EU

Meet the Team

Prudhvi Gurram, Ph.D.

Staff R&D Engineer

Computer Vision

Kitware Remote

Ph.D. in Imaging Science
Rochester Institute of Technology

M.S. in Electrical Engineering
Rochester Institute of Technology

B.E. in Electronics and Communication Engineering
National Institute of Technology Karnataka

Profile siloutte with hexagons in the background.

Prudhvi Gurram, Ph.D. is a staff R&D engineer working remotely on Kitware’s Computer Vision Team. He develops robust solutions in object-based detection, feature detection, motion pattern learning, and anomaly detection. His research interests include representation learning and generative modeling to build semantically meaningful feature spaces and concepts for downstream higher-level reasoning, novelty detection, and interpretability/explainability of ML systems.

Prudhvi has extensive experience applying artificial intelligence/machine learning (AI/ML) to advance computer vision capabilities. Prior to joining Kitware, he was a member of the senior technical staff at The Johns Hopkins University Applied Physics Laboratory (APL). He worked on applied artificial intelligence/machine learning (AI/ML) research for perception and reasoning in autonomous systems. He also served as the project lead on a program involving context-aware multi-object tracking in full motion video. He was part of other projects related to overhead imagery analytics and causal reasoning for AI-enabled medical assistants. Before his time at APL, Prudhvi was a senior lead engineer at Booz Allen Hamilton from 2016 to 2020. He led a team of AI/ML researchers performing R&D efforts in image and video analytics, scene perception for autonomous and intelligent systems, and federated machine learning. From 2009 to 2016, Prudhvi was a research associate with the image processing branch of Army Research Laboratory (ARL). In this role, he worked on ML research for hyperspectral image analysis, object detection, and activity recognition. 

Prudhvi received his Ph.D. in imaging science and his master’s degree in electrical engineering from the Rochester Institute of Technology. He received his bachelor of engineering degree in electronics and communication engineering from the National Institute of Technology Karnataka in India.

Professional Associations & Service

  • Senior Member, IEEE, 2015-present

  • Member, IEEE Signal Processing Society (SPS) and Geoscience and Remote Sensing Society (GRSS), 2009-present

  • Technical Program Committee, Machine Learning for Communications and Networking Track, MILCOM 2022

  • Co-chair and Organizer, IEEE AIPR Special Session on Interpretable AI/ML, 2019

  • Technical Program Committee, International Workshop on Heterogeneous Face Recognition (HFR), 2017 – 2018

  • Technical Program Committee, Video Surveillance and Transportation Imaging Applications Conference, SPIE Electronic Imaging, 2012 – 2014

  • Publications co-chair for the IEEE International Conference on Image Processing (ICIP) 2012

