Machine Learning Grounded in the Principle of Parsimony
Organizer
Speaker
Time
Saturday, July 6, 2024 10:30 AM - 12:30 PM
Venue
A6-101
Online
Zoom 637 734 0280
(BIMSA)
Speaker Intro
Dr. Yaqing Wang is currently an Associate Professor at the Beijing Institute of Mathematical Sciences and Applications (BIMSA). She received her Ph.D. in Computer Science and Engineering from the Hong Kong University of Science and Technology in 2019, under the supervision of Professor Lionel M. Ni and Prof. James T. Kwok. From 2019 to 2024, she was a Staff Researcher at Baidu Research, recruited through the AIDU Program. Dr. Wang has published 40 papers in top-tier international conferences and journals, including ICML, NeurIPS, ICLR, KDD, TheWebConf, SIGIR, AAAI, IJCAI, EMNLP, TPAMI, JMLR, CSUR, and TIP, with more than 6000 citations. She is a recipient of the Hong Kong PhD Fellowship (2014–2018), was selected for Beijing Nova Program (2025), AAAI New Faculty Highlight (2026), IJCAI Early Career Spotlights (2026), and has been listed among the World’s Top 2% Scientists (2024-2025). She is a senior member of ACM, IEEE, and CCF. Dr. Wang serves as an Associate Editor of Neural Networks, editorial board member of Machine Learning and Area Chair for ACL Rolling Review. Her techniques have been deployed in large-scale real-world systems at Baidu, Meituan, and other industry applications.
Dr. Yaqing Wang’s research focuses on machine learning, artificial intelligence, and data science. She strives to develop refined, data-efficient, and cost-effective scientific solutions to real-world problems. Her current research interests include:
- Few-Shot Learning, Meta-Learning, and In-Context Learning
- Data-Efficient Agentic Learning
- Cold-Start Recommendation and Personalized User Modeling
- AI for Science and Mathematics (AI + X)
Dr. Yaqing Wang’s research focuses on machine learning, artificial intelligence, and data science. She strives to develop refined, data-efficient, and cost-effective scientific solutions to real-world problems. Her current research interests include:
- Few-Shot Learning, Meta-Learning, and In-Context Learning
- Data-Efficient Agentic Learning
- Cold-Start Recommendation and Personalized User Modeling
- AI for Science and Mathematics (AI + X)