View the PDF version: Rebecc Li - CV 2024.pdf

Work Experience

• 2021 - Present: Research Engineer / Machine Learning Engineering Manager
  • Adobe Inc (San Jose, CA)

  • 2024: Led the development of an Automatic Evaluation Framework for benchmarking the performance of Image and Video GenAI models. Advanced standards in Responsible AI through industry-leading practices. Check out my public talk.

  • 2023: Engineering founder and Release Lead of a cross-functional GenAI model development inference SDK, scaling the contributor team from 10 to 100+. Shipped as Adobe Firefly, a groundbreaking GenAI creation platform known for safe commercial use. Its applications in Photoshop were recognized as one of Time’s Best AI Inventions of 2023. Check out my public talk.

  • 2022: Developed a GAN-based face makeup transfer feature, deployed in Photoshop Express.

  • 2022: Led training data collection and processing for a smart distractor detection system, shipped as the Distraction Removal Feature in Photoshop.

  • 2021: Created a cross-library image augmentation API, named Beacon-aug, incorporating 300+ augmentation operations from 8 popular libraries. Awarded Top Session Award at Adobe Tech Summit 2022.
• 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

Education

• B.S. in Information Engineering, XIDIAN University, 2015
• Ph.D in Image Processing and Information Analysis , University of Houston , 2020

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

• Founder&Team Captain, Adobe Dragon Boat Club, 2023
• 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