  • Reviewer for numerous IEEE and SPIE Journals and Conferences

Publications

  1. T. Banerjee, P. Gurram, and G. Whipps, "A Bayesian theory of change detection in statistically periodic random processes," IEEE Transactions on Information Theory, vol. 67, no. 4, pp. 2562–2580, 2021.
  2. S. Chakraborty, P. Gurram, F. Le, L. Kaplan, and R. Tomsett, "Augmenting saliency maps with uncertainty," in Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 2021.
  3. A. Parayil, H. Bai, J. George, and P. Gurram, "Decentralized Langevin dynamics for Bayesian learning," Advances in Neural Information Processing Systems, vol. 33, pp. 15978–15989, 2020.
  4. T. Banerjee, P. Gurram, and G. Whipps, "Multislot and multistream quickest change detection in statistically periodic processes," in 2020 IEEE International Symposium on Information Theory (ISIT), 2020.
  5. C. de Melo, B. Rothrock, P. Gurram, O. Ulutan, and B. Manjunath, "Vision-based gesture recognition in human-robot teams using synthetic data," in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.
  6. J. George and P. Gurram, "Distributed stochastic gradient descent with event-triggered communication," in Proceedings of the AAAI Conference on Artificial Intelligence, 2020.
  7. R. Tomsett, D. Harborne, S. Chakraborty, P. Gurram, and A. Preece, "Sanity checks for saliency metrics," in Proceedings of the AAAI conference on artificial intelligence, 2020.
  8. T. Banerjee, P. Gurram, and G. Whipps, "Quickest Event Detection Using Multimodal Data In Nonstationary Environments.," CoRR, 2019.
  9. T. Banerjee, P. Gurram, and G. Whipps, "A sequential detection theory for statistically periodic random processes," in 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2019.
  10. J. George, T. Yang, H. Bai, and P. Gurram, "Distributed stochastic gradient method for non-convex problems with applications in supervised learning," in 2019 IEEE 58th Conference on Decision and Control (CDC), 2019.
  11. T. Banerjee, P. Gurram, and G. Whipps, "Bayesian quickest detection of changes in statistically periodic processes," in 2019 IEEE International Symposium on Information Theory (ISIT), 2019.
  12. T. Banerjee, P. Gurram, and G. Whipps, "Quickest detection of deviations from periodic statistical behavior," in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.
  13. T. Banerjee, G. Whipps, P. Gurram, and V. Tarokh, "Sequential event detection using multimodal data in nonstationary environments," in 2018 21st International Conference on Information Fusion (FUSION), 2018.
  14. R. Tomsett, A. Widdicombe, T. Xing, S. Chakraborty, S. Julier, P. Gurram, R. Rao, and M. Srivastava, "Why the failure? how adversarial examples can provide insights for interpretable machine learning," in 2018 21st International Conference on Information Fusion (FUSION), 2018.
  15. T. Banerjee, G. Whipps, P. Gurram, and V. Tarokh, "Cyclostationary statistical models and algorithms for anomaly detection using multi-modal data," in 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018.
  16. S. Hu, N. Short, P. Gurram, K. Gurton, and C. Reale, "MWIR-to-visible and LWIR-to-visible face recognition using partial least squares and dictionary learning," in Face Recognition Across the Imaging Spectrum. Springer, Cham, 2016, pp. 69–90.
  17. P. Gurram, H. Kwon, and C. Davidson, "Coalition game theory-based feature subspace selection for hyperspectral classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 6, pp. 2354–2364, 2016.
  18. T. Wu, P. Gurram, R. Rao, and W. Bajwa, "Clustering-aware structure-constrained low-rank representation model for learning human action attributes," in 2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2016.
  19. S. Hu, N. Short, B. Riggan, C. Gordon, K. Gurton, M. Thielke, P. Gurram, and A. Chan, "A polarimetric thermal database for face recognition research," in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2016.
  20. Z. Peng, P. Gurram, H. Kwon, and W. Yin, "Sparse kernel learning-based feature selection for anomaly detection," IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 3, pp. 1698–1716, 2015.
  21. N. Short, S. Hu, K. Gurton, A. Yuffa, P. Gurram, and G. Videen, "Changing the Paradigm in Human Identification," Optics & Photonics News, vol. 40, 2015.
  22. N. Short, S. Hu, P. Gurram, K. Gurton, and A. Chan, "Improving cross-modal face recognition using polarimetric imaging," Optics letters, vol. 40, no. 6, pp. 882–885, 2015.
