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Zhang, G., Wang, X., Ozturk, A., ... & Samir, A. E. (2026). A Precision-Constrained Framework for Evaluating Noninvasive Biomarkers in MASLD and Beyond. Accepted in Nature Communications.
Zhang, G., Wang, X., Cheah E., Guo P., Pierce, T., Samir A. Development and Evaluation of Ultrasound Image Learning Pipelines for MASLD Risk Stratification. (2026) Accepted in IEEE Engineering in Medicine and Biology Society (EMBC).
Zhang, G., Ni, P., Cheah E., Chandra R., Guo P, Chung, R., Samir, A. Ultrasound-Based Prediction of Cirrhosis Decompensation Using Large-Scale Computer Vision Models. (2026) Accepted in IEEE Engineering in Medicine and Biology Society (EMBC).
Zhang, G., Wang, X., Cheah E., Guo P., Pierce, T., Samir A. (2026) A Deep Learning Approach for Automated Region of Interest Extraction in 2D Shear Wave Elastography to Predict Liver Fibrosis in Metabolic Dysfunction-Associated Steatotic Liver Disease. Accepted in American Institute of Ultrasound in Medicine (AIUM).
Ni, P., Zhang, G., Cheah E., Guo P., Chandra S., Chung, R., Samir A. (2026) Large Language Model-Assisted Ultrasound Characterization Improves Early Detection of Cirrhosis Decompensation. Accepted Digestive Disease Week (DDW).
Kafaei, A., Schoen, S., Candel I., Zhang, G., Guo, P., Wang, M., Tadross, R., Washburn, M., Rivaz, H., Samir, A. (2025) Particle Velocity Estimation in Shear Wave Elastography Using a Mamba-Based Spatiotemporal Network. in IEEE International Ultrasonics Symposium.
Zhang, G., & Etemad, A. (2025). Partial Label Learning for Emotion Recognition from EEG. in IEEE Transactions on Affective Computing. https://doi.org/10.1109/TAFFC.2025.3562027
Chu, V., Zhang, G., Li, Q., Radulovich, N., Tsao, M., He, H., & Wang, B. (2024, July). Characterization and Profiling of Neoantigens Identified in Patient-Derived Tumor Organoids Using a Peptide-Based Transformer Model. Presentation at the ISMB 2024 Conference, Computational and Systems Immunology Track, C-075.
Coles, N. A., Perz, B., Behnke, M., Eichstaedt, J., Kim, S. H., Vu, T. N., Raman, C., Tejada, J., Zhang, G., Cui, T., & others. (2024). Big Team Science Reveals Promises and Limitations of Machine Learning Efforts to Model the Physiological Basis of Affective Experience. Royal Society Open Science. https://doi.org/10.1098/rsos.241778
Zhang, G., & Etemad, A. (2024). Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean Space. IEEE Transactions on Emerging Topics in Computational Intelligence, 8(2), 1469-1483. https://doi.org/10.1109/TETCI.2023.3332549
Zhang, G., & Etemad, A. (2023). Distilling EEG Representations via Capsules for Affective Computing. Pattern Recognition Letters, 171, 99-105. https://doi.org/10.1016/j.patrec.2023.05.011
Zhang, G., Davoodnia, V., & Etemad, A. (2022). Parse: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition. IEEE Transactions on Affective Computing, 13(4), 2185-2200. https://doi.org/10.1109/TAFFC.2022.3210441
Zhang, G., & Etemad, A. (2022, May). Holistic Semi-Supervised Approaches for EEG Representation Learning. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 1241-1245). IEEE. https://doi.org/10.1109/ICASSP43922.2022.9746528
Zhang, G., & Etemad, A. (2022, February). Pairwise Representation Alignment for Semi-Supervised EEG-Based Emotion Recognition. In AAAI Workshop on Human-Centric Self-Supervised Learning.
Zhang, G., & Etemad, S. A. (2021). Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 1138-1149. https://doi.org/10.1109/TNSRE.2021.3089594
Zhang, G., & Etemad, A. (2021, September). Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition. In 9th International Conference on Affective Computing and Intelligent Interaction (ACII) (pp. 1-8). IEEE. https://doi.org/10.1109/ACII52823.2021.9597449
Zhang, G., Davoodnia, V., Sepas-Moghaddam, A., Zhang, Y., & Etemad, A. (2019). Classification of Hand Movements from EEG Using a Deep Attention-Based LSTM Network. IEEE Sensors Journal, 20(6), 3113-3122. https://doi.org/10.1109/JSEN.2019.2956991
Zhang, G., Morin, E., Zhang, Y., & Etemad, S. A. (2018, July). Non-Invasive Detection of Low-Level Muscle Fatigue Using Surface EMG with Wavelet Decomposition. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5648-5651). IEEE. https://doi.org/10.1109/EMBC.2018.8513588
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