb. 2014.
    Understanding Video and Text by Joint Spatial, Temporal, and Causal Inference
  38. Center for Image Science, Johns Hopkins University, Feb. 2014.
    Pursuing a Unified Representsation and Model of Visual Knowledge
  39. Scene Understanding workshop at CVPR, June 2013
    Scene Understanding by Inferring the "Dark Matters": Physics, Funcationality, Causality and Minds [ppt_slides]
  40. Third Conference of Tsinghua Sanya International Mathematics Forum, Jan, 2013
    Stochastic Sets and Regimes of Mathematical Models of Images [ppt slides]
  41. Seminar at Peking University, August, 2012
    Understanding images and Video by joint Spatial, Temporal and Causal Inference
  42. Beijing Int'l Summer School on Vision, Cognition and Learning, August, 2012
    Lecture 1: Object and scene representation and parsing [ppt slides]
    Lecture 2: Event parsing and inferring agent’s intents and goals [ppt slides]
    Lecture 3: Open problems and challenges in vision, learning and cognition [ppt slides]
  43. Tutorial at the Stochastic Image Grammar (SiG), June, 2012
    Lecture 1: Spatial AND-OR graph for representing the scene-object-part-primitive hierarchy [ppt slides]
    Lecture 2: Causal AND-OR graphs (C-AOG) for representing the causal-action recursion for reasoning [ppt slides]
    Lecture 3: Information projection for learning S-AOG, T-AOG, C-AOG [ppt slides]
  44. UC Riverside EE Colloqium, January, 2012
    Learning Visual Knowledge by Information Projection [ppt_slides]
  45. UC LA CS seminar, May, 2012
    Spatial, temporal, and Causal Inference for Understabding Image and Video
  46. NSF Workshop on Frontier of Vision, August, 2011.
    Lecture 1: Hack, Math, and Stat [ppt slides ]
    Lecture 2: Visual Conceptualization by Stochastic Sets [ppt slides ]
  47. IPAM Summer School on Probabilistic Models of Cognition, July, 2011.
    Lecture 1: Learning And-Or Graph Representations for Objects and Events
    Lecture 2: Top-down/Bottom-up Inference in And-Or graphs
  48. Microsoft Research Asia, August 2010
    The Joy of Ambiguities in Vision and Visual Arts [ppt_slides]
  49. Seminar at the Redwood Center for Theoretical Neuroscience at UC Berkeley on November 12, 2009
    Information Scaling and Perceptual Transitions in Natural Images and Video [ppt_slides]
  50. Beijing Summer School on Vision, Learning, and Pattern Recognition, July 2009,
    Lecture 1: Pursuing Manifolds in the Universe of Images [pdf]
    Lecture 2: Inference in And-Or Graphs [pdf]
  51. The J.K. Aggarwal Lecture, a plenary speech at the ICPR08, Dec.10, 2008,
    Pursuing Implicit and Explicit Manifolds by Information projection [ pdf ]
  52. Plenary Speech at the Chinese Conference on Pattern Recognition, Oct. Beijing,
  53. Psychology Workshop on Natural Environments Tasks and Intelligence, March, 2008, UT Austin,
    Information Scaling and Manifolds Learning in Natural Images and Video
  54. Computer Vision Distinguished Speaker Series, Univ. of Central Florida, March, 2008,
    Visual Learning and Conceptualization: pursuing manifolds in the universe of images.
  55. NIPS workshop on the Grammar of Vision, Dec. 2007, Van Couver, Canada,
    Object Category by Stochastic Grammar
  56. Object Video Seminar, Nov. 2007,
    Object Recognition in Natural and Aerial Images
  57. Int'l Workshop on Object Categorization, Oct. 2007, Rio, Brazil.
    Object Category Modeling, Learning, and Recognition by Stochastic Grammar [pdf]
  58. MIT Vision Symposium, May, 2007, Cambridge, MA.
    Object Category Modeling, Learning, and Recognition by Stochastic Grammar. [pdf]
