BIMSA > Ya Qing Wang
Associate Professor Ya Qing Wang

Group: Artificial Intelligence and Machine Learning

Office: A13-105

Email: wangyaqing@bimsa.cn

Research Field: Machine Learning, Data Mining, AI for Science

Webpage: https://wangyaqing.github.io/

Biography

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.

Research Interest

  • Few-Shot Learning and Meta Learning
  • Graph Learning
  • Drug Discovery and Bioinformatics
  • Large Language Models and Agents
  • Cold-Start Recommendation

Education Experience

  • 2010 - 2014      Shandong University      Computer Science and Technology      B.E.
  • 2014 - 2019      Hong Kong University of Science and Technology      Computer Science and Engineering      Ph.D      (Supervisor: Prof. Lionel M. Ni and Prof. James T. Kwok)

Work Experience

  • 2024 -      Beijing Institute of Mathematical Sciences and Applications      Associate Professor
  • 2019 - 2024      Baidu Research      Staff Researcher

Publication

  • [1] Shiguang Wu, Yaqing Wang, Quanming Yao, Why In-Context Learning Models are Good Few-Shot Learners?, International Conference on Learning Representations (ICLR) (2025)
  • [2] Quanming Yao, Yongqi Zhang, Yaqing Wang, Nan Yin, James Kwok, Qiang Yang, Beyond Scaleup: Knowledge‐Aware Parsimony Learning from Deep Networks, AI Magazine, 46(1), e12211 (2025)
  • [3] Yaqing Wang, Hongming Piao, Daxiang Dong, Quanming Yao, and Jingbo Zhou, Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2024)
  • [4] Shiguang Wu, Yaqing Wang, and Quanming Yao, PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction, International Joint Conference on Artificial Intelligence (IJCAI) (2024)
  • [5] Yongpan Zou,Yunshu Wang, Haozhi Dong, Yaqing Wang, Yanbo He, and Kaishun Wu, PreGes-Net: Few-shot Acoustic Gesture Recognition Based on Task-adaptive Pretrained Networks, IEEE Transactions on Mobile Computing (2024)
  • [6] Daxiang Dong, Shiguang Wu, Yaqing Wang, Jingbo Zhou, Haifeng Wang, ColdU: Cold-Start Recommendation with User-Specific Modulation, IEEE Conference on Artificial Intelligence (CAI) (2024)
  • [7] Yaqing Wang, Zaifei Yang, and Quanming Yao, Accurate and Interpretable Drug-drug Interaction Prediction Enabled by Knowledge Subgraph Learning, Communications Medicine (Commun. Med.,Nature Portfolio), 4(1), 59 (2024)
  • [8] Quanming Yao, Zhenqian Shen, Yaqing Wang†, and Dejing Dou, Property-Aware Relation Networks for Few-Shot Molecular Property Prediction, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 46(8), 5413-5429 (2024)
  • [9] Yan Wen, Chen Gao, Lingling Yi, Liwei Qiu, Yaqing Wang, and Yong Li, Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering, ACMSIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2023)
  • [10] Jiang Bian, Jizhou Huang, Shilei Ji, Yuan Liao, Xuhong Li, Qingzhong Wang, Jingbo Zhou, Yaqing Wang, Dejing Dou, and Haoyi Xiong, Feynman: Federated Advertising for Ecosystems-Oriented Mobile Apps Recommendation, IEEE Transactions on Service Computing (TSC), 16(5), 3361-3372 (2023)
  • [11] Zhen Wang, Hongyi Nie, Wei Zheng, Yaqing Wang, and Xuelong Li, A Novel Tensor Learning Model for Joint Relational Triplet Extraction, IEEE Transactions on Cybernetics (TCYB), 54(4), 2483-2494 (2023)
  • [12] Xuhong Li, Haoyi Xiong, Yi Liu, Dingfu Zhou, Zeyu Chen, Yaqing Wang, and Dejing Dou, Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models, Machine Learning (MLJ), 112(6), 2193-2209 (2023)
  • [13] Shiguang Wu, Yaqing Wang, Qinghe Jing, Daxiang Dong, Dejing Dou, and Quanming Yao, ColdNAS: Search to Modulate for User Cold-Start Recommendation, The Web Conference (TheWebConf/WWW) (2023)
  • [14] Kexin Zheng, Yaqing Wang, Quanming Yao, and Dejing Dou, Simplified Graph Learning for Inductive Short Text Classification, Conference on Empirical Methods in Natural Language Processing (EMNLP) (2022)
  • [15] Haoran Liu, Haoyi Xiong, Yaqing Wang, Haozhe An, Dongrui Wu, and Dejing Dou, Exploring the Common Principal Subspace of Deep Features in Neural Networks, Machine Learning (MLJ), 111(3), 1125-1157 (2022)
  • [16] Yaqing Wang, Song Wang, Yanyan Li, and Dejing Dou, Recognizing Medical Search Query Intent by Few-shot Learning, International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (2022)
  • [17] Yaqing Wang, Xin Tian, Haoyi Xiong, Yueyang Li, Zeyu Chen, Sheng Guo, and Dejing Dou, RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning, Findings of the ACL: NAACL (NAACL Findings) (2022)
  • [18] Quanming Yao, Yaqing Wang, Bo Han, and James T. Kwok, Efficient Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization, Journal of Machine Learning Research (JMLR), 23(136), 1-60 (2022)
  • [19] Yan Li, Xinjiang Lu, Yaqing Wang, and Dejing Dou, Generative Time Series Forecasting with Diffusion, Denoise and Disentanglement, Neural Information Processing Systems (NeurIPS) (2022)
  • [20] Yaqing Wang, Abulikemu Abuduweili, Quanming Yao, and Dejing Dou, Property-Aware Relation Networks for Few-Shot Molecular Property Prediction (Spotlight), Neural Information Processing Systems (NeurIPS) (2021)
  • [21] Yaqing Wang, Song Wang, Quanming Yao, and Dejing Dou, Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification, Conference on Empirical Methods in Natural Language Processing (EMNLP) (2021)
  • [22] Yaqing Wang, Quanming Yao, and James T. Kwok, A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Learning, The Web Conference (TheWebConf / WWW) (2021)
  • [23] Yaqing Wang, James T. Kwok, and Lionel M. Ni, Generalized Convolutional Sparse Coding with UnknownNoise, IEEE Transactionson Image Processing (TIP), 29(3), 5386-5395 (2020)
  • [24] Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni, Generalizing from a Few Examples: A Survey on Few-Shot Learning, ACM Computing Surveys (CSUR), 53(3), 1-34 (2020)
  • [25] Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni, Online convolutional sparse coding with sample-dependent dictionary, International Conference on Machine Learning (ICML) (2018)
  • [26] Yaqing Wang, Quanming Yao, James T.Kwok, and Lionel M. Ni, Scalable Online Convolutional Sparse Coding, IEEE Transactions on Image Processing (TIP), 27(10), 4850-4859 (2018)
  • [27] Yaqing Wang, James T. Kwok, Quanming Yao, and Lionel M. Ni, Zero-shot learning with a partial set of observed attributes, International Joint Conference on Neural Networks (IJCNN) (2017)

 

Update Time: 2025-03-28 01:57:07