Biography
Dr. Ying Liu is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University. She obtained a Ph.D. degree in Electrical Engineering from the State University of New York at Buffalo. Her main research interests are in image and video processing and coding, coding for machines, deep learning, and computer vision. She is a member of the Institute of Electrical and Electronics Engineers (IEEE) and a member of the Society of Photo-Optical Instrumentation Engineers (SPIE). Her research articles are published in prestigious journals such as the IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), IEEE Transactions on Multimedia (TMM), IEEE Transactions on Consumer Electronics, Pattern Recognition (Elsevier), and many flagship conferences. She serves as an Associate Editor for the IEEE Transactions on Circuits and Systems for Video Technology, and she also serves as the technical program committee member of multiple international conferences. She is the Secretary/Treasurer of the Asia-Pacific Signal and Information Processing Association (APSIPA) US Chapter.
Education
Ph.D., Electrical Engineering, SUNY at Buffalo, 2012
M.S., Electrical Engineering, SUNY at Buffalo, 2008
B.S., Telecommunications Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing, China, 2006
Research
- Image and Video Processing
- Image and Video Coding
- Coding for Machines
- Deep Learning
- Computer Vision
Courses Taught
- COEN 166/266 Artificial Intelligence
- COEN 140 Machine Learning and Data Mining
- COEN 194/195/196 Senior Design Project
- COEN 240 Machine Learning
- COEN 347 Advanced Techniques in Video Coding
Awards
- Ying Liu (PI), “Learned Video Compression with Generative Adversarial Networks and Transformers,” NVIDIA Academic Hardware Grant Program, Mar. 2022.
- Ying Liu (PI), “ERI: Generative Adversarial Networks for Video Coding,” external funding, National Science Foundation, Feb. 1, 2022-Jan. 31, 2024, estimated.
- Nam Ling (PI) and Ying Liu (PI), “Low Complexity and High Efficiency Image and Video Coding with Deep Learning on Heterogeneous Platforms,” external funding, Kwai, Inc, June 16, 2021- June 15, 2022.
- Nam Ling (PI) and Ying Liu (Co-PI), “Low Complexity and High Efficiency Image and Video Processing with Neural Network on Heterogeneous Platforms,” external funding, Kwai, Inc., June 16, 2020 - June 15, 2021.
- Ying Liu (PI), Kuehler Undergraduate Research Grant, School of Engineering, Santa Clara University, 2023.
- Ying Liu (PI), Kuehler Undergraduate Research Grant, School of Engineering, Santa Clara University, 2022.
- Ying Liu (PI), Internal Research Grant, School of Engineering, Santa Clara University, 2020/2021.
- Ying Liu (PI), Internal Research Grant, School of Engineering, Santa Clara University, 2019/2020.
Publications
Journals
- Michael G. Schimpf, Nam Ling, Ying Liu, “Compressing of medium- to low-rate transform residuals with semi-extreme sparse coding as an alternate transform in video,” IEEE Transactions on Consumer Electronics, Mar. 2023, accepted.
- Fityanul Akhyar, Ying Liu, Chao‑Yung Hsu, Timothy K. Shih, Chih‑Yang Lin, “FDD: a deep learning-based steel defect detector,” The International Journal of Advanced Manufacturing Technology, vol. 126, pp. 1093 - 1107, Mar. 2023.
- Bingxin Hou, Ying Liu, Nam Ling, Yongxiong Ren, Lingzhi Liu, “A survey of efficient deep learning models for moving object segmentation,” APSIPA Transactions on Signal and Information Processing, vol. 12, no. 1, pp. 1 - 84, Jan. 2023.
- Bingxin Hou, Ying Liu, Nam Ling, Lingzhi Liu, Yongxiong Ren, “A fast lightweight 3D separable convolutional neural network with multi-input multi-output for moving object detection,” IEEE Access, vol. 9, pp. 148433 - 148448, Oct. 2021.
Conference Proceedings
- Yifei Pei, Ying Liu, Nam Ling, Yongxiong Ren, Lingzhi Liu, “An end-to-end deep generative network for low bitrate image coding,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), Monterey, CA, May 2023, accepted.
- Tianma Shen, Ying Liu, “Learned image compression with transformers,” in Proceedings of the SPIE Defense + Commercial Sensing, Conference 12522, Big Data V: Learning, Analytics, and Applications, Orlando, FL, May 2023.
- Pengli Du, Ying Liu, Nam Ling, Yongxiong Ren, and Lingzhi Liu, “Generative video compression with a transformer-based discriminator,” in Proceedings of the Picture Coding Symposium (PCS), San Jose, CA, Dec. 2022.
- Zhongpeng Zhang, Ying Liu, “Side information driven image coding for machines,” in Proceedings of the Picture Coding Symposium (PCS), San Jose, CA, Dec. 2022.
- Mareeta Mathai, Ying Liu, Nam Ling, “A lightweight model with separable CNN and LSTM for video prediction,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), Austin, TX, May-June 2022.
- Pengli Du, Ying Liu, Nam Ling, Lingzhi Liu, Yongxiong Ren, Ming-Kai Hsu, “A generative adversarial network for video compression,” in Proceedings of the SPIE Defense + Commercial Sensing, Conference: Big Data IV: Learning, Analytics, and Applications, Orlando, Florida, Apr. 2022.
- Bingxin Hou, Ying Liu, Nam Ling, Lingzhi Liu, Yongxiong Ren, Ming-Kai Hsu, “F3DsCNN: a fast two-branch 3D separable CNN for moving object detection,” in Proceedings of the IEEE Conference on Visual Communications and Image Processing (VCIP), Munich, Germany, Dec. 2021.
- Yifei Pei, Ying Liu, Nam Ling, Lingzhi Liu, and Yongxiong Ren, “Class-specific neural network for video compressed sensing,” in Proceedings of the IEEE International Symposium on Circuits and Systems, Daegu, Korea, May 2021.
- Ying Liu, Pengli Du, and Yuzhu Li, “Hierarchical motion-compensated deep network for video compression,” in Proceedings of the SPIE Symposium on Defense + Commercial Sensing, Conference 11730, Big Data III: Learning, Analytics, and Applications, Orlando, FL, Apr. 2021.
- Michael Schimpf, Nam Ling, Yunhui Shi, and Ying Liu, “Sparse coding of intra prediction residuals for screen content coding,” in Proceedings of the IEEE International Conference on Consumer Electronics (ICCE), 2021.
Thesis
- Decoding of Purely Compressed Sensed Video
Y. Liu (advisor: Prof. Dimitris A. Pados), Ph.D. Thesis, Department of Electrical Engineering, SUNY Buffalo, June 2012.
