Dr. Li Yang (https://lyang-666.github.io/) is an assistant professor in the Computer Science Department at the University of North Carolina at Charlotte. Prior to that, he received my Ph.D. degree from Arizona State University, advised by Prof. Deliang Fan, a Master’s degree at the University of Central Florida, and a Bachelor’s degree at Northeastern University, China.
Dr. Yang’s research interests lie in AI/Machine learning, primarily focusing on efficient and continual machine learning methodologies, along with their practical applications in diverse domains such as computer vision, natural language processing, and more. His recent research directions include 1) Efficient machine learning for both training and inference; 2) Efficient continual learning/transfer learning towards edge computing; 3) AI hardware accelerator with software and hardware co-design. During his Ph.D. studies, Dr.Yang has 31 publications in IEEE/ACM top-tier conferences and journals (e.g., NeurIPS, CVPR, ICLR, AAAI, DAC, DATE, TNNLS, TC, JETC, etc.) with the receipt of the Best Interactive Presentation(IP) Award from DATE 2022, and Young Fellow Award from DAC 2020.
Dr. Yang的研究兴趣集中在人工智能/机器学习领域,主要关注高效和持续的机器学习方法,以及它们在计算机视觉、自然语言处理等多个领域的实际应用。他最近的研究方向包括:1)高效的训练和推理机器学习;2)面向边缘计算的高效持续学习/迁移学习;3)具有软硬件协同设计的人工智能硬件加速器。在博士研究期间,Dr. Yang在IEEE/ACM等顶级会议和期刊(如NeurIPS、CVPR、ICLR、AAAI、DAC、DATE、TNNLS、TC、JETC等)上发表了31篇论文,并荣获了来自DATE 2022的最佳互动演示(IP)奖,以及来自DAC 2020的青年学者奖。