Shengtai Ju
Computer Science · Purdue University West Lafayette
Publications
19
Citations
215
Est. group size
—
Recurring co-author estimate
Active years
9
Publishing since 2017
Shengtai Ju's publication record spans two distinct areas: deep learning methods for wireless signal analysis (such as classifying radio modulation types and identifying interference), and computer vision techniques for recognizing human hand-washing actions from video, including work on how hand poses and shadows affect recognition accuracy. There is also a small, apparently unrelated cluster of publications on migraine and acupuncture. This mix suggests contributions to multiple applied machine learning problems rather than a single narrow specialty.
Publication output has been irregular over the last decade, with active years (2019, 2021, 2024) interspersed with gaps (2022) and generally low overall volume, averaging about 1.2 papers per year over the last five years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Systematic acupuncture explains acupuncture at Baihui (GV20) and Fengchi (GB20) targeting the inflammatory response to regulate migraine.
PubMed · 2025
- Exploring the Impact of Hand Pose and Shadow on Hand-Washing Action Recognition
2024
- Exploring the Impact of Hand Pose and Shadow on Hand-washing Action Recognition
arXiv (Cornell University) · 2024
- Shadow Augmentation for Handwashing Action Recognition: from Synthetic to Real Datasets
arXiv (Cornell University) · 2024
- Shadow Augmentation for Handwashing Action Recognition: From Synthetic to Real Datasets
2024
- Ensemble Wrapper Subsampling for Deep Modulation Classification
IEEE Transactions on Cognitive Communications and Networking · 2021
- Efficient Training of Deep Classifiers for Wireless Source Identification Using Test SNR Estimates
IEEE Wireless Communications Letters · 2020
- Fast Deep Learning for Automatic Modulation Classification
arXiv (Cornell University) · 2019
- Deep Learning for Interference Identification: Band, Training SNR, and Sample Selection
2019
- Deep Learning for Interference Identification: Band, Training SNR, and Sample Selection
arXiv (Cornell University) · 2019
- Efficient Training of Deep Classifiers for Wireless Source Identification using Test SNR Estimates
arXiv (Cornell University) · 2019
- A PyTorch Framework for Automatic Modulation Classification using Deep Neural Networks
Purdue e-Pubs (Purdue University System) · 2018
- Deep Neural Network Architectures for Modulation Classification using Principal Component Analysis
Purdue e-Pubs (Purdue University System) · 2018
- Classification and Limits
Headache · 2017
- arXiv (Cornell University)×6
- Electronic Imaging×2
- Purdue e-Pubs (Purdue University System)×2
- IEEE Wireless Communications Letters×1
- IEEE Transactions on Cognitive Communications and Networking×1
- Xinyu Zhou
Computer Science · Indiana University
- Md Habibur Rahman
Engineering · Purdue University West Lafayette
- Mark R. Bell
Engineering · Purdue University West Lafayette
- Jun Chen
Engineering · Purdue University West Lafayette
- Yizhen Jia
Engineering · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
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