publications | Charlie C. Li

2024

What radio waves tell us about sleep!

Hao He, Chao Li, Wolfgang Ganglberger, and 7 more authors

Sleep, Aug 2024

Bib HTML

@article{10.1093/sleep/zsae187,
  author = {He, Hao and Li, Chao and Ganglberger, Wolfgang and Gallagher, Kaileigh and Hristov, Rumen and Ouroutzoglou, Michail and Sun, Haoqi and Sun, Jimeng and Westover, M Brandon and Katabi, Dina},
  title = {{What radio waves tell us about sleep!}},
  journal = {Sleep},
  pages = {zsae187},
  year = {2024},
  month = aug,
  issn = {0161-8105},
  doi = {10.1093/sleep/zsae187},
  url = {https://doi.org/10.1093/sleep/zsae187},
  dimensions = {true},
}

WiOpt

Achieving AoI Fairness in Spatially Distributed Wireless Networks: From Theory to Implementation

Nicholas Jones, Joshua Wornell, Chao Li, and 1 more author

In 22nd International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt 2024), Aug 2024

Bib HTML

@inproceedings{jones2024achieving,
  title = {Achieving AoI Fairness in Spatially Distributed Wireless Networks: From Theory to Implementation},
  author = {Jones, Nicholas and Wornell, Joshua and Li, Chao and Modiano, Eytan},
  booktitle = {22nd International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt 2024)},
  year = {2024},
  dimensions = {true},
}

Zero Field Antiferromagnetically Coupled Skyrmions and their Field-Driven Uncoupling in Composite Chiral Multilayers

In Submission to Advanced Functional Materials, Aug 2024
Assessing Sleep in Pediatric Populations from nocturnal breathing signals

Dina Katabi Chao Li

In Submission, Aug 2024
Uncovering Sleep and Respiratory Biomarkers in Rett Syndrome via Passive At-Home Monitoring

In Submission, Aug 2024

2022

SoftTouch: A Sensor-Placement Framework for Soft Robotic Hands

Chao Li, and Nancy Pollard

In 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids), Aug 2022

Bib HTML

@inproceedings{10000138,
  author = {Li, Chao and Pollard, Nancy},
  booktitle = {2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)},
  title = {SoftTouch: A Sensor-Placement Framework for Soft Robotic Hands},
  year = {2022},
  volume = {},
  number = {},
  pages = {504-511},
  doi = {10.1109/Humanoids53995.2022.10000138},
  dimensions = {true},
}

Exploring mmWave Radar and Camera Fusion for High-Resolution and Long-Range Depth Imaging

Akarsh Prabhakara, Diana Zhang, Chao Li, and 4 more authors

In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Aug 2022

Bib HTML

@inproceedings{9982080,
  author = {Prabhakara, Akarsh and Zhang, Diana and Li, Chao and Munir, Sirajum and Sankaranarayanan, Aswin C. and Rowe, Anthony and Kumar, Swarun},
  booktitle = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  title = {Exploring mmWave Radar and Camera Fusion for High-Resolution and Long-Range Depth Imaging},
  year = {2022},
  volume = {},
  number = {},
  pages = {3995-4002},
  doi = {10.1109/iros47612.2022.9982080},
  dimensions = {true},
}

2021

The Elliott and Connolly Benchmark: A Test for Evaluating the In-Hand Dexterity of Robot Hands

Ryan Coulson, Chao Li, Carmel Majidi, and 1 more author

In 2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids), Aug 2021

Bib HTML

@inproceedings{9555798,
  author = {Coulson, Ryan and Li, Chao and Majidi, Carmel and Pollard, Nancy S.},
  booktitle = {2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids)},
  title = {The Elliott and Connolly Benchmark: A Test for Evaluating the In-Hand Dexterity of Robot Hands},
  year = {2021},
  volume = {},
  number = {},
  pages = {238-245},
  doi = {10.1109/HUMANOIDS47582.2021.9555798},
}

Semi-supervised classification of radiology images with NoTeacher: A teacher that is not mean

Balagopal Unnikrishnan, Cuong Nguyen, Shafa Balaram, and 3 more authors

Medical Image Analysis, Aug 2021

Abs Bib HTML

Deep learning models achieve strong performance for radiology image classification, but their practical application is bottlenecked by the need for large labeled training datasets. Semi-supervised learning (SSL) approaches leverage small labeled datasets alongside larger unlabeled datasets and offer potential for reducing labeling cost. In this work, we introduce NoTeacher, a novel consistency-based SSL framework which incorporates probabilistic graphical models. Unlike Mean Teacher which maintains a teacher network updated via a temporal ensemble, NoTeacher employs two independent networks, thereby eliminating the need for a teacher network. We demonstrate how NoTeacher can be customized to handle a range of challenges in radiology image classification. Specifically, we describe adaptations for scenarios with 2D and 3D inputs, with uni and multi-label classification, and with class distribution mismatch between labeled and unlabeled portions of the training data. In realistic empirical evaluations on three public benchmark datasets spanning the workhorse modalities of radiology (X-Ray, CT, MRI), we show that NoTeacher achieves over 90-95% of the fully supervised AUROC with less than 5–15% labeling budget. Further, NoTeacher outperforms established SSL methods with minimal hyperparameter tuning, and has implications as a principled and practical option for semi-supervised learning in radiology applications.
@article{UNNIKRISHNAN2021102148, title = {Semi-supervised classification of radiology images with NoTeacher: A teacher that is not mean}, journal = {Medical Image Analysis}, volume = {73}, pages = {102148}, year = {2021}, issn = {1361-8415}, doi = {10.1016/j.media.2021.102148}, url = {https://www.sciencedirect.com/science/article/pii/S1361841521001948}, author = {Unnikrishnan, Balagopal and Nguyen, Cuong and Balaram, Shafa and Li, Chao and Foo, Chuan Sheng and Krishnaswamy, Pavitra}, keywords = {Semi-supervised deep learning, Classification, Radiographs, MRI, Mean teacher, Co-training}, dimensions = {true}, }