EU

Meet the Team

Isaac Gerg, Ph.D.

Staff R&D Engineer

Computer Vision

Kitware Remote

Ph.D. in Electrical Engineering
Penn State University

M.S. in Electrical Engineering
Penn State University

B.S. in Computer Engineering
Penn State University

Isaac Gerg

Isaac Gerg is a Staff R&D Engineer working remotely on Kitware’s Computer Vision Team. He is focused on machine learning tasks where he’ll be collaborating with researchers and training algorithms to solve challenging problems.

Isaac is also a co-founder and primary algorithm developer for ASASIN, the advanced synthetic aperture sonar imagining engine. ASASIN was created in 2012 as an image formation code for synthetic aperture sonar (SAS) systems. It has been deployed in situ on embedded hardware in underwater drones, desktop systems for Navy operators, and in the cloud for distributed processing.

Prior to working at Kitware, Issac worked at Penn State’s Applied Research Lab as a staff scientist. He focused on remote sensing but a large portion was specific to undersea remote sensing with synthetic aperture sonar (SAS) on board REMUS 600 unmanned underwater vehicles (UUVs). His tasks included developing custom algorithms for signal processing (beamforming), image processing, object recognition, saliency detection, autofocus (image enhancement), image segmentation, and image compression. This research led him into the area of perception which has been a passion since taking his first undergrad classes in computer vision. During this time, Isaac was invited to take a sabbatical as a visiting scientist at the NATO Centre for Maritime for Research & Experimentation in La Spezia, Italy where he developed deep-learning algorithms for their SAS systems.

Before working at Penn State, Isaac worked at Raytheon as a software engineer, eventually making his way to the algorithms group. He was responsible for developing distributed signal processing algorithms for an HPC system. He obtained his masters while working full-time studying the area of hyperspectral remote sensing.

Isaac received his Ph.D. in electrical engineering from The Pennsylvania State University (Penn State), working under Dr. Vishal Monga. He also received his master’s degree in the same area, focusing on remote sensing, and a bachelor’s degree in computer engineering from Penn State.

Awards

  • 3rd Place in the Student Paper Competition presented by IEEE IGARSS, 2021

  • 2nd Place in the Student Poster Competition presented by IEEE OCEANS, 2021

  • Penn State Applied Research Lab’s Engineering Excellence Award presented by Penn State, 2016

Invited Talks & Media

  • Isaac D Gerg. AI/ML Integration in the Synthetic Aperture Sonar (SAS) Processing Pipeline: A Comprehensive Approach. At Naval Applications of Machine Learning (NAML). March 2024.

  • Isaac D. Gerg. Demystifying AI in Healthcare: An Intro to Large Language Models. At International Pediatric Endosurgery Group, Virtual, United States. June 2023.

  • Isaac D. Gerg, et al. Coupling rendering and generative adversarial networks for artificial sonar image generation. At Synthetic Data for Privacy, Security and Augmentation. Center for Accountable, Responsible and Transparent AI. University of Bath. 2022.

  • Isaac D. Gerg, Daniel C. Brown. The Advanced Synthetic Aperture Sonar Imaging eNgine (ASASIN), a time-domain backprojection beamformer using graphics processing units. At Acoustical Society of America, Hawaii, United States. October 2016.

  • Isaac D. Gerg, Daniel C. Brown. Moving away from the phase center approximation in micronavigation for synthetic aperture sonar. At Acoustical Society of America, October 2016. Hawaii, United States

  • Isaac D. Gerg. New and Open Source Technologies for Next Generation SIGINT Applications. At Raytheon Information Systems & Computing Symposium, Norwood, MA. April 2009.

  • Isaac D. Gerg. Speedy, Unsupervised Hyperspectral Exploitation. At 7th Raytheon Systems and Software Symposium, Tucson, AZ. May 2008.

  • Isaac D. Gerg and Stephen Wagner. High-Speed Interconnect Trade Study for a Real Time Signal Processing System. At 5th Raytheon Systems and Software Symposium, Denver, CO. March 2006.

  • Isaac D. Gerg. Low Cost/Performance Distributed Architecture Evaluation for Use in a Real-Time Signal Processing System. At Raytheon PSTN Mini-Expo, Fort Wayne, IN. October 2005

