Rodney LaLonde, Ph.D.

Senior R&D Engineer

Computer Vision

Kitware North Carolina
Carrboro, NC

Ph.D. in Computer Science
University of Central Florida

M.Sc. in Computer Science
University of Central Florida

B.A. in Physics, Mathematics, and Computer science
St. Olaf College

Rodney LaLonde

Rodney LaLonde, Ph.D., is a senior R&D engineer on Kitware’s Computer Vision Team located at Carrboro, North Carolina. His work primarily focuses on DIY-AI -related projects, including object re-association and search and fine-grained classification.

Rodney is one of the world-leading experts on Capsule Neural Networks, having completed his Ph.D. dissertation on the Algorithms and Applications of Novel Capsule Networks. Along with several influential publications, such as creating the first-ever capsule network for semantic segmentation (Capsules for Object Segmentation), in 2019 he co-hosted a tutorial on capsule networks at the Conference on Computer Vision and Pattern Recognition (CVPR). His small object detection research was also published at CVPR in 2018, with the resulting code integrated into a trillion-dollar defense platform.

Prior to joining Kitware, Rodney was a machine learning intern for Aptiv, where he worked to design and prototype a single-shot instance segmentation framework for use in their autonomous driving platform. Before his Aptiv internship, Rodney was a computer vision and machine learning intern for Lockheed Martin (LM) in the special projects division of the advanced research team. There, he was involved with several projects, independent research and development (IRaD) proposals, and white papers in topics including object detection and tracking, time-series prediction, 3D scene understanding, and more. He also independently created a weekly tech talk series that covered deep learning and computer vision topics. The series proved popular and had high attendance rates among engineers across all of LM’s business sectors.

While working on his Ph.D. at the University of Central Florida, Rodney worked with his advisor on several large grants which were successfully funded. The largest of these were two NIH R01 Grants each for over $2.5 million. The first of these was related to explainable AI for pancreatic cancer detection and diagnosis. The second was for human-centered AI for lung cancer detection and diagnosis. They also had several other successful grants ranging from $50K to $1.7M on similar topics related to pancreatic or lung cancer.

Rodney received his Ph.D. in computer science from the Center for Research in Computer Vision (CRCV) at the University of Central Florida (UCF). He also received his Master of Science degree in computer science from CRCV at UCF. He received his bachelor’s degree in physics, mathematics, and computer science with distinction from St. Olaf College.

For more information about Rodney and the projects he’s worked on, you can visit his personal website.

Awards

  • Student Travel Award for Top Student Papers presented by the Medical Image Computing and Computer Assisted Interventions Society (MICCAI), 2020

  • Student Travel Award for Top Student Papers presented by the Canadian Institute for Advanced Research (CIFAR), 2018

Professional Associations & Service

  • Student Member of IEEE Computer Society

  • Student Member of IEEE Signal Processing Society

Publications

  1. R. LaLonde, D. Torigian, and U. Bagci, "Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses," in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020.
  2. R. LaLonde, Z. Xu, I. Irmakci, S. Jain, and U. Bagci, "Capsules for biomedical image segmentation," Medical Image Analysis, vol. 68, pp. 101889, Nov. 2020.
  3. R. LaLonde III, "Algorithms and Applications of Novel Capsule Networks," Oral Examination, University of Central Florida, 2020.
  4. S. Laha, R. LaLonde, A. Carmack, H. Foroosh, J. Olson, S. Shaikh, and U. Bagci, "Analysis of Video Retinal Angiography with Deep learning and Eulerian Magnification," Frontiers in Computer Science, Jul. 2020.
  5. R. LaLonde, P. Kandel, C. Spampinato, M. Wallace, and U. Bagci, "Diagnosing Colorectal Polyps in the Wild with Capsule Networks," in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020.
  6. R. LaLonde, I. Tanner, K. Nikiforaki, G. Papadakis, P. Kandel, C. Bolan, M. Wallace, and U. Bagci, "INN: Inflated Neural Networks for IPMN Diagnosis," in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019.
  7. A. Rodriguez, A. Sharma, I. Tanner, P. Kandel, R. LaLonde, D. Livingston, J. Manoj, M. Raimondo, M. Wallace, and U. Bagci, "Sa1342–Brown Adipose Tissue Activation Detected by Artifical Intelligence Assisted Radiomics: An Early Biomarker for Pancreatic Ductal Adenocarcinoma (PDAC)," Gastroenterology, vol. 156, no. 6, pp. S-320, May 2019.
  8. P. Kandel, R. LaLonde, V. Ciofoaia, M. Wallace, and U. Bagci, "Su1741 COLORECTAL POLYP DIAGNOSIS WITH CONTEMPORARY ARTIFICIAL INTELLIGENCE," Gastrointestinal Endoscopy, vol. 89, no. 6, pp. AB403, Jun. 2019.
  9. R. LaLonde and U. Bagci, "Capsules for Object Segmentation," in 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), 2018.
  10. R. LaLonde, D. Zhang, and M. Shah, "ClusterNet: Detecting Small Objects in Large Scenes by Exploiting Spatio-Temporal Information," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018.
  11. H. Du, R. LaLonde, R. van Mechelen, and S. Zhang, "Performing Semantic Segmentation on an Extremely Small Dataset," in Midwest Instruction and Computing Symposium, 2016.

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