Christopher Funk

Christopher Funk

Senior R&D Engineer

Dr. Christopher Funk obtained his Ph.D. in Computer Science and Engineering with a focus on computer vision and machine learning from Penn State University in the fall of 2018 under Professor Yanxi Liu. He is interested in artificial intelligence and in striving to understand intelligence. More specifically, his research interests include machine learning, human and machine perception, deep learning, and semi-supervised learning.

Dr. Funk has researched deep learning/machine learning, symmetry detection/mid-level vision, crowdsourced data collection, human pose estimation, object/fine-grained attribute recognition, understanding similarities in human and machine cognition, and statistically-validated evaluation. He has worked in multiple modalities, including image, video, remote sensing, motion capture, foot pressure, and medical data (e.g. electroencephalogram (EEG)).  Dr. Funk helped in the running of the conference on Computer Vision and Pattern Recognition 2017 (CVPR), and he has helped run the symmetry competition workshop at the International Conference on Computer Vision 2017 (ICCV). His work has been published in ICCV and CVPR proceedings.

Dr. Funk will always consider himself a New Yorker from the city since growing up there.  He has a bachelor’s degree in Government from Franklin & Marshall and a master’s degree in Computer Science from Pace University, where he was a teaching assistant for Computer Vision, Pattern Recognition and Machine Learning, Computer Graphics, and Computational Symmetry courses.  He also worked with many machine learning tasks such as texture transfer and synthesis using classical methods and generative adversarial networks (GANs).  For his master’s degree, he worked with cognitive architectures such as Soar and physics engines like PhsyX in attempting to create a cognitive approach to computer vision.

Dr. Funk now works for Kitware, Inc on projects such as DARPA’s Learning with Less Labels and DARPA’s SAIL-ON Novelty Detection as well as other remote sensing projects.   Most recently, he has created a united framework for the DARPA and other projects to simplify researchers’ integration with evaluators’ test harnesses and is being applied to a few different projects.   

  1. J. Scott, B. Ravichandran, C. Funk, R. Collins, and Y. Liu, "From Image to Stability: Learning Dynamics from Human Pose," in European Conference on Computer Vision 2020, 2020.
  2. C. Funk, J. Crall, W. Hicks, C. Law, P. Tunison, R. Blue, A. Hoogs, T. Rovito, and A. Maltenfort, "WEFT Feature Detection and Mensuration for Airplane Classification in Satellite Imagery," in MSS National Symposium on Sensor and Data Fusion, 2019.
  3. C. Funk, S. Nagendra, J. Scott, B. Ravichandran, J. Challis, R. Collins, and Y. Liu, "Learning Dynamics from Kinematics: Estimating 2D Foot Pressure Maps from Video Frames," arXiv preprint arXiv:1811.12607, Nov. 2018. [URL]
  4. C. Funk, S. Lee, M. Oswald, S. Tsogkas, W. Shen, A. Cohen, S. Dickinson, and Y. Liu, "2017 ICCV Challenge: Detecting Symmetry in the Wild," in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017. [URL]
  5. J. Scott, R. Collins, C. Funk, and Y. Liu, "4D Model-Based Spatiotemporal Alignment of Scripted Taiji Quan Sequences," in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017. [URL]
  6. C. Funk and Y. Liu, "Beyond Planar Symmetry: Modeling Human Perception of Reflection and Rotation Symmetries in the Wild," in Proceedings of the IEEE International Conference on Computer Vision, 2017. [URL]
  7. C. Funk and Y. Liu, "LabelMeSymmetry: a tool for human symmetry perception," Journal of Vision, vol. 16, no. 12, pp. 306, Sep. 2016. [URL]
  8. C. Funk and Y. Liu, "Symmetry reCAPTCHA," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. [URL]

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