Charles Law

Charles Law

Distinguished Engineer

C. Charles Law received a Ph.D. in neuroscience from Brown University in 1995, and an B.S. in Electrical Engineering and Mathematics from Carnegie Mellon University in 1989. His interests include visualization of adaptive mesh refinement (AMR) simulation data, application of large-data methods to neuroscience research and digital pathology.

Dr. Law is currently principal investigator of a DOE SBIR titled “Multi-Resolution Streaming for Remote Scalable Visualization” and is also principal investigator of an NIH SBIR award, “Scalable computational tools for reverse engineering neural circuits from histology.”

  1. 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.
  2. C. Law, R. Blue, D. Stoup, P. Tunison, A. Hoogs, B. Vasu, J. Van Cor, J. Kerekes, A. Savakis, T. Rovito, C. Stansifer, and S. Thomas, "Deep Learning for Object Detection and Classification in Satellite Imagery," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2018.
  3. S. Han, A. Fafard, J. Kerekes, M. Gartley, E. Ientilucci, A. Savakis, C. Law, J. Parhan, M. Turek, K. Fieldhouse, and T. Rovito, "Efficient generation of image chips for training deep learning algorithms," in SPIE Defense + Security, 2017. [URL]
  4. C. Law, J. Parham, M. Dawkins, P. Tunison, D. Stoup, R. Blue, K. Fieldhouse, M. Turek, A. Hoogs, S. Han, A. Farafard, J. Kerekes, E. Lentilucci, M. Gartley, T. Savakis, T. Rovito, S. Thomas, and C. Stansifer, "Deep learning for object detection and object-based change detection in satellite imagery," in Proceedings of the MSS National Symposium on Sensor and Data Fusion, 2017.
  5. M. Audette, D. Rivière, C. Law, L. Ibanez, S. Aylward, J. Finet, X. Wu, and M. Ewend, "Approach-specific multi-grid anatomical modeling for neurosurgery simulation with public-domain and open-source software," in Proceedings of SPIE--the International Society for Optical Engineering, 2011.
  6. W. Jeong, J. Beyer, M. Hadwiger, R. Blue, C. Law, A. Vazquez-Reina, R. Reid, J. Lichtman, and H. Pfister, "Ssecrett and NeuroTrace: Interactive Visualization and Analysis Tools for Large-Scale Neuroscience Data Sets," IEEE Computer Graphics and Applications, vol. 30, no. 3, pp. 58-70, May 2010. [URL]
  7. W. Schroeder, L. Avila, K. Martin, W. Hoffman, and C. Law, The VTK User's Guide. Kitware, 2006.
  8. W. Schroeder, K. Martin, B. Lorensen, L. Sobierajski, R. Avila, and C. Law, Visualization Toolkit: An Object-Oriented Approach to 3D Graphics, 4th. Kitware, 2006.
  9. J. Ahrens, B. Geveci, and C. Law, "ParaView: An End-User Tool for Large-Data Visualization," in The Visualization Handbook. Academic Press, 2005, pp. 717-731.
  10. J. Ahrens, K. Brislawn, K. Martin, B. Geveci, C. Law, and M. Papka, "Large-scale data visualization using parallel data streaming," IEEE Computer Graphics and Applications, vol. 21, no. 4, pp. 34-41, 2001. [URL]
  11. K. Martin, S. Erne, C. Law, S. Conforto, J. Mallick, and B. Tatar, "Multiple Modality Biomagnetic Analysis System," in Biomag 96. Springer New York, 2000, pp. 1146-1149. [URL]
  12. C. Law, W. Schroeder, K. Martin, and J. Temkin, "A multi-threaded streaming pipeline architecture for large structured data sets," in Proceedings Visualization '99 (Cat. No.99CB37067), 1999. [URL]
  13. C. Law, L. Sobierajski Avila, and W. Schroeder, "Application of path planning and visualization for industrial-design and maintainability-analysis," in Annual Reliability and Maintainability Symposium: International Symposium on Product Quality and Integrity, 1998. [URL]

Bibliography generated 2020-02-18-14:00:09 (3569)