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FPGL: A Meta-Learned Implicit Neural Representation Framework for Medical Image Segmentation
Kushal Vyas,
Daniel Kim,
Tucker Netherton,
Ashok Veeraraghavan,
Guha Balakrishnan
Medical Image Analysis (MedIA), 2026
paper
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doi
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bibtex
@article{vyas2026fpgl,
title = {FPGL: A Meta-Learned Implicit Neural Representation Framework for Medical Image Segmentation},
author = {Vyas, Kushal and Kim, Daniel and Netherton, Tucker and Veeraraghavan, Ashok and Balakrishnan, Guha},
journal = {Medical Image Analysis},
volume = {115},
pages = {104316},
year = {2026},
doi = {10.1016/j.media.2026.104316}
}
Meta-learns a shared INR initialization by jointly fitting scans and segmentation maps, so novel subjects can be segmented simply by fine-tuning on their raw test-time observations.
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The Surprising Effectiveness of Noise Pretraining for Implicit Neural Representations
Kushal Vyas,
Alper Kayabasi,
Daniel Kim,
Vishwanath Saragadam,
Ashok Veeraraghavan,
Guha Balakrishnan
CVPR, 2026
project page
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arXiv
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pdf
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bibtex
@inproceedings{vyas2026surprising,
title = {The Surprising Effectiveness of Noise Pretraining for Implicit Neural Representations},
author = {Vyas, Kushal and Kayabasi, Alper and Kim, Daniel and Saragadam, Vishwanath and Veeraraghavan, Ashok and Balakrishnan, Guha},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2026}
}
Pretraining INRs with structured noise yields dramatic signal-fitting improvements. Noise with 1/|fα| spectral structure matches the best data-driven initialization methods.
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Engineered Underwater Vehicle for Ocean Litter Mapping
Daniel Kim
New Mexico Journal of Science, 2024
project page
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video
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code
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bibtex
@article{kim2024oceanai,
title = {Engineered Underwater Vehicle for Ocean Litter Mapping},
author = {Kim, D.},
journal = {New Mexico Journal of Science},
year = {2024}
}
An autonomous underwater vehicle using YOLOv5 and SLAM to map subsurface debris, identifying 174,000+ pieces of litter.
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Developing a Control Algorithm and Simulation for Thrust Vector Controlled Rockets
Daniel Kim,
A. Iturregui
New Mexico Journal of Science, 2022
project page
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video
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bibtex
@article{kim2022tvc,
title = {Developing a Control Algorithm and Simulation for Thrust Vector Controlled Rockets},
author = {Kim, D. and Iturregui, A.},
journal = {New Mexico Journal of Science},
year = {2022}
}
A PID-based control algorithm and 6-DOF flight simulation for model rockets with thrust vector control.
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3D Gaussian Splatting for Learned Drone Navigation
CMU Robotics Institute Summer Scholars (AirLab), 2026
project page
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paper
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poster
A photorealistic, differentiable drone simulator built from 3D Gaussian Splat reconstructions, used to train monocular RGB navigation policies across diverse scenes.
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Ember: Rocket Active Control
project page
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code
A custom Teensy-based flight computer and thrust vector control system that actively stabilizes model rockets during ascent, starting from PID control with Kalman filtering. Ember extends it with a differentiable 6-DOF rocket simulator that trains a neural TVC controller by backpropagating through the physics, then flies the learned policy on the real hardware.
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Alpine
project page
A modular PyTorch library for rapidly prototyping implicit neural representations, with object-oriented interfaces and visualization tools for scientific computing.
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OceanAI
project page
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code
An autonomous underwater glider with a custom YOLOv5 vision pipeline and SLAM navigation that identified over 174,000 debris objects.
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StudyBeats
project page
A real-time productivity platform with focus tools, task sync, and Lo-Fi streaming, serving 1200+ users.
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OrbitSim
project page
An N-body gravity simulator visualizing 8,000+ active satellite trajectories in real time, built on a custom C++ engine with Vulkan rendering.
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