汪炀，教授，博士生导师。现任中国科学技术大学软件学院副院长，中国计算机学会传感器网络专业委员会执行委员，中国计算机学会智能交通分会执行委员，安徽省杰出青年科学基金获得者。2002年本科毕业于中国科学技术大学计算机系，2007年于中国科学技术大学获计算机应用专业博士学位。主要研究方向包括数据挖掘、开放机器学习、AI for Science、物联网与城市计算等。作为课题/系统负责人先后承担了中国科学院稳定支持基础研究领域青年团队计划项目、中国科学院“智能科学家系统”专项、国家重大科研仪器研制项目（部门推荐），并作为项目负责人主持了国家自然科学基金面上项目（连续3项）和安徽省杰青项目在内的纵向课题40余项。近年来，聚焦人工智能基础理论研究并致力于发展面向物质科学的人工智能方法，在人工智能及交叉科学领域顶级国际学术会议及期刊上以第一/通信作者身份发表CCF-B类、相当水平及以上高水平论文100余篇（其中CCF-A类及相当水平顶级论文60余篇，包括JACS、IEEE TPAMI、ICML、ICLR、NeurIPS、SIGKDD等），多次入选人工智能顶会Oral和Spotlight Paper。获2017年度IBM全球杰出学者奖（IBM Faculty Award）、2020年CSC中国优秀教师奖等，并多次获得省、学会及学校的优博或优博提名导师奖。

电子邮箱：angyan@ustc.edu.cn       联系电话：0512- 87161105

联系地址：江苏省苏州市工业园区若水路99号中国科学技术大学苏州高等研究院绍钧楼

个人主页：https://di.ustc.edu.cn/_upload/tpl/15/71/5489/template5489/PersonalSite/index.html

主要研究方向：

基础理论研究：数据挖掘、开放机器学习、AI for Science、物联网与城市计算等

 前沿应用探索：人工智能与物质科学交叉应用、人工智能与半导体材料交叉应用等

代表性著作

[1]     [IEEE TPAMI] Kun Wang, Yuxuan Liang*, Xinglin Li, Guohao Li, Bernard Ghanem, Roger Zimmermann, Zhengyang Zhou, Huahui Yi, Yudong Zhang, Yang Wang*. Brave the Wind and the Waves: Discovering Robust and Generalizable Graph Lottery Tickets, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. (CCF-A, IF=20.8)

[2]     [ICML 2024] Zhe Zhao, Pengkun Wang*, Haibin Wen, Wei Xu, Lai Song, Qingfu Zhang, Yang Wang*, Two Fists, One Heart: Multi-Objective Optimization Based Strategy Fusion for Long-tailed Learning, International Conference on Machine Learning, 2024. (CCF-A)

[3]     [ICML 2024] Chen Zhang, Qiang He, Zhou Yuan, Elvis S. Liu*, Hong Wang, Jian Zhao, Yang Wang*, Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment, International Conference on Machine Learning, 2024. (CCF-A)

[4]     [NeurIPS 2024] Zhongchao Yi, Zhengyang Zhou, Qihe Huang, Yanjiang Chen, Liheng Yu, Xu Wang, Yang Wang*. Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework, The 38th Conference on Neural Information Processing Systems, 2024. (CCF-A, Oral)

[5]     [NeurIPS 2024] Zhe Zhao, Haibin Wen, Zikang Wang, Pengkun Wang, Fanfu Wang, Song Lai, Qingfu Zhang, Yang Wang. Breaking Long-Tailed Learning Bottlenecks: A Controllable Paradigm with Hypernetwork-Generated Diverse Experts, The 38th Conference on Neural Information Processing Systems, 2024. (CCF-A, Spotlight)

[6]     [NeurIPS 2024] Pengkun Wang^#, Zhe Zhao^#, Haibin Wen, Fanfu Wang, Binwu Wang, Qingfu Zhang*, Yang Wang*. LLM-AutoDA: Large Language Model-Driven Automatic Data Augmentation for Long-tailed Problems, The 38th Conference on Neural Information Processing Systems, 2024. (CCF-A)