  59. Symposium on the Mathematics of Perception, June, 2007. Newport, RI.
    Quest for a Stochastic Grammar of Images. [pdf]
  60. Chinese Academy of Science, Institute of Computing Seminar, Mar. 2007, Beijing.
    Computer Vision and High Performance Computing. [pdf]
  61. ChangJiang Scholar Lectureship, Huazhong University of Science and Technology, Mar. 2007,
    Foundation and Trend in Computer Vision. [pdf]
  62. Workshop on Texture and Natural Image Processing, Jan. 2007, Paris, France.
    Texture, Texton and Primal Sketch: Integrating MRF and Wavelets [pdf]
  63. International Symposium on Vision by Brains and Machines, Nov. 2006, MonteVideo, Uruguay.
    Visual Learning with Implicit and Explicit Manifolds     [pdf]
  64. Dragon Star Lecture Series, July 2006,
    Modeling, Learning, and Conceptualizing Visual Patterns.
  65. Int'l Workshop on Semantic Learning, June 2006,
    Knowledge Representation and Learning Schemes for Large Object Categories [pdf]
  66. IMA workshop on Visual Learning and Recognition, May 2006,
    Advocate for Generative Models. [pdf]
  67. Neyman seminar, Berkeley Statistics Department, April 2005,
    Cluster Sampling and Data-Driven Markov Chain Monte Carlo     [pdf]
  68. Math Science Research Institute, Berkeley, Workshop on Visual Recognition, Mar. 2005,
    Context Sensitive Graph Grammar and Top-down/Bottom-up Inference[pdf]
  69. Math Science Research Institute, Berkeley, Workshop on Low-Middle level Vision, Feb. 2005,
    From Primal Sketch to 2 1/2 D Sketch [pdf]
  70. Math Science Research Institute, Berkeley, Workshop on Introduction to Vision Jan. 2005,
    Seeing as Statistical Inference [pdf]
  71. IPAM workshop on Multiscale Geometric Analysis, 2004
    From Scaling Laws of Natural Images to Regimes of Image Models [pdf]
  72. GRC on Sensory Coding and the Natural Environments, 2004
  73. Technical Univ. of Denmark, 2003 [pdf]
  74. Int'l Workshop on High-Level Knowledge in 3D Modeling and Motion Analysis 2003 [pdf]
  75. Inti'l Workshop on Object Recognition 2003
    Visual Inference by Markov Chain Monte Carlo Methods [pdf]
  76. University of South California, 2003
  77. A talk to the Psychology Dept. at UCLA, "A Math Theory for Texture, Texton, Primal Sketch and Gestalt Fields" [pdf]
  78. Los Alamos National Lab, 2002 [Talk1.pdf], [Talk2.pdf], [Talk3.pdf]
  79. Robotics institute, Carnegie Mellon University, 2002
  80. 1st Cape Cod workshop on Monte Carlo Methods, 2002
  81. 2nd Int'l workshop on Texture, 2002
  82. Interface meeting 2002
  83. Kodak research lab., 2001.
  84. Microsoft Research Beijing, 2001.
  85. Univ. of California, Los Angeles, 2001.
  86. 1st Bayes Vision Workshop, San. Francisco, 2001
  87. The Abdus Salum International Centre for Theoretical Physics, Italy, (Teaching Short Courses), 2000.
  88. Institute for Mathematics and its Applications, 2000.
  89. Microsoft Research, Beijing, 2000.
  90. Pattern Theory Seminar, Brown University, 2000.
  91. AI seminar, Carnegie Mellon University, 2000.
  92. Workshop on Generic Object Recognition, Corfu, Greece, 1999.
  93. School of Mathematics, Georgia Institute of Technology, 1999.
  94. Brown University, 1999.
  95. University of Chicago, 1999.
  96. Centre Int'l De Recontres Math, Marseille, FRANCE. 1998.
  97. Institute of Henri Poincare, Paris, France. 1998.
  98. Inria at Antipolis, FRANCE. 1998.
© S.-C. Zhu
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