Education
- B.S. in Information Engineering, XIDIAN University, 2015
- Ph.D in Bio-image and Information Analysis Lab , University of Houston , 2020
Work Experience
- 2021 - now: Research Engineer
- 2015 - 2020: Research Assistant
- University of Houston (Houston,TX)
- Bio-image and Information Analysis Lab in Electrical and Computer Engineer Dept
- Thesis: “Large Scale Nucleus Segmentation and Neighborhood Analysis on Rat Brain Images”
- Supervisor: Professor Badri Roysam
- Summer 2019: Deep Learning Engineer Intern
- Anadarko Petroleum (the Woodlands, TX)
- Project: “Seismic Image Recovery and Optimal Sampling Recommendation”
- Mentors: Dr.Nikolaos Mitsakos, Dr.Ping Lu
- Summer 2018: Deep Learning Engineer Intern
- Ambarella Corporation (San Jose, CA)
- Project: “Cross-platform Solutions for Self-driving Car Chip Simulation”
- Summer 2017: Pre-doc Fellow
- NINDS, National Institute of Health (Bethesda,MD)
- Project: “ A Pipeline for High throughout Image Processing and Data Analysis of Brain Tissues”
- Supervisor: Dr. Dragan Maric
Skills & Languages
- Programming: Python ( Skimage, OpenCV, Pandas), Matlab, Linux Bash, R, C/C++, VHDL, SQL
- AI Framwroks: Keras, Tensorflow, Pytorch, Caffe, ONNX
- Cloud Service: HPC, Docker, AWS, Colab
- Languages: English, Chinese(Mandarin)
Leadership
- Graduate Affair Chair, IEEE-University of Houston Student Branch, 2018
- Outreach/ Coordinator, US & China Innovation and Investment Summit, 2017
- President, Model United Nations Association of Xidian University, 2013
Reviewers
- American Journal of Neural Networks and Applications(AJNNA) , 2020
- Workshop on Medical Image Computing and Computer Assisted Intervention(MICCAI), 2020
- Geophysical Journal International, 2019
- IEEE International Symposium on Biomedical Imaging, 2018
Publications
- Andriushchenko, M.,X. Rebecca. Li, Oxholm, G., Gittings, T., Bui, T., Flammarion, N. and Collomosse, J., 2022. ARIA: Adversarially Robust Image Attribution for Content Provenance. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 33-43).
- Maric, D., Jahanipour, J., Li, X.R. et al. Whole-brain tissue mapping toolkit using large-scale highly multiplexed immunofluorescence imaging and deep neural networks. Nat Commun 12, 1550 (2021).
- Yuan, P., Mobiny, A., Jahanipour, J., Li, X., Cicalese, P. A., Roysam, B., … & Van Nguyen, H. (2020, October). Few Is Enough: Task-Augmented Active Meta-learning for Brain Cell Classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 367-377). Springer, Cham.
- Li, X.R., Mitsakos, N., Lu, P., Xiao, Y., Zhan, C. and Zhao, X.,(2019) Generative Inpainting Network Applications on Seismic Image Compression and Non-Uniform Sampling. Workshop on Neural Information Processing Systems (NIPS): Solving Inverse Problems with Deep Networks .
- Li, X. R., Mitsakos, N., Lu, P., Xiao, Y., & Zhao, X. (2019). Seismic compressive sensing by generative inpainting network: Toward an optimized acquisition survey. The Leading Edge, 38(12), 923-933.
- Yuan, P., Rezvan, A., Li, X., Varadarajan, N. and Van Nguyen, H., (2019). Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images. Journal of clinical medicine, 8(8), p.1159.
- Zhao X, Lu P, Zhang Y, Chen J, Li X. Swell-noise attenuation: A deep learning approach. The Leading Edge. (2019) Dec;38(12):934-42.
- Zhao, X., Lu, P., Zhang, Y., Chen, J., & Li, X. (2019). Attenuating Random Noise in Seismic Data by a Deep Learning Approach. arXiv preprint arXiv:1910.12800.
- Li Xiaoyang, “A Simplified Normalization Operation for Perfect Reconstruction from a Modified STFT”, In Pros, IEEE 12th International Conference on Signal Processing (ICSP) , 2014, P42-45
