Chang Liu

Chang Liu

Ph.D. Candidate at Boston University

About Me

Chang Liu is a 4th-year Ph.D. student in Biomedical Engineering at Boston University. Chang joined Computational Imaging Systems Lab advised by Prof. Lei Tian in 2021 working on deep learning and computational imaging. His current work is developing deep-learning-based denoising algorithms for ultra-fast, large-scale, high-resolution neural imaging.

Chang is expertized in self-supervised learning, image/video denoising, and biomedical signal processing. He is proficient in Python, MATLAB, and common deep learning tools including PyTorch and TensorFlow. His research interests include deep learning, computational imaging, signal processing, brain-computer interfaces, and neuromodulation.

Previously, Chang received his B.S. in Biomedical Engineering from Tsinghua University in China and performed research in brain-computer interfaces (BCI) aiming to increase the speed of BCI spellers. He received his M.S. in Biomedical Engineering from Carnegie Mellon University, where he conducted research in transcranial-focused ultrasound (tFUS) neuromodulation.

Journal Publications

  • 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]

  • Kosnoff, J., Yu, K., Liu, C., & He, B. (2023). Transcranial Focused Ultrasound to V5 Enhances Human Visual Motion Brain-Computer Interface by Modulating Feature-Based Attention. bioRxiv, 2023-09. [link] [pdf]

  • 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