WNCG professor Atlas Wang received a Data Science Research Award from Adobe for his work on “Towards Automated Design of Efficient Deep Multi-Modal Recommendation Models.”
Every year, Adobe funds a university faculty research program to “promote the understanding and use of data science in the area of marketing with the goal “to encourage both theoretical and empirical development of solutions to problems in marketing.”
"Today’s recommendation algorithms leverage deep learning to maximize accuracy, that inevitably incurs large computational burden.,” said Prof. Wang. “For example, recent analysis reveals that the top recommendation models contribute to more than 72% of all AI inference cycles across Facebook’s production datacenter. Despite the substantial demands, very limited efforts have been devoted to optimizing the design of those deep learning recommendation models, and improving their efficiency. “
This project’s overarching goal is to develop an automated framework, for the end-to-end design optimization of efficient deep recommendation models, from multi-modal data whose contents may include high-quality images, graphics, videos, tags and textual descriptions.
“We will leverage state-of-the-art techniques from multi-modal fusion, model compression, and AutoML,” said Wang. “The outcome of this project can be potentially integrated with Adobe Magento/AEM’s asset search and recommendation platform."
Earlier this year, Prof. Wang also received an ARO Young Investigator award on the topic of theoretically understanding sparse representation and deep learning; an IBM Faculty Research Award on the topic of AutoML; and an Amazon Machine Learning Research Award on the topic of image understanding on smart phones. His team also won the second prize in the CVPR 2020 Low-Power Computer Vision (LPCV) Challenge, leveraging algorithm-hardware co-design to develop a practical energy-efficient system to detect and recognize texts from UAV-captured videos.
Professor Zhangyang "Atlas" Wang is currently an Assistant Professor of Electrical and Computer Engineering at The University of Texas at Austin. He was an Assistant Professor of Computer Science and Engineering, at the Texas A&M University, from 2017 to 2020. Prof. Wang is broadly interested in the fields of machine learning, computer vision, optimization, and their interdisciplinary applications. His latest interests focus on automated machine learning (AutoML), learning-based optimization, machine learning robustness, and efficient deep learning.
He has received many research awards and scholarships, including most recently an ARO Young Investigator award, an IBM faculty research award, an Amazon research award, a Young Faculty Fellow of TAMU, and four research competition prizes from CVPR/ICCV/ECCV.