Christopher Funk

Christopher Funk

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. He is interested in artificial intelligence and in striving to understand intelligence. More specifically, his research interests include machine learning, human and machine perception, and deep learning. 

Dr. Funk has completed research in crowdsourced data collection, deep learning/machine learning, human pose estimation and evaluation, and symmetry detection. He has worked in multiple modalities, including image data, motion capture data, foot pressure, and medical data (e.g. electroencephalogram (EEG)).

After growing up in New York City, Dr. Funk will always consider himself a New Yorker from the city. 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. 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. He also worked with many machine learning tasks such as texture transfer and synthesis using classical methods and generative adversarial networks (GANs).

In 2017, Dr. Funk helped execute the conference on Computer Vision and Pattern Recognition (CVPR), and he helped run the symmetry competition workshop at the International Conference on Computer Vision (ICCV). His work has been published in ICCV and CVPR proceedings. 

  1. 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]
  2. 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]
  3. 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]
  4. C. Funk and Y. Liu, "LabelMeSymmetry: a tool for human symmetry perception," Journal of Vision, vol. 16, no. 12, pp. 306, Sep. 2016. [URL]
  5. 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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