معرفی
Dr. Ying Liu is an Associate Professor in the Department of Computer Science and Engineering at Santa Clara University's School of Engineering. She serves as the Secretary/Treasurer of the APSIPA US Chapter and holds multiple leadership roles including APSIPA Distinguished Lecturer (2025-26) and Vice Chair of the APSIPA US Local Chapter. Her research focuses on deep learning applications for visual data processing and compression.
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, 2006
Dr. Liu's research spans deep learning-based image/video processing, coding for machines, and generative AI. Her work integrates convolutional neural networks, transformers, and generative models to advance video compression and machine vision systems. Current projects include vision-language models and point cloud coding, with emphasis on computational efficiency for real-world deployment.
Analysis of her 15 most recent publications reveals a strong focus on neural video compression (40%), image coding for machines (30%), and generative AI applications (20%), with increasing emphasis on transformer architectures and multi-task frameworks since 2022. Her research bridges theoretical innovation with practical industrial applications in manufacturing and autonomous systems.
Scientific Awards:
- Researcher of the Year Award, School of Engineering, Santa Clara University (2024)
- APSIPA Distinguished Lecturer Appointment (2025-26)
Dr. Liu actively mentors PhD students in the Video and Image Processing (VIP) Laboratory, with four current advisees including Pengli Du (first PhD graduate in 2024). Her research is supported by significant grants including an NSF ERI award ($500K, 2022-2025), NVIDIA Academic Hardware Grant, and multiple industry-funded projects with Kwai Inc. totaling over $300K. She also secures internal university funding through Kuehler Undergraduate Research Grants and School of Engineering Research Grants.
The VIP Laboratory develops deep learning solutions for the projected 13 billion global cameras by 2030, focusing on applications for mobile video sharing, surveillance, and autonomous vehicles. Current projects utilize CNNs, GANs, RNNs, and transformers for visual coding efficiency.
