冯文杰
邮箱: fengwenjie@ustc.edu.cn
个人主页: https://wenchieh.github.io
地址: 中科大高新校区一号学科楼A521A
主要研究兴趣：
My research interests include large-scale data mining, trustworthy and secure AI & System, particularly in machine learning techniques and real applications, including graph mining & learning, ML privacy, foundation models, FinTech, learning system. I have several publications appeared in several top conferences (e.g., ICML, NeurIPS, ICDE, WWW, ACM MM) and journals (e.g., TKDE, TKDD, TOSEM). Moreover, I have served as the PC member for top-tier international conferences including ICML, CVPR, WWW, SIGKDD, NeurIPS, IJCAI, etc., and the invited reviewer for prestigious journals including TKDE, TDSC, TKDD, KAIS, and TNNLS.
招生/招聘信息：
General Hiring Requirements:
Passion, determination and preservation to conduct high-quality research.
Good communication and collaboration skills.
Strong coding ability (C/C++ or Python).
1. Hiring tenure-track faculties and postdocs in DM/ML:
With PhD degree (or graduate soon)
At least three first-author papers on top tier conferences
2. Hiring PhD students from USTC and masters:
English (CET-6 score 500+, or equal levels)
Strong mathematics foundations (calculus, linear algebra, optimization, information theory, probability and statistics, etc.)
3. Hiring master students and undergraduate interns:
Good foundation in mathematics (calculus, linear algebra, probability and statistics)
Experience in high-level competitions (e.g., ACM-ICPC, KDD-Cup, ) or top-tier publications will be preferred.
教育经历：
Sep. 2010 – Jun. 2014, Bachelor in Computer Science and Technology, Beijing Jiaotong University, China
Sep. 2014 – Sep. 2020, Ph.D. in Computer Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, China
研究经历：
Feb. 2025 – Now, Professor, University of Science and Technology of China
Oct. 2020 – Feb. 2025, Research Fellow, National University of Singapore
主要论著：
[1] Wenjie Feng, Li Wang, Bryan Hooi, See-Kiong NG, Shenghua Liu. Interrelated Dense Subgraph Detection in Multilayer Networks. IEEE International Conference on Data Engineering (ICDE), 2025.
[2] Jianqing Xu, Shen Li, Jiaying Wu, Miao Xiong, Ailin Deng, Yuge Huang, Jiazhen Ji, Wenjie Feng, Shouhong Ding, Bryan Hooi. ID3: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition. Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS), 2024.
[3] Brian Formento, Wenjie Feng, Chuan-Sheng Foo, Anh Tuan Luu, See-Kiong Ng. Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks. 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics. (NAACL), 2024.
[4] Wenjie Feng, Li Wang, Bryan Hooi, See-Kiong NG, Shenghua Liu. Interrelated Dense Subgraph Detection in Multilayer Networks. IEEE Transactions on Knowledge and Data Engineering (TKDE 2024). Vol. 36, no. 11, pp. 6462-6476.
[5] Naibo Wang, Yuchen Deng, Wenjie Feng#, Shichen Fan, Jianwei Yin, See-Kiong Ng. One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity. ACM International Conference on Multimedia. ACM Multimedia, 2024.
[6] Wenjie Feng, Shenghua Liu, Danai Koutra, Xueqi Cheng. Unified Dense Subgraph Detection: Fast Spectral Theory based Algorithms. IEEE Transactions on Knowledge and Data Engineering (TKDE 2023). Vol. 36, no. 3, pp. 1356-1370.
[7] Wenjie Feng, Shenghua Liu, Xueqi Cheng. Hierarchical Dense Pattern Detection in Tensors. ACM Transactions on Knowledge Discovery from Data (TKDD 2023). Vol. 17, no. 6 , pp.1-29.
[8] Mingqi Yang, Wenjie Feng,Yanming Shen, Bryan Hooi. Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering. Proceedings of the 40th International Conference on Machine Learning (ICML), 2023.
[9] Naibo Wang, Wenjie Feng, Jianwei Yin, See-Kiong Ng. EasySpider: A No-Code Visual System for Crawling the Web. Proceedings of the ACM Web Conference (The WebConf), 2023.
[10] Naibo Wang, Wenjie Feng#, Yuchen Deng, Moming Duan, Fusheng Liu, See-Kiong Ng. Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning. Transactions on Machine Learning Research (TMLR), 2023.
[11] Xiaobing Sun∗, Wenjie Feng∗, Shenghua Liu, Yuyang Xie, Bryan Hooi, Wenhan Wang, and Xueqi Cheng. MonLAD: Money Laundering Agents Detection in Transaction Streams. International conference on Web Search and Data Mining (WSDM), 2022.
[12] Miao Xiong, Shen Li, Wenjie Feng, Ailin Deng, Jihai Zhang, Bryan Hooi. Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation. Transactions on Machine Learning Research (TMLR), 2022.
[13] Yuntao Du, Jindong Wang, Wenjie Feng#, Sinno Pan, Tao Qin, Renjun Xu, Chongjun Wang. AdaRNN: Adaptive Learning and Forecasting of Time Series. ACM International Conference on Information and Knowledge Management (CIKM), 2021
[14] Wenjie Feng, Shenghua Liu, Danai Koutra, Huawei Shen, and Xueqi Cheng. SpecGreedy: Unified Dense Subgraph Detection. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2020. [Best Student Paper Award]
[15] Wenjie Feng, Shenghua Liu, Xueqi Cheng. CachCore: Catching Hierarchical Dense Subtensor. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019.
[16] Jindong Wang, Yiqiang Chen, Wenjie Feng, Han Yu, Meiyu Huang, Qiang Yang. Transfer Learning with Dynamic Distribution Adaptation. ACM Transactions on Intelligent Systems and Technology (TIST), 2019
[17] Wenjie Feng, Shenghua Liu, Christos Faloutsos, Bryan Hooi, Huawei Shen, and Xueqi Cheng. Beyond outliers and on to micro-clusters: Vision-guided anomaly detection. Pacific-Asia Conference in Knowledge Discovery and Data Mining (PAKDD), 2019
[18] Jindong Wang*, Wenjie Feng∗, Yiqiang, Chen, Han Yu, Philip S Yu. Visual Domain Adaptation with Manifold Embedded Distribution Alignment. ACM International Conference on Multimedia. ACM Multimedia, 2018.