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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
PhD student at the University of Georgia working on deep learning for time series, signal processing, and biomedical applications.
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
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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
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
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
This is a description of a teaching experience. You can use markdown like any other post.