The 40th AAAI Conference on Artificial Intelligence (AAAI 2026) was held from January 20 to 27, 2026, at the Singapore EXPO in Singapore. As one of the most influential international conferences in artificial intelligence, AAAI 2026 featured extensive discussions on foundation models, machine learning theory, agent systems, intelligent decision-making, and the societal impacts of AI, presenting recent advances and emerging trends in the field.

During the conference, the AAAI New Faculty Highlights program attracted broad attention. The program solicited nominations from the global AI research community and selected early-career faculty members whose research directions had begun to take shape, highlighting their representative contributions and future research visions. Selected participants were invited to give presentations in the dedicated New Faculty Highlights sessions. For AAAI 2026, a total of 31 scholars worldwide were selected for this program.

At AAAI 2026, Dr. Yaqing Wang, Associate Professor at the Beijing Institute of Mathematical Sciences and Applications (BIMSA), was selected for the AAAI 2026 New Faculty Highlights program and delivered an invited presentation.

Dr. Wang received her Ph.D. in Computer Science and Engineering from the Hong Kong University of Science and Technology in 2019. From 2019 to 2024, she served as a Senior Researcher at Baidu Research. She has published more than 30 papers in leading international conferences, including NeurIPS, ICML, ICLR, KDD, The Web Conference, SIGIR, AAAI, IJCAI, and EMNLP, as well as in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Journal of Machine Learning Research (JMLR), and IEEE Transactions on Image Processing (TIP). Her work has received over 5,500 citations. She currently serves as an Action Editor for the Journal Citation Reports (JCR) Q1 journal Neural Networks and as an Associate Editor for the ACL Rolling Review. She was selected for the Beijing Science and Technology Rising Star Program in 2025 and was included in the World’s Top 2% Scientists list in both 2024 and 2025.

Dr. Wang’s research focuses on data-efficient machine learning and intelligent agents, including few-shot learning, meta-learning, in-context learning, and cold-start recommendation. Her work aims to advance the theoretical foundations of artificial intelligence while bridging methodological developments with real-world applications, particularly at the intersection of AI and scientific computing.

In the New Faculty Highlights session held on January 24, Dr. Wang delivered a talk entitled “From Few-Shot Learning to Data-Efficient Intelligence.” In her presentation, she systematically outlined her research perspective and recent progress on data-efficient intelligence. She emphasized that while deep learning and large-scale models have achieved remarkable success in data-rich settings, real-world applications such as scientific discovery and drug development are often characterized by expensive data acquisition, limited annotations, and dynamically changing environments. These challenges call for reliable learning under limited data and interaction budgets.

Drawing inspiration from human few-shot learning mechanisms, the talk highlighted the central role of prior knowledge in efficient learning. Dr. Wang reviewed the theoretical foundations of few-shot learning and extended these ideas to in-context learning phenomena in large language models, discussing their connections to meta-learning and rapid adaptation mechanisms. She further extended the discussion to intelligent agent systems, introducing her representative work on data-efficient agent learning. These methods aim to achieve fast alignment with user preferences and task requirements under minimal interaction and supervision, offering new perspectives for building reliable, controllable, and personalized intelligent systems.