NETS Team | NEural TransmissionS (NETS) Lab

The NETS Lab was established in 2021 and is led by Dr. Ryan T. White. The team consists of 4 PhD students, 2 MS students, and numerous undergraduates, but we collaborate with many others!

Meet the Team

Assistant Professor, Department of Mathmematical Sciences

rwhite@fit.edu

Research: Deep learning, computer vision, edge computing, natural language processing, probability, stochastic analysis, and statistics. Projects have spanned astronautics, aerospace engineering, biomedical engineering, genetics, biology, ecology, glaciology.

Background: PhD Applied Mathematics (2015) at Florida Tech, AI/ML and algorithm development, stochastic analysis.

Recent Works: See Dr. White's Google Scholar profile

mattzs2012@my.fit.edu

Research: Efficient object tracking algorithms for edge hardware

Background: Aerospace engineering

Recent Works:

M. N. Attzs, T. Mahendrakar, M. Meni, R. T. White, and I. Silver (2023). A comparison of tracking-by-detection algorithms for real-time satellite component tracking. 37th Annual Small Satellite Conference. https://digitalcommons.usu.edu/smallsat/2023/all2023/225/

T. Mahendrakar, M. N. Attzs, J. M. Duarte, A. L. Tisaranni, R. T. White, and M. Wilde. Impact of intra-class variance on YOLOv5 model performance for autonomous navigation around non-cooperative targets. AIAA SCITECH 2023 Forum. https://arc.aiaa.org/doi/abs/10.2514/6.2023-2374

vnguyen2014@my.fit.edu

Research: Generative AI: synthetic electronic health record generation, computer vision, NLP

Background: Medical data analytics at Truveta, NLP engineer with CSE department

Recent Works:

V. M. Nguyen, E. Sandidge, T. Mahendrakar, and R. T. White. Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting. https://arxiv.org/abs/2401.02588
B. Nagda, V. M. Nguyen, and R. T. White. Hard Pattern Mining and Ensemble Learning for Detecting DNA Promoter Sequences. IEEE/ACM Transactions on Computational Biology and Bioinformatics. https://ieeexplore.ieee.org/abstract/document/10342761
A. Tikayat Ray, A. P. Bhat, R. T. White, V. M. Nguyen, O. J. Pinon Fischer, D. and N. Mavris (2023). Examining the Potential of Generative Language Models for Aviation Safety Analysis: Case Study and Insights Using the Aviation Safety Reporting System (ASRS). Aerospace. 2023; 10(9):770. https://doi.org/10.3390/aerospace10090770

B. Caruso, T. Mahendrakar, V. M. Nguyen, R. T. White, and T. Steffen (2023). 3D reconstruction of noncooperative resident space object using Instant NeRF and D-NeRF. 33rd AAS/AIAA Space Flight Mechanics Meeting. https://arxiv.org/abs/2301.09060

J. H. Dshalalow, V. M. Nguyen, R. R. Sinden, and R. T. White (2022). Determination of Mutation Rates with Two Symmetric and Asymmetric Mutation Types. Symmetry, 14(8):1701. https://www.mdpi.com/2073-8994/14/8/1701

mmeni2021@my.fit.edu

Research: Information theoretic analysis of neural models, explainable AI, detecting bias

Background: Software development at USGS, ML researcher at US Army Corps of Engineers, Data Scientist in private sector

Recent Works:

