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publications

A Teaching App to Demonstrate Hebb’s Learning Rule

Published in Bachelor thesis, University of Liverpool, 2021

Bachelor thesis on developing a teaching app to demonstrate Hebb’s rule in machine learning.

Recommended citation: Li, J. (2021). A Teaching App to Demonstrate Hebb's Learning Rule. Bachelor thesis, University of Liverpool. https://github.com/jimmylihui/jimmylihui.github.io/blob/master/files/app%20to%20illustrate%20hebb%20rule.pdf

Principal Component Analysis Machine Learning to Determine Membership of Globular Cluster M56

Published in Master thesis, University College London, 2022

Master thesis on using PCA and machine learning for globular cluster membership determination.

Recommended citation: Li, J. (2022). Principal Component Analysis Machine Learning to Determine Membership of Globular Cluster M56. Master thesis, University College London. https://github.com/jimmylihui/jimmylihui.github.io/blob/master/files/Principal%20component%20analysis%20machine%20learning%20to%20determine%20membership%20of%20globular%20cluster%20M56.pdf

MGTST: Multi-scale and Cross-channel Gated Transformer for Multivariate Long-term Time-series Forecasting

Published in 2024, 2024

Multi-scale and cross-channel gated transformer for multivariate long-term time-series forecasting.

Recommended citation: Li, J., Zhou, Z., & Yeung, D.Y. (2024). MGTST: Multi-scale and Cross-channel Gated Transformer for Multivariate Long-term Time-series Forecasting. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=CGnz6VQAAAAJ&citation_for_view=CGnz6VQAAAAJ:u5HHmVD_uO8C

Genbench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models

Published in arXiv preprint arXiv:2406.01627, 2024

A benchmarking suite for systematic evaluation of genomic foundation models.

Recommended citation: Liu, Z., Li, J., Li, S., Zang, Z., Tan, C., Huang, Y., Bai, Y., & Li, S.Z. (2024). Genbench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models. arXiv preprint arXiv:2406.01627. https://arxiv.org/abs/2406.01627

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges

Published in arXiv preprint, 2025

Survey on safety mechanisms, training paradigms, and emerging challenges in LLM alignment.

Recommended citation: Lu, H., Fang, L., Zhang, R., et al. (2025). Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges. arXiv preprint arXiv:2507.19672. https://arxiv.org/abs/2507.19672

Peak-R1: Instruction-Tuned Large Language Models for Robust J-Peak Detection in Cardiomechanical Signals

Published in NeurIPS 2025 Workshop on Learning from Time Series for Health, 2025

Instruction-tuned LLMs for robust J-peak detection in BCG/cardiomechanical signals.

Recommended citation: Li, J., Zhang, Y., Zeng, Z., Chen, J., Zhang, X., Lu, J., Song, W.Z., & Dou, F. (2025). Peak-R1: Instruction-Tuned Large Language Models for Robust J-Peak Detection in Cardiomechanical Signals. NeurIPS 2025 Workshop on Learning from Time Series for Health. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=CGnz6VQAAAAJ&citation_for_view=CGnz6VQAAAAJ:9yKSN-GCB0IC

ADLGen: Synthesizing Symbolic, Event-Triggered Sensor Sequences for Smart-Home Human Activity Modeling

Published in Proceedings of the 2026 ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems, 2026

Synthetic symbolic, event-triggered sensor sequences for smart-home human activity modeling.

Recommended citation: You, W., Jiang, H., Li, J., Liu, Z., Liu, T., Lu, J., & Dou, F. (2026). ADLGen: Synthesizing Symbolic, Event-Triggered Sensor Sequences for Smart-Home Human Activity Modeling. Proceedings of the 2026 ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems, 1029-1042. https://doi.org/10.1145/3774906.3802796 https://dl.acm.org/doi/10.1145/3774906.3802796

DeepArrhythmia: Segment-Contextualized ECG Arrhythmia Classification via Selective Evidence Acquisition

Published in arXiv preprint arXiv:2605.16441, 2026

Segment-contextualized ECG arrhythmia classification via selective evidence acquisition.

Recommended citation: Li, J., Fang, R., Liu, Z., Song, W.Z., Lu, J., & Dou, F. (2026). DeepArrhythmia: Segment-Contextualized ECG Arrhythmia Classification via Selective Evidence Acquisition. arXiv preprint arXiv:2605.16441. https://arxiv.org/abs/2605.16441

Peak-Detector: Explainable Peak Detection via Instruction-Tuned Large Language Models in Physiological Signals

Published in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2026

Instruction-tuned LLMs for explainable peak detection across physiological signals.

Recommended citation: Li, J., Zhang, Y., Zeng, Z., Chen, J., Song, Y., Xiao, Y., Dong, N., Lu, J., Kwon, Y., & Dou, F. (2026). Peak-Detector: Explainable Peak Detection via Instruction-Tuned Large Language Models in Physiological Signals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). https://doi.org/10.1145/3810224 https://dl.acm.org/doi/10.1145/3810224

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015