A Lucky Guy who just Proudly Got His PhD!
Meng XIAO
Computer Network Information Center,
Chinese Academy of Sciences.
Email: xiaomeng7890@gmail.com
Address: No.2 Dongsheng South St, Beijing, P.R China, 100190
Tel: (+86) 17607102333
About Me
I received my PhD in June 2023 and am currently a Special Research Assistant (postdoctoral fellow) working in the Computer Network Information Center, Chinese Academy of Sciences, mentored by Prof. Yuanchun Zhou. I have published several papers prolifically on top-tier venues such as IEEE TKDE, ACM TKDD, AIJ, NeurIPS, IJCAI, IEEE ICDM, etc. I also serve as a PC member or reviewer on many premier international conferences and journals. My research topics include Data-centric AI, AI4LifeScience, and Scientific Data Mining. I have been honored with the Special Prize of the Chinese Academy of Sciences President Scholarship in 2023.
I want to thank my classmates and friends such as Wang Dongjie (KU), Qiao Ziyue (GBU), and Ning Zhiyuan (CNIC, CAS), as well as my instructors or collaborators such as Zhou Yuanchun (CNIC, CAS), Du Yi (CNIC, CAS), Fu Yanjie (ASU), Wu Min (A*STAR), Wang Pengyang (UM), and Liu Kunpeng (PSU) for their support. Thank you for helping me get through the long night.
Some News
[2024-04-28] Recently, my papers has been accepted by COLING-2024, DASFAA-2024 (Long Paper), Pattern Recognition and, IJCAI24!
[2024-01-23] One paper has been accepted by the Artificial Intelligence Journal! I am proud of my co-author, Hao Dong!
[2023-12-19] I have been elected as the PC member of SIGKDD 2024, ACM MM, and DASFAA 2024, the reviewer of ACM TKDD.
[2023-12-19] Recently, I have been granted the Postdoctoral Fellowship Program of CPSF!
[2023-12-16] Recently, one paper has been accepted by ACM TKDD! Special thanks to my co-author Dongjie Wang!
[2023-11-17] Recently, I have been granted the China Postdoctoral Science Foundation Funded Project, the Young Elite Scientists Sponsorship Program by BAST, and the Special Research Assistant Funded Project of the Chinese Academy of Sciences!
[2023-09-28] One paper from my advised student has been accepted by ICDM-23 (Short Paper)!
[2023-09-22] One paper has been accepted by NeurIPS 2023 (Spotlight)! Special thanks to my co-author Dongjie Wang!
[2023-09-11] I have been elected as the PC member of SIAM DataMining 2024, IJCNN 2024, Neural Networks, and ICLR 2024.
[2023-09-03] One paper has been accepted by ICDM-23 (Regular Paper, 9.37%)! Special thanks to my co-author Dongjie Wang!
[2023-08-26] I have been elected as the PC member of The Web Conf 2024.
[2023-07-15] I have been elected as the PC member of CIKM 2023.
[2023-07-13] I have been invited as Web Co-Chairs of the ICDM: DCAI (Data-Centric AI) workshop.
[2023-06-28] I have been awarded the Special Prize (Only 1% among UCAS, USTC) of President Scholarship for Postgraduate Students!
[2023-06-10] I have received my PhD degree from UCAS!
[2023-05-07] I have been nominated for the President Scholarship in 1st place (1%, within CNIC).
[2023-04-21] I have been selected as the reviewer of Information Processing & Management.
[2023-04-20] One paper has been accepted by IJCAI-23! Congratulations to my co-author Ziyue Qiao!
[2023-02-21] One paper has been accepted by IEEE TKDE! Special thanks to my co-author Ziyue Qiao!
[2023-02-09] I have been elected as the Technical PC member of IJCNN 2023!
[2022-12-27] Two of my papers have been accepted by SIAM Datamining 2023!
[2022-12-23] I have been elected as the PC member of KDD 2023. I will be able to contribute more to the data mining community!
