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GuessKT: Improving Knowledge Tracing via Considering Guess Behaviors

Published in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024

We propose GuessKT, a knowledge tracing model that explicitly models students’ guess behaviors to improve the reliability of learning feedback and enhance prediction accuracy across multiple datasets.

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Advancing Fetal Surveillance with Physiological Sensing: Detecting Hypoxia in Fetal Sheep

Published in IEEE Sensors 2024, 2024

Using fetal EEG, ECG, EMG and blood pressure signals in a preclinical sheep model, we develop machine learning methods to detect hypoxia–ischemia during labour-like umbilical cord occlusion.

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Fair Knowledge Tracing in Second Language Acquisition

Published in arXiv preprint, 2024

We study fairness in knowledge tracing for second language acquisition, proposing methods to improve predictive performance while mitigating biases across learner subgroups.

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Fetal Sleep: A Cross-Species Review of Physiology, Measurement, and Classification

Published in Sleep (under minor revision), 2025

A comprehensive review of fetal sleep physiology, measurement modalities and state classification across humans, sheep and other preclinical models, highlighting implications for neurodevelopment and monitoring.

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Contrastive Graph Auto-Encoder for Graph Embedding

Published in Neural Networks, 2025

We propose a contrastive graph auto-encoder that learns high-quality node embeddings via joint reconstruction and contrastive objectives, achieving strong performance on graph representation learning benchmarks.

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FetalSleepNet: A Transfer Learning Framework with Spectral Equalisation Domain Adaptation for Fetal Sleep Stage Classification

Published in IEEE Journal of Biomedical and Health Informatics (under review), 2025

FetalSleepNet transfers adult sleep EEG knowledge to fetal sheep EEG using spectral equalisation and domain adaptation, improving cross-species fetal sleep stage classification.

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Fetal Sleep State Classification Using Deep Learning (Poster Rapid Fire)

Published: October 28, 2025

Presented at the IEEE BHI 2025 Poster Rapid Fire session.
This rapid-fire oral presentation highlighted the deep-learning pipeline for fetal sleep state classification using multimodal physiological signals.

Fetal Sleep State Classification Using Deep Learning (Poster Session A)

Published: October 28, 2025

Poster Session A at IEEE BHI 2025, presenting the full workflow and results of fetal sleep state classification based on multimodal physiology.

Fetal Sleep State Classification Using Deep Learning (Poster Session B)

Published: October 28, 2025

Poster Session B at IEEE BHI 2025, showcasing detailed model comparisons and feature analysis for fetal REM/Non-REM/Intermediate sleep staging.

Fetal Sleep State Classification Using Deep Learning (NextGen Scholar Presentation)

Published: October 29, 2025

Selected as an IEEE BHI 2025 NSF-EMBS-Google NextGen Scholar.
A 3-minute lightning talk summarizing innovations in fetal sleep staging and domain adaptation across species.

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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