M. J. Meni, R. T. White, M. L. Mayo, and K. Pilkiewicz (2024). Entropy-based Guidance of Deep Neural Networks for Accelerated Convergence and Improved Performance (Submitted). Preprint: https://arxiv.org/abs/2308.14938
M. Mattei, M. J. Meni, R. Hillner, E. Christ, K. Chandra, V. Chivukula, and R. T. White (2024). Evaluating Performance of Physics-Informed Neural networks for Predicting 3D Blood Flow Patterns. (Submitted).
K. Taylor, M. J. Meni, and R. T. White (2024). PEEKing into the Universe. Conference for Undergraduate Women in Physics (CUWiP). Poster presentation.
M. J. Meni, T. Mahendrakar, R. T. White, M. L. Mayo, and K. Pilkiewicz (2024). Taking a PEEK into YOLOv5 for Satellite Component Recognition via Entropy-based Visual Explanations. AIAA SCITECH Forum 2024. https://arc.aiaa.org/doi/abs/10.2514/6.2024-2766
M. J. Meni and R. T. White. Information-Informed Neural Networks: Probabilistically Interpretable Neural Decisionmaking. Invited talk at Frontiers in Geoscience Seminar, Earth and Environmental Sciences (EES) division and the Center for Space and Earth Science (CSES), Los Alamos National Lab, Los Alamos, NM, Aug 13, 2023.
M. J. Meni, K. Daust, T. Jia, and R. T. White (2023). Wind Forecasting with Recurrent Neural Networks. PIMS Workshop on Forecasting and Mathematical Modeling for Renewable Energy. (Poster presentation).
M. N. Attzs, T. Mahendrakar, M. J. Meni, R. T. White, and I. Silver (2023). A comparison of tracking-by-detection algorithms for real-time satellite component tracking. 37th Annual Small Satellite Conference. https://digitalcommons.usu.edu/smallsat/2023/all2023/225/

(Major collaborator from the Autonomy Lab)

tmahendrakar2020@my.fit.edu

Research: On-orbit servicing, autonomous proximity operations, guidance and navigation

Background: Aerospace Engineering at NASA Jet Propulsion Laboratory, Collins Aerospace, and Delta Air Lines

Recent Works:

T. Mahendrakar, R. T. White, M. Wilde, and M. Tiwari (2024). Real-time, On-board Spacecraft Feature Detection with Lightweight CNNs. Submitted.
V. M. Nguyen, E. Sandidge, T. Mahendrakar, and R. T. White (2024). Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting. Submitted. https://arxiv.org/abs/2401.02588
M. J. Meni, T. Mahendrakar, R. T. White, M. L. Mayo, and K. Pilkiewicz (2024). Taking a PEEK into YOLOv5 for Satellite Component Recognition via Entropy-based Visual Explanations. AIAA SCITECH Forum 2024. https://arc.aiaa.org/doi/abs/10.2514/6.2024-2766
M. N. Attzs, T. Mahendrakar, M. Meni, R. T. White, and I. Silver (2023). A comparison of tracking-by-detection algorithms for real-time satellite component tracking. 37th Annual Small Satellite Conference. https://digitalcommons.usu.edu/smallsat/2023/all2023/225/
T. Mahendrakar, R. T. White, M. Wilde, and M. Tiwari. SpaceYOLO: A Human-Inspired Model for Real-time, On-board Spacecraft Feature Detection. IEEE Aerospace Conference 2023. https://arxiv.org/abs/2302.00824
A. Ekblad, T. Mahendrakar, R. T. White, M. Wilde, I. Silver, B. Wheeler. Resource-constrained FPGA Design for Satellite Component Feature Extraction. IEEE Aerospace Conference 2023. https://arxiv.org/abs/2301.09055
B. Caruso, T. Mahendrakar, V. M. Nguyen, R. T. White, Todd Steffen. 3D Reconstruction of Non-cooperative Resident Space Objects using Instant NGP-accelerated NeRF and D-NeRF. AAS/AIAA Spaceflight Mechanics Conference 2023. https://arxiv.org/abs/2301.09060
T. Mahendrakar, S. Holmberg, A. Ekblad, E. Conti, R. T. White, M. Wilde, and I. Silver. Autonomous Rendezvous with Non-cooperative Target Objects with Swarm Chasers and Observers. AAS/AIAA Spaceflight Mechanics Conference 2023. https://arxiv.org/abs/2301.09059
T. Mahendrakar, M. N. Attzs, J. M. Duarte, A. L. Tisaranni, R. T. White, and M. Wilde. Impact of Intra-class Variance on YOLOv5 Model Performance for Autonomous Navigation around Non-Cooperative Targets. AIAA SCITECH Forum 2023. https://doi.org/10.2514/6.2023-2374
T. Mahendrakar, A. Ekblad, N. Fischer, M. Wilde, R. T. White, B. Kish, and I. Silver. Performance Study of YOLO V5 and Faster RCNN for Autonomous Navigation around Non-Cooperative Targets. IEEE Aerospace Conference 2022. https://doi.org/10.1109/AERO53065.2022.9843537

