My publications explore representation learning, neural compression, multimodal fusion, and learning from limited or imbalanced data. The applications include physiological signals and human activity data.
You can also find my publications on Google Scholar.
2025 IEEE International Symposium on Circuits and Systems (ISCAS 2025), 2025
An attention-based autoencoder for learning compact representations of gait time-series data. The work examines the trade-off between compression and reconstruction fidelity, using a reduced-dimensional attention mechanism.
IEEE Transactions on Biomedical Circuits and Systems (TBioCAS), 2024
A supervised contrastive learning framework that combines augmentation and model optimization for classification with scarce and imbalanced data. The study also examines the trade-off between inference efficiency and classification quality in a hardware implementation.
2024 IEEE International Symposium on Circuits and Systems (ISCAS 2024), 2024
A multimodal learning approach that combines complementary EEG and ECG inputs through squeeze-and-excitation fusion. The study examines feature extraction, class imbalance, and model design for sleep-stage classification.
2024 IEEE Biomedical Circuits and Systems Conference (BioCAS 2024), 2024
An autoencoder-based approach to compressing and reconstructing variable-length audio sequences, with a composite loss designed to preserve reconstruction quality. The method was evaluated on respiratory sound recordings in the BioCAS 2024 Grand Challenge.
2023 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2023), 2023
A sequence-classification model that combines convolutions and self-attention to learn from multivariate sensor data. The method is applied to gait detection and human activity recognition using inertial measurements.
2023 IEEE Biomedical Circuits and Systems Conference (BioCAS 2023), 2023
A classification approach combining supervised contrastive pretraining, feature augmentation, and MixUp to learn from limited and imbalanced data. The work evaluates these techniques on respiratory sound classification tasks.