Peking University - China
AUTHOR PROFILE
EARLY ACADEMIC PURSUITS
Yu Shen pursued advanced studies in the field of machine learning, earning a PhD from Peking University. His academic journey equipped him with expertise in AutoML and graph learning, laying the foundation for his contributions to the field.
PROFESSIONAL ENDEAVORS
Dr. Yu Shen has made significant contributions to the field of machine learning through his professional endeavors. He has been involved in pioneering projects such as Openbox, a generalized black-box optimization service, and VolcanoML, which speeds up end-to-end AutoML through scalable search space decomposition. His work at institutions like Peking University and the Chinese Academy of Sciences demonstrates his commitment to advancing the frontiers of machine learning.
CONTRIBUTIONS AND RESEARCH FOCUS
Yu Shen's research focus centers on machine learning, AutoML, and graph learning. His contributions include the development of innovative frameworks such as Pasca, a graph neural architecture search system, and Grain, which improves the data efficiency of graph neural networks. Through his research, Dr. Shen addresses key challenges in machine learning and contributes to the development of efficient and scalable algorithms.
IMPACT AND INFLUENCE
Dr. Yu Shen's research has had a significant impact on the field of machine learning, as evidenced by his numerous citations and publications in prestigious conferences and journals. His work on Openbox and other projects has garnered attention from the academic community and industry practitioners, shaping the discourse on black-box optimization and AutoML. As a leading expert in his field, Dr. Shen continues to influence the direction of research in machine learning and graph learning.
ACADEMIC CITES
Yu Shen's research has been widely cited in the academic community, reflecting the significance and relevance of his contributions to machine learning. His papers have been cited over 350 times since 2019, attesting to the impact of his work on advancing the state-of-the-art in AutoML and graph learning.
LEGACY AND FUTURE CONTRIBUTIONS
Dr. Yu Shen's legacy lies in his groundbreaking contributions to machine learning and AutoML. His innovative frameworks and algorithms have paved the way for advancements in graph learning and optimization. In the future, Dr. Shen's research is expected to continue shaping the field of machine learning, with applications spanning various domains, including agricultural and development economics, where his expertise can be leveraged to address complex challenges and drive positive change.
NOTABLE PUBLICATION
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition. 2023 (36)
Transfer Learning for Bayesian Optimization: A Survey. 2023 (15)