T. Mahendrakar, R. T. White, M. Wilde, A. Rivikin, J. Cutler, K. Watkins, A. Ekblad, and N. Fischer. Use of artificial intelligence for feature recognition and flightpath planning around non-cooperative resident space objects. AIAA ASCEND 2021. https://arc.aiaa.org/doi/abs/10.2514/6.2021-4123

T. Mahendrakar, R. T. White, and M. Wilde (2021). Real-time satellite component recognition YOLO V5. 35th Annual Small Satellite Conference. https://digitalcommons.usu.edu/smallsat/2021/all2021/51/

Research: Physics guided neural networks for bloodflow modeling, entropy-based analysis of neural networks

Background: Object detection, edge hardware, swarms

Research: multi-view, 3D object detection

Research: Spacecraft feature detection, hardware-in-the-loop flight tests, natural language processing

Recent Works:

T. Mahendrakar, S. Holmberg, A. Ekblad, E. Conti, R. T. White, M. Wilde, and I. Silver. Autonomous Rendezvous with Non-cooperative Target Objects with Swarm Chasers and Observers. AAS/AIAA Spaceflight Mechanics Conference 2023. https://arxiv.org/abs/2301.09059

Research: computer vision in astronomy, information theoretic analysis of neural networks

Recent work:

K. Taylor, M. J. Meni, and R. T. White (2024). PEEKing into the Universe. Conference for Undergraduate Women in Physics (CUWiP). Poster presentation.

Research: computer vision in space, 3D rendering

Recent work:

V. M. Nguyen, E. Sandidge, T. Mahendrakar, and R. T. White (2024). Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting https://arxiv.org/abs/2401.02588

Research: geophysical signals processing

Research: geophysical signals processing

Research: Analysis of inflation dependence of global development data

Research: Bloodflow modeling

Former Students

Gone but not forgotten! :)

aekblad2019@my.fit.edu

Research: Computer vision, FPGAs

Recent Works:

A. Ekblad, T. Mahendrakar, R. T. White, M. Wilde, I. Silver, B. Wheeler. Resource-constrained FPGA Design for Satellite Component Feature Extraction. To appear at IEEE Aerospace Conference 2023. https://arxiv.org/abs/2301.09055

T. Mahendrakar, S. Holmberg, A. Ekblad, E. Conti, R. T. White, M. Wilde, and I. Silver. Autonomous Rendezvous with Non-cooperative Target Objects with Swarm Chasers and Observers. AAS/AIAA Spaceflight Mechanics Conference 2023. https://arxiv.org/abs/2301.09059

T. Mahendrakar, A. Ekblad, N. Fischer, M. Wilde, R. T. White, B. Kish, and I. Silver. Performance Study of YOLO V5 and Faster RCNN for Autonomous Navigation around Non-Cooperative Targets. IEEE Aerospace Conference 2022. https://doi.org/10.1109/AERO53065.2022.9843537

T. Mahendrakar, A. Rivikin, J. Cutler, K. Watkins, A. Ekblad, N. Fischer, R. T. White, and M. Wilde. Use of artificial intelligence for feature recognition and flightpath planning around non-cooperative resident space objects. AIAA ASCEND 2021. https://arc.aiaa.org/doi/abs/10.2514/6.2021-4123

Research: Computer vision, spacecraft feature detection, glacier tracking, GIS

Experience: Computer vision contractor for defense applications

Research: glacier tracking, satellite component recognition