publications

2026

1. AAAI’26

   

   Multivariate Gaussian Representation Learning for Medical Action Evaluation

   Developed a novel multivariate Gaussian-based representation learning method for fine-grained temporal and spatial action evaluation, inspired by Gaussian Splatting. Integrated RGB and skeletal joint data modalities for downstream tasks, achieving state-of-the-art performance on benchmarks such as PennAct and CPRCoach.

   In The 40th Annual AAAI Conference on Artificial Intelligence, 2026
2. ACL’26

   

   (In review) Modeling LLM Unlearning As An Asymmetric Two-task Learning Problem

   Introduced a novel asymmetric two-task learning framework, SAGO, for language model unlearning. The method prioritizes retention while effectively resolving gradient conflicts, improving performance on benchmarks such as WMDP and RWKU, and advancing the Pareto frontier of forgetting and retention.

   In The 64th Annual Meeting of the Association for Computational Linguistics, 2026
3. Nature Comm.

   

   (In review, round 2) Disentangling the Drivers and Host-Mediated Global Spread of Influenza A Virus

   Utilized Bayesian inference and integrated phylodynamic and phylogeographic analysis to model the global spread and influencing factors of the H7 virus. Contributed to data analysis, modeling, verification, and manuscript preparation.

   Nature Communications, 2026

2025

1. BIBM’25

   

   Bidirectional Time-Frequency Pyramid Network for Enhanced Robust EEG Classification

   Proposed BiTE, an end-to-end EEG classification framework integrating multistream synergy, pyramid attention, and bidirectional adaptive convolutions for robust cross-paradigm BCI tasks. Achieved state-of-the-art performance with both within- and cross-subject generalization. Demonstrated computational efficiency across motor imagery (MI) and steady-state visual evoked potential (SSVEP) paradigms.

   In 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)Oral, AR: 19.6% , 2025

   arXiv Bib Code

   @inproceedings{hong2025biteeeg,
     title = {Bidirectional Time-Frequency Pyramid Network for Enhanced Robust EEG Classification},
     booktitle = {2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
     year = {2025},
     organization = {IEEE},
     description = {<p>Proposed BiTE, an end-to-end EEG classification framework integrating multistream synergy, pyramid attention, and bidirectional adaptive convolutions for robust cross-paradigm BCI tasks. Achieved state-of-the-art performance with both within- and cross-subject generalization. Demonstrated computational efficiency across motor imagery (MI) and steady-state visual evoked potential (SSVEP) paradigms.</p>}
   }