publications | Charlie C. Li

2024

What radio waves tell us about sleep!

Hao He*, Chao Li*, Wolfgang Ganglberger, Kaileigh Gallagher, Rumen Hristov, Michail Ouroutzoglou, Haoqi Sun, Jimeng Sun, M Brandon Westover, and Dina Katabi

In Sleep, August 2024

ISSN: 0161-8105

Bib HTML PDF

@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 = {08},
                doi = {10.1093/sleep/zsae187},
                url = {https://doi.org/10.1093/sleep/zsae187},
                eprint = {https://academic.oup.com/sleep/advance-article-pdf/doi/10.1093/sleep/zsae187/59634046/zsae187.pdf}}

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

Nicholas Jones, Joshua Wornell, Chao Li, and Eytan Modiano

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

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Uncovering Sleep and Respiratory Biomarkers in Rett Syndrome via Passive At-Home Monitoring

Chao Li, Krishna Pranav, Monica Coenraads, Lieberman, David, Jana Von Hehn, Randall Carpenter, Coughlin, Michelle, Dina Katabi

In Preparation for Submission
Assessing Sleep in Pediatric Populations from nocturnal breathing signals

Chao Li, Hao He, Dina Katabi

In Preparation for Special Issue Submission
Zero Field Antiferromagnetically Coupled Skyrmions and their Field-Driven Uncoupling in Composite Chiral Multilayers

May Inn Sim, Dickson Thian, Ramu Maddu, Xiaoye Chen, Hang Khume Tan, Chao Li, Pin Ho, Anjan Soumyanarayanan

In Submission to Advanced Functional Materials

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), 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), 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), 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, 2021

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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}, }