Publication Info
2024
Meng Xiao, Dongjie Wang, Min Wu, Kunpeng Liu, Hui Xiong, and Yuanchun Zhou*, Yanjie Fu*. “Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective”. ACM Transactions on Knowledge Discovery from Data, https://doi.org/10.1145/3638059, 2024. (TKDD) [paper]
Zhiyuan Ning, Chunlin Tian, Meng Xiao, Wei Fan, Pengyang Wang, Li Li, Pengfei Wang, Yuanchun Zhou. “FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization”. The 32nd International Joint Conference on Artificial Intelligence, 2024. (IJCAI-24).
Mengyi Huang^, Meng Xiao^, Ludi Wang, Yi Du*. “DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation”. LREC-COLING 2024, 2024. (LREC-COLING)
Ziyue Qiao, Meng Xiao, Weiyu Guo, Xiao Luo, Hui Xiong, “Information Filtering and Interpolating for Semi-supervised Graph Domain Adaptation”. Pattern Recognition. 2024. (PR)
Hao Dong, Pengyang Wang, Meng Xiao, Zhiyuan Ning, Pengfei Wang, Yuanchun Zhou. “Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning”. Artificial Intelligence Journal, 2024. (AIJ) [paper]
Ping Xu, Zhiyuan Ning, Meng Xiao, Guihai Feng, Xin Li, Yuanchun Zhou, Pengfei Wang. “scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding”. 29th International Conference on Database Systems for Advanced Applications, 2o24. (DASFAA)
2023Meng Xiao, Ziyue Qiao, Yanjie Fu, Hao Dong, Yi Du*, Pengyang Wang, Hui Xiong, and Yuanchun Zhou*. “Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification.” IEEE Transactions on Knowledge and Data Engineering, doi:10.1109/TKDE.2023.3248608, 2023. (TKDE) [paper]
Dongjie Wang^, Meng Xiao^, Min Wu, Pengfei Wang, Yuanchun Zhou, Yanjie Fu*. “Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions”, NeurIPS 2023 [Spotlight]. (NIPS). 2023
Ziyue Qiao^, Luo Xiao^, Meng Xiao^, Hao Dong, Yuanchun Zhou, Hui Xiong*. “Semi-supervised Domain Adaptation in Graph Transfer Learning”, The 32nd International Joint Conference on Artificial Intelligence. (IJCAI-23). 2023
Meng Xiao, Dongjie Wang, Min Wu, Pengfei Wang, Yuanchun Zhou, Yanjie Fu*. “Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection”, 23rd IEEE International Conference on Data Mining. (ICDM), doi:10.1109/ICDM58522.2023.00078. 2023
Xunxin Cai, Meng Xiao*, Zhiyuan Ning, and Yuanchun Zhou, "Resolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-based Data Augmentation". 23rd IEEE International Conference on Data Mining, doi:10.1109/ICDM58522.2023.00107. (ICDM). 2023 [paper]
Meng Xiao, Dongjie Wang, Min Wu, Ziyue Qiao, Pengfei Wang, Kunpeng Liu, Yuanchun Zhou, and Yanjie Fu*. “Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents”, 2023 SIAM International Conference on Data Mining. (SDM). 2023 [paper] [code]
Xu Ye^, Meng Xiao^, Zhiyuan Ning, Weiwei Dai, Wenjuan Cui, Yi Du*, and Yuanchun Zhou. “NEEDED: Introducing Hierarchical Transformer to Eye Diseases Diagnosis”, 2023 SIAM International Conference on Data Mining. (SDM). 2023 [paper] [code]
2022
Ziyue Qiao, Yanjie Fu, Pengyang Wang, Meng Xiao, Zhiyuan Ning, Pengfei Wang, Yi Du*, and Yuanchun Zhou. “RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training.” In IEEE Transactions on Big Data 2022. (TBD) [paper]
2021
Meng Xiao^, Ziyue Qiao^, Yanjie Fu, Yi Du, Pengyang Wang, and Yuanchun Zhou*. “Expert Knowledge Guided Length-