  23. N. Short, S. Hu, P. Gurram, and K. Gurton, "Exploiting polarization-state information for cross-spectrum face recognition," in 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2015.
  24. T. Wu, P. Gurram, R. Rao, and W. Bajwa, "Hierarchical union-of-subspaces model for human activity summarization," in Proceedings of the IEEE International Conference on Computer Vision Workshops, 2015.
  25. N. Short, S. Hu, and P. Gurram, "Dimensionality analysis of facial signatures in visible and thermal spectra," in Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, 2015.
  26. P. Gurram and H. Kwon, "Optimal sparse kernel learning in the empirical kernel feature space for hyperspectral classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 4, pp. 1217–1226, 2014.
  27. B. Park, W. Windham, S. Ladely, P. Gurram, H. Kwon, S. Yoon, K. Lawrence, N. Narang, and W. Cray Jr, "Detection of non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups with hyperspectral microscope imaging technology," Transactions of the ASABE, vol. 57, no. 3, pp. 973–986, 2014.
  28. P. Gurram and R. Rao, "Entropy metric regularization for computational imaging with sensor arrays," in 2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2014.
  29. P. Gurram and H. Kwon, "Coalition game theory based feature subset selection for hyperspectral image classification," in 2014 IEEE Geoscience and Remote Sensing Symposium, 2014.
  30. H. Wu, P. Gurram, H. Kwon, and S. Prasad, "A hybrid csvm-hmm model for acoustic signal classification using a tetrahedral sensor array," in SENSORS, 2014 IEEE, 2014.
  31. S. Hu, P. Gurram, H. Kwon, and A. Chan, "Thermal-to-visible face recognition using multiple kernel learning," in Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 2014.
  32. R. Rao and P. Gurram, "Entropy formulations for signal reconstruction from sensor arrays," in Wireless Sensing, Localization, and Processing IX, 2014.
  33. P. Gurram and H. Kwon, "Contextual SVM using Hilbert space embedding for hyperspectral classification," IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 5, pp. 1031–1035, 2013.
  34. P. Gurram, S. Hu, and A. Chan, "Uniform grid upsampling of 3D lidar point cloud data," in Three-Dimensional Image Processing (3DIP) and Applications 2013, 2013.
  35. P. Gurram, H. Kwon, and T. Han, "Sparse kernel-based hyperspectral anomaly detection," IEEE Geoscience and Remote Sensing Letters, vol. 9, no. 5, pp. 943–947, 2012.
  36. P. Gurram and H. Kwon, "Sparse kernel-based ensemble learning with fully optimized kernel parameters for hyperspectral classification problems," IEEE transactions on geoscience and remote sensing, vol. 51, no. 2, pp. 787–802, 2012.
  37. P. Gurram, H. Rhody, and E. Saber, "Semiautomated system for three-dimensional modeling of buildings from aerial video," Journal of Electronic Imaging, vol. 21, no. 1, pp. 013007, 2012.
  38. B. Park, W. Windham, S. Ladely, P. Gurram, H. Kwon, S. Yoon, K. Lawrence, N. Narang, and W. Cray, "Classification of Shiga toxin-producing Escherichia coli (STEC) serotypes with hyperspectral microscope imagery," in Sensing for Agriculture and Food Quality and Safety IV, 2012.
  39. P. Gurram and H. Kwon, "Support-vector-based hyperspectral anomaly detection using optimized kernel parameters," IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 6, pp. 1060–1064, 2011.
  40. P. Gurram and H. Kwon, "Generalized optimal kernel-based ensemble learning for hyperspectral classification problems," in 2011 IEEE International Geoscience and Remote Sensing Symposium, 2011.
  41. P. Gurram and H. Kwon, "A full diagonal bandwidth gaussian kernel SVM based ensemble learning for hyperspectral chemical plume detection," in 2010 IEEE International Geoscience and Remote Sensing Symposium, 2010.
  42. P. Gurram, E. Saber, and H. Rhody, "A segment-based mesh design for building parallel-perspective stereo mosaics," IEEE transactions on geoscience and remote sensing, vol. 48, no. 3, pp. 1256–1269, 2009.
  43. P. Gurram, H. Rhody, E. Saber, and F. Sahin, "Automated 3D object identification using Bayesian networks," in 2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009), 2009.
  44. P. Gurram, S. Lach, E. Saber, H. Rhody, and J. Kerekes, "3d scene reconstruction through a fusion of passive video and lidar imagery," in 36th Applied Imagery Pattern Recognition Workshop (aipr 2007), 2007.

Bibliography generated 2022-09-13-11:00:07 (5417)