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. 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, with a research focus on machine learning. From 2019 to 2024, she worked as a Staff Researcher at Baidu Research, where she focused on developing AI technologies for cold-start recommendation with limited labeled data, retrieval intent recognition, large language models and agent optimization, as well as AI for Science (AI4Science).
Dr. Wang’s research interests span machine learning, artificial intelligence, and data science. Guided by the principle of parsimony, her work aims to uncover efficient, low-cost, and interpretable scientific mechanisms to address real-world challenges. Her current research focuses on data-efficient learning paradigms such as few-shot learning, meta-learning, and in-context learning; the modeling of large language models and agents; cross-disciplinary AI applications in science and mathematics (AI + X); and recommendation systems and user modeling under cold-start scenarios.
Dr. Wang has published over 30 papers in top-tier conferences and journals, including NeurIPS, ICML, ICLR, KDD, TheWebConf, SIGIR, EMNLP, TPAMI, JMLR, and TIP, with more than 4,700 citations. As a key contributor, she has participated in major national research projects, including the Ministry of Science and Technology’s “Next Generation Artificial Intelligence” initiative under the 2030 Innovation Megaprojects and a General Program of the National Natural Science Foundation of China. She serves as a Senior Program Committee member for IJCAI and AAAI, and regularly reviews for top conferences and journals such as ICML, NeurIPS, ICLR, and TPAMI. Dr. Wang was named to the World's Top 2% Scientists List in 2024 and selected for the Beijing Nova Program in 2025.
Dr. Wang’s research interests span machine learning, artificial intelligence, and data science. Guided by the principle of parsimony, her work aims to uncover efficient, low-cost, and interpretable scientific mechanisms to address real-world challenges. Her current research focuses on data-efficient learning paradigms such as few-shot learning, meta-learning, and in-context learning; the modeling of large language models and agents; cross-disciplinary AI applications in science and mathematics (AI + X); and recommendation systems and user modeling under cold-start scenarios.
Dr. Wang has published over 30 papers in top-tier conferences and journals, including NeurIPS, ICML, ICLR, KDD, TheWebConf, SIGIR, EMNLP, TPAMI, JMLR, and TIP, with more than 4,700 citations. As a key contributor, she has participated in major national research projects, including the Ministry of Science and Technology’s “Next Generation Artificial Intelligence” initiative under the 2030 Innovation Megaprojects and a General Program of the National Natural Science Foundation of China. She serves as a Senior Program Committee member for IJCAI and AAAI, and regularly reviews for top conferences and journals such as ICML, NeurIPS, ICLR, and TPAMI. Dr. Wang was named to the World's Top 2% Scientists List in 2024 and selected for the Beijing Nova Program in 2025.