Professional Associations & Service

  • Session Chair – IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2022

Publications

  1. I. Gerg, D. Cook, and V. Monga, "Deep Adaptive Phase Learning: Enhancing Synthetic Aperture Sonar Imagery Through Learned Coherent Autofocus," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 9517-9532, 2024. [URL]
  2. B. Bingol, I. Gerg, and V. Monga, "Progressive Diffusion Autofocus for Synthetic Aperture Sonar Imagery," in IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 2024.
  3. D. Melamed, C. Johnson, I. Gerg, Chen Zhao, R. Blue, A. Hoogs, Brian Clipp, and P. Morrone, "UNCOVERING BIAS IN BUILDING DAMAGE ASSESSMENT FROM SATELLITE IMAGERY," in IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 2024.
  4. I. Gerg and C. Cotner, "A Perceptual Metric Prior on Deep Latent Space Improves Out-Of-Distribution Synthetic Aperture Sonar Image Classification," in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023. [URL]
  5. I. Gerg and V. Monga, "Deep Multi-Look Sequence Processing for Synthetic Aperture Sonar Image Segmentation," IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-15, 2023. [URL]
  6. T. Hoang, K. Dalton, I. Gerg, T. Blanford, D. Brown, and V. Monga, "Resonant Scattering-Inspired Deep Networks for Munition Detection in 3D Sonar Imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-17, 2023. [URL]
  7. I. Gerg and V. Monga, "Preliminary Results on Distribution Shift Performance of Deep Networks for Synthetic Aperture Sonar Classification," in OCEANS 2022, Hampton Roads, 2022. [URL]
  8. T. Hoang, K. Dalton, I. Gerg, T. Blanford, D. Brown, and V. Monga, "Domain Enriched Deep Networks for Munition Detection in Underwater 3D Sonar Imagery," in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022. [URL]
  9. I. Gerg and V. Monga, "Synthetic Aperture Sonar Image Segmentation Using Adaptive, Learned Beam Steering," in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022. [URL]
  10. Y. Sun, I. Gerg, and V. Monga, "Iterative, Deep Synthetic Aperture Sonar Image Segmentation," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022. [URL]
  11. I. Gerg and V. Monga, "Structural Prior Driven Regularized Deep Learning for Sonar Image Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022. [URL]
  12. I. Gerg and V. Monga, "A Learnable Image Compression Scheme for Synthetic Aperture Sonar Imagery," in OCEANS 2021: San Diego – Porto, 2021. [URL]
  13. Y. Sun, I. Gerg, and V. Monga, "Iterative, Deep, and Unsupervised Synthetic Aperture Sonar Image Segmentation," in OCEANS 2021: San Diego – Porto, 2021. [URL]
  14. I. Gerg and V. Monga, "Real-Time, Deep Synthetic Aperture Sonar (SAS) Autofocus," in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021. [URL]
  15. I. Gerg, D. Williams, and V. Monga, "Data Adaptive Image Enhancement and Classification for Synthetic Aperture Sonar," in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020. [URL]
  16. D. Brown, I. Gerg, and T. Blanford, "Interpolation Kernels for Synthetic Aperture Sonar Along-Track Motion Estimation," IEEE Journal of Oceanic Engineering, vol. 45, no. 4, pp. 1497-1505, Oct. 2020. [URL]
  17. I. Gerg, D. Brown, S. Wagner, D. Cook, B. O'Donnell, T. Benson, and T. Montgomery, "GPU Acceleration for Synthetic Aperture Sonar Image Reconstruction," in Global Oceans 2020: Singapore – U.S. Gulf Coast, 2020. [URL]
  18. A. Reed, I. Gerg, J. McKay, D. Brown, D. Williamsk, and S. Jayasuriya, "Coupling Rendering and Generative Adversarial Networks for Artificial SAS Image Generation," in OCEANS 2019 MTS/IEEE SEATTLE, 2019. [URL]
  19. D. Brown, S. Johnson, I. Gerg, and C. Brownstead, "Simulation and testing results for a sub-bottom imaging sonar," in 177th Meeting of the Acoustical Society of America, 2019. [URL]
  20. D. Williams, R. Hamon, and I. Gerg, "ON THE BENEFIT OF MULTIPLE REPRESENTATIONS WITH CONVOLUTIONAL NEURAL NETWORKS FOR IMPROVED TARGET CLASSIFICATION USING SONAR DATA," in Proceedings of the Underwater Acoustics, 2019. [URL]
  21. J. McKay, I. Gerg, and V. Monga, "Bridging The GAP: Simultaneous Fine Tuning for Data Re-Balancing," in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018. [URL]
  22. J. McKay, I. Gerg, V. Monga, and R. Raj, "What's mine is yours: Pretrained CNNs for limited training sonar ATR," in Proceedings on the Oceans Conference & Exposition, 2017.
  23. I. Gerg, "Moving away from the phase center approximation in micronavigation for synthetic aperture sonar," Journal of the Acoustical Society of America, vol. 140, no. 4_Supplement, pp. 3348-3348, Oct. 2016. [URL]
  24. I. Gerg, "The Advanced Synthetic Aperture Sonar Imaging eNgine (ASASIN), a time-domain backprojection beamformer using graphics processing units," Journal of the Acoustical Society of America, vol. 140, no. 4_Supplement, pp. 3347-3347, Oct. 2016. [URL]
  25. I. Gerg, "An evaluation of three endmember extraction algorithms: ATGP, ICA-EEA & VCA," in 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010. [URL]

Bibliography generated 2024-09-16-12:00:06 (7224)