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. Her research focuses on machine learning. From 2019 to 2024, she worked as a Staff Researcher at Baidu Research, specializing in cold-start recommendation with sparse labeled samples, query understanding, large model and agent optimization, and AI4Science. Her research spans machine learning and artificial intelligence, with a focus on parsimony learning, including few-shot learning, sparse learning, and low-rank learning, aimed at solving real-world problems in biomedicine, recommendation systems, and natural language processing efficiently and cost-effectively. Dr. Wang has published many papers in top-tier international conferences and journals, including NeurIPS, ICML, ICLR, KDD, TheWebConf, SIGIR, EMNLP, TPAMI, JMLR, and TIP, with more than 4000 citations. Her survey on few-shot learning is the most cited paper in ACM Computing Surveys in the past five years and has been recognized as an ESI Hot Paper (top 0.1%). As a key member, she has led major projects such as the National Key R&D Program (Science and Technology Innovation 2030) and projects funded by the National Natural Science Foundation of China. Dr. Wang also serves as a Senior Program Committee member for IJCAI and AAAI and reviews for top conferences and journals, including ICML, NeurIPS, ICLR, and TPAMI. Dr. Wang has been selected for the World's Top 2% Scientists List (single year) by Stanford University and Elsevier company on 2024.