Chang Liu

Chang Liu

Ph.D. Candidate at Boston University

About Me

Chang Liu is a 5th-year Ph.D. student in Computational Imaging Systems Lab at Boston University, specializing in deep learning, computational imaging, and signal processing. His research focuses on developing self-supervised video denoising frameworks for high-speed fluorescence imaging, leveraging generative models for uncertainty quantification, and integrating advanced computational methods into imaging systems to enhance experimental efficiency.

Chang is actively seeking opportunities as a Machine Learning Engineer or Research Scientist, where he can apply his expertise in deep learning and computational imaging to solve real-world challenges. Chang has extensive experience with Python (PyTorch, TensorFlow), C++, MATLAB, and a range of deep learning techniques, including self-supervised learning, diffusion models, and transformer-based architectures.

Before joining Boston University, Chang earned his M.S. from Carnegie Mellon University and his B.S. from Tsinghua University, working on projects in biomedical signal processing, brain-computer interfaces, and neuromodulation.

Journal Publications

  • Liu, C., Lu, J., Wu, Y., Ye, X., Ahrens, A. M., Platisa, J., … & Tian, L. (2024). DeepVID v2: self-supervised denoising with decoupled spatiotemporal enhancement for low-photon voltage imaging. Neurophotonics, 11(4), 045007. [link] [pdf] [code]

  • Ding, G., Liu, C., Yin, J., Teng, X., Tan, Y., He, H., … & Cheng, J. X. (2024). Self-Supervised Elimination of Non-Independent Noise in Hyperspectral Imaging. arXiv preprint arXiv:2409.09910. [link] [pdf]

  • Kosnoff, J., Yu, K., Liu, C., & He, B. (2024). Transcranial focused ultrasound to V5 enhances human visual motion brain-computer interface by modulating feature-based attention. Nature Communications, 15(1), 4382. [link] [pdf]

  • Platisa, J., Ye, X., Ahrens, A. M., Liu, C., Chen, I. A., Davison, I. G., … & Chen, J. L. (2023). High-speed low-light in vivo two-photon voltage imaging of large neuronal populations. Nature methods, 20(7), 1095-1103. [link] [pdf] [code]

  • Liu, C.*, Yu, K.*, Niu, X., & He, B. (2021). Transcranial focused ultrasound enhances sensory discrimination capability through somatosensory cortical excitation. Ultrasound in Medicine & Biology, 47(5), 1356-1366. [link] [pdf]

  • Yu, K., Liu, C., Niu, X., & He, B. (2020). Transcranial focused ultrasound neuromodulation of voluntary movement-related cortical activity in humans. IEEE Transactions on Biomedical Engineering, 68(6), 1923-1931. [link] [pdf]

  • Liu, D., Liu, C., Chen, J., Zhang, D., & Hong, B. (2020). Doubling the speed of N200 speller via dual-directional motion encoding. IEEE Transactions on Biomedical Engineering, 68(1), 204-213. [link]

Conference Proceedings

  • Liu, C., Platisa, J., Ye, X., Ahrens, A. M., Chen, I. A., Davison, I. G., … & Tian, L. (2024, March). Resolution-improved self-supervised two-photon voltage imaging denoising. In Neural Imaging and Sensing 2024 (p. PC1282807). SPIE. [link]

  • Liu, C., Platisa, J., Ye, X., Ahrens, A. M., Chen, I. A., Davison, I. G., … & Tian, L. (2023, March). Two-photon voltage imaging denoising by self-supervised learning. In Neural Imaging and Sensing 2023 (Vol. 12365, pp. 13-14). SPIE. [link]

  • Liu, C., Platisa, J., Ye, X., Ahrens, A. M., Chen, I. A., Davison, I. G., … & Tian, L. (2022, April). DeepVID: A Self-supervised Deep Learning Framework for Two-photon Voltage Imaging Denoising. In Optics and the Brain (pp. BTu4C-4). Optica Publishing Group. [link]

  • Liu, D., Liu, C., & Hong, B. (2019, March). Bi-directional visual motion based BCI speller. In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 589-592). IEEE. [link]

* Indicates equal contribution