[7]     [NeurIPS 2024] Kuo Yang, Zhengyang Zhou*, Qihe Huang, Limin Li, Yuxuan Liang, Yang Wang*. Improving Generalization of Dynamic Graph Learning via Environment Prompt, The 38th Conference on Neural Information Processing Systems, 2024. (CCF-A)

[8]     [NeurIPS 2023] Qihe Huang, Lei Shen, Ruixin Zhang, Zhengyang Zhou*, Yang Wang*. CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement, The 37th Conference on Neural Information Processing Systems, 2023. (CCF-A)

[9]     [ICLR 2025] Yudong Zhang, Xuan Yu, Xu Wang*, Zhaoyang Sun, Chen Zhang, Pengkun Wang, Yang Wang*, COFlowNet: Conservative Constraints on Flows Enable High-Quality Candidate Generation, International Conference on Learning Representations, 2025. (清华-A)

[10] [ICLR 2025] Shuai Zhang, Junfeng Fang, Xuqiang Li, hongxin xiang, ALAN XIA, Ye Wei, Wenjie Du*, Yang Wang*, Iterative Substructure Extraction for Molecular Relational Learning with Interactive Graph Information Bottleneck, International Conference on Learning Representations, 2025. (清华-A)

[11] [ICLR 2025] Zaige Fei^#, Fan Xu^#, Junyuan Mao, Yuxuan Liang, Qingsong Wen, Kun Wang*, Hao Wu*, Yang Wang*, Open-CK: A Large Multi-Physics Fields Coupling benchmarks in Combustion Kinetics, International Conference on Learning Representations, 2025. (清华-A)

[12] [ICLR 2024] Kun Wang, Hao Wu, Yifan Duan, Guibin Zhang, Kai Wang, Xiaojiang Peng, Yu Zheng, Yuxuan Liang*, Yang Wang*, NuwaDynamics: Discovering and Updating in Causal Spatio-Temporal Modeling, International Conference on Learning Representations, 2024. (清华-A, Spotlight)

[13] [ICLR 2024] Binwu Wang, Pengkun Wang*, Wei Xu, Xu Wang, Yudong Zhang, Kun Wang, Yang Wang*, Kill Two Birds with One Stone: Rethinking Data Augmentation for Deep Long-tailed Learning, International Conference on Learning Representations, 2024. (清华-A)

[14] [ICLR 2023] Zhengyang Zhou, Qihe Huang, Gengyu Lin, Kuo Yang, Yang Wang*, Remedying Dynamic Graph Topology-task Discordance via Target Homophily, International Conference on Learning Representations, 2023. (清华-A)

[15] [ICLR 2023] Kun Wang, Yuxuan Liang*, Pengkun Wang,  Xu Wang, Pengfei Gu, Junfeng Fang, Yang Wang*, Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective, International Conference on Learning Representations, 2023. (清华-A)

[16] [JACS] Guokun Yang^#, Hengyu Xiao^#, Hao Gao^#, Baicheng Zhang, Wei Hu, ChengChen, Qinyu Qiao, Guozhen Zhang, Shuo Feng, Daobin Liu, Yang Wang*, Jun Jiang*, Yi Luo*. Repairing Noise-Contaminated Low-Frequency Vibrational Spectra with an Attention U-Net Neural Network, Journal of the American Chemical Society, 2024. (化学领域顶级期刊, IF=14.5)

[17] [JACS] Wenjie Du^#, Fenfen Ma^#, Baicheng Zhang^#, Jiahui Zhang, Di Wu, Edward Sharman, Jun Jiang*, Yang Wang*. Spectroscopy-Guided Deep Learning Predicts Solid-Liquid Surface Adsorbate Properties in Fire-new and Unseen Solvents, Journal of the American Chemical Society, 2023. (化学领域顶级期刊, IF=14.5)