CV_YIMING

Yiming Zhong

PhD student in Computer Science

Vanderbilt University

About Me

I am a first-year PhD student in Computer Science at Vanderbilt. My current research interest lies on AI for Healthcare, where I am particularly interested in leveraging artificial intelligence to optimize clinical workflows and improve diagnostic accuracy. Before joining Vanderbilt, I received my B.S degree from the Department of Biomedical Engineering at Southern University of Science and Technology (SUSTech) in 2026. Outside of research, I enjoy playing badminton and am trying tennis.

E-mail: yiming.zhong@vanderbilt.edu

Last updated: September 24, 2026

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Interests

AI for Healthcare
Computer Vision
Medical Image Analysis

Education

Bachelor
Southern University of Science and Technology (SUSTech), Shenzhen, China
Exchange student
Nanyang Technological University (NTU), Singapore

Projects

Sleep Staging

Contactless Smart Infant Sleep Monitoring System

Developed a multispectral physiological imaging system for precise, contactless monitoring of infant vital signs, in collaboration with a leading hospital in Wenzhou. Established an interpretable video-based sleep/wake classification model to enhance monitoring accuracy from consumer-grade to clinical-grade standards. Leveraged ECG and PPG signals to extract and validate infant motion metrics for preliminary sleep analysis. Deployed open-source human pose estimation and optical flow algorithms to compute key-point motion features, confirming feasibility of camera-based polysomnography (PSG) for infant sleep staging. Conducted camera-based PSG monitoring on 100 infants to establish normative sleep-stage benchmarks. Extracted limb movement coordination and intensity features from video data, applying SVM and Random Forest classifiers for binary and multi-class sleep-stage classification. Applied DL algorithms (LSTM, Transformer) to improve the accuracy of infant sleep stage classification.

Feb 1, 2025

BCI

SSVEP-based BCI Robotic Car Control System with MATLAB

Collected EEG signals from occipital regions using gold cup electrodes and a Cyton board (250 Hz). Applied Butterworth filters for noise reduction and extracted key EEG components (4–35 Hz). Implemented and compared CCA and FFT methods to decode SSVEP frequencies with high accuracy. Transmitted motion commands via Bluetooth to demonstrate reliable and precise human-machine interaction. Reference: Li, M.; He, D.; Li, C.; Qi, S. Brain–Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance. Brain Sci. 2021, 11, 450. https://doi.org/10.3390/brainsci11040450

Nov 5, 2024

ECG

MATLAB-based ECG Signal Acquisition and Processing

Developed a user-friendly prototype for heart health monitoring, emphasizing real-time analysis and ease of use. Built a differential circuit and analogue filtering system for accurate ECG signal measurement. Integrated the AD8232 Heart Rate Monitor and MATLAB to process ECG signals via an NI USB 6009 module. Configured a MATLAB GUI for real-time sampling, noise removal, and enhanced waveform analysis.

Sep 5, 2024

Seizure

Multidimensional Video-based Contactless Infant Seizure Monitoring

Developed a real-time monitoring and prediction algorithm for infant seizures in collaboration with a a leading tertiary hospital in Guangzhou, aiming to establish a low-cost, contactless detection system to mitigate resource limitations and inconsistencies in seizure diagnosis quality. Preprocessed raw ECG signals to extract heart rate and calculate heart rate variability (HRV). Utilized remote photoplethysmography (rPPG) to extract heart rate and HRV from video data for non-invasive physiological monitoring. Applied optical flow techniques to analyze global and skin-region motion in vEEG videos. Deployed open-source human pose estimation tools to detect infant keypoints and compute motion intensity. Analyzed limb movement intensity using cross-correlation and Pearson correlation coefficients to integrate motion and physiological signals. Introduced a camera-based solution for NICU settings, enabling contactless monitoring of infant motion and vital signs with improved precision and efficiency.

Apr 1, 2024

Publications

BHI 2025

A Pilot Clinical Study to Understand the Relationship between General Movements and Ultra-Short-Term HRV of Neonates

This study synchronously monitors GMs and USTHRV in a neonatal intensive care unit (NICU) to understand their physiological relationship, demonstrating the potential of UST-HRV in neonatal health monitoring.

Oct 29, 2025

EMBC 2025

Camera-based Analysis of Motion Coordination Between Infant Left and Right Limbs: A Clinical Study in NICU.

Infant limb motion coordination analysis

Jul 17, 2025

Education

Bachelor
Southern University of Science and Technology (SUSTech), Shenzhen, China September 2022 – July 2026
Cumulative GPA: 3.79/4.0 | Weighted Average Score: 90.68/100
Thesis on AI for healthcare. Supervised by Prof Wenjin Wang . Presented papers at 2 IEEE conferences (1 in EMBC and 1 in BHI), and 1 TBME journal paper is under review.
Exchange student
Nanyang Technological University (NTU), Singapore August 2025 – December 2025
Courses included:
- Control in Biosystem
- Machine Learning & Optimization for Bioengineers