Mr. Weitao Tang

Mr. Weitao Tang

Postgraduate Student
Department of Electrical and Computer Systems Engineering
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Weitao Tang is a Ph.D. candidate in Artificial Intelligence in Medical Technology at Monash University, Australia. Prior to his doctoral studies, he earned a Master of Data Science from Monash University, specializing in Artificial Intelligence in Education, and a Bachelor of Science (Honours) in Computer Science from the University of Nottingham Ningbo China (UNNC), where he focused on Computer Vision.

Currently, he is supported by the Faculty of Engineering International Postgraduate Research Scholarship (FEIPRS) and the Ex Animo Scholarship for Engineering (EASE) at Monash University, with an additional research top-up funded by the U.S. National Institutes of Health (NIH). His research focuses on biomedical signal processing and machine learning, with an emphasis on applying deep learning and transfer learning to fetal electroencephalogram (EEG)🧠, electrocardiogram (ECG) 🫀, and electromyography (EMG) 💪 for sleep state classification 💤 and hypoxia–ischemia detection ⚠️.

He works collaboratively across several institutions, including Emory University and the Georgia Institute of Technology, and is jointly supervised by:

Together, this multidisciplinary team aims to advance AI-driven physiological monitoring, fetal neurodevelopment assessment, and early diagnosis in perinatal medicine.

Qualifications

  • Master of Data Science, Monash University, 2023
  • Bachelor of Science (Honours) in Computer Science, University of Nottingham Ningbo China (UNNC), 2019

Expertise

Biomedical Artificial Intelligence & Perinatal Monitoring

Decoupling complex physiological time-series signals to advance fetal and pediatric health. Focuses on developing deep learning frameworks, transfer learning, and domain adaptation methodologies for automated sleep state classification and early hypoxia-ischemia detection using multimodal signals (EEG, ECG, EMG).

Advanced Data Science & Biomedical Signal Processing

Expertise in biomedical time-series analysis, representation learning (including CNNs, Transformers, and contrastive learning), and graph data mining. Proficient in engineering robust, trustworthy Machine Learning models capable of cross-species and cross-domain generalization.

🎖️ Honors & Awards

  • 🌟 2025 — NextGen Scholar Award (NSF–EMBS–Google Sponsored) Selected as a global recipient of the “Next-Generation Young Scholar” program at IEEE BHI 2025. This prestigious award is jointly sponsored by the U.S. National Science Foundation (NSF), the IEEE Engineering in Medicine and Biology Society (EMBS), and Google, recognizing outstanding early-career researchers with the potential to lead future innovations in biomedical informatics.

 

  • 🏅 2025 — Best Poster Award, IEEE BHI 2025 (Atlanta, USA)
    Recognized by the conference committee for the work: “Fetal Sleep State Classification Using Deep Learning,” selected for its technical excellence and impact on fetal health monitoring.

 

  • 🇦🇺 2023 — Stephen FitzGerald Scholar
    I was selected as one of the national recipients of the prestigious Stephen FitzGerald Scholars Program, funded by the National Foundation for Australia–China Relations (an initiative of the Australian Department of Foreign Affairs and Trade).

Research Interests

  • 💤 Adult, neonatal and fetal sleep state classification
    Using deep learning for automated identification of adult, neonatal and fetal behavioral states.
  • 🧠 Multimodal physiological signal analysis
    EEG, ECG, EMG, and heart rate variability for comprehensive fetal monitoring.
  • 🔁 Transfer learning & cross-species/domain adaptation
    Improving generalization between adult-neonatal-fetal datasets and across species.
  • 🩺 Fetal hypoxia–ischemia detection
    Early prediction of neurodevelopmental risk from physiological signals.
  • 🤖 Trustworthy & interpretable AI for perinatal care
    Model robustness, transparency, and clinical decision support.
  • 📈 Representation learning for biomedical time-series
    CNNs, RNNs, Transformers, contrastive learning, and latent space modeling.
  • 📝 2026 — Paper Published, IEEE Journal of Biomedical Health Informatics (JBHI) “FetalSleepNet: A Transfer Learning Framework with Spectral Equalisation Domain Adaptation for Fetal Sleep Stage Classification” (SCI, JCR Q1, IF = 7.7)
  • 💤 2026 — Paper Published, Sleep (Oxford University Press)
    “Fetal Sleep: A Cross-Species Review of Physiology, Measurement, and Classification” (SCI, JCR Q1, IF = 7.0)
  • 🇺🇸 2025 — 1-Page Abstract Accpeted, IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI’25), (Atlanta, USA)
    “Fetal Sleep State Classification Using Deep Learning.”
  • 🔬 2024 — Paper Published, IEEE Sensors 2024 (Kobe, Japan)
    “Advancing Fetal Surveillance with Physiological Sensing: Detecting Hypoxia in Fetal Sheep”
  • 🧠 2025 — Paper Published, Neural Networks “Contrastive Graph Auto-Encoder for Graph Embedding” (SCI, JCR Q1, IF = 7.2) — Note: Result of Master’s research.
  • 🎤 2024 — Paper Published, IEEE ICASSP 2024 (Seoul, Korea)
    “GuessKT: Improving Knowledge Tracing via Guess Behaviors”
  • ⚖️ 2024 — arXiv Preprint, Computer Science (Edu) “Fair Knowledge Tracing in Second Language Acquisition” Investigated algorithmic fairness across platforms (iOS/Android) and regions (Developed vs. Developing countries) using Duolingo datasets. [arXiv:2412.18048]

Monash ECSE Postgraduate Travel Support (2025 – 2026)

  • Role: Principal Investigator / Recipient

  • Funding Body: Department of Electrical and Computer Systems Engineering, Monash University

  • 2026: Awarded to support manuscript presentation at the IEEE-EMBS BHI 2026 in Hong Kong.

  • 2025: Awarded to support travel and poster presentation at the IEEE-EMBS BHI 2025 in Atlanta, USA.

U.S. National Institutes of Health (NIH) Research Grant (Top-up) (2024 – Present)

  • Role: Ph.D. Researcher / Key Personnel

  • Funding Body: U.S. National Institutes of Health (NIH)

  • Project Context: Multi-institutional collaborative funding (Monash × Emory × Georgia Tech) dedicated to advancing AI-driven physiological monitoring, fetal neurodevelopment assessment, and early hypoxia detection in perinatal medicine.

Ex Animo Scholarship for Engineering (EASE) & Faculty of Engineering International Postgraduate Research Scholarship (FEIPRS) (2024 – Present)

  • Role: Principal Recipient

  • Funding Body: Faculty of Engineering, Monash University

  • Purpose: Highly competitive institutional funding covering full tuition fees and providing a living stipend to support outstanding international doctoral candidates conducting high-impact research in engineering and medical technology.

Supervision

Undergraduate

Connor Page
Advancing Fetal Surveillance with Physiological Sensing: Detecting Hypoxia in Fetal Sheep
04/2026 to Until Now

Justin Kong
Advancing Fetal Surveillance with Physiological Sensing: Detecting Hypoxia in Fetal Sheep
04/2026 to Until Now

Devon Spilecki
Advancing Fetal Surveillance with Physiological Sensing: Detecting Hypoxia in Fetal Sheep
04/2026 to Until Now

Last modified: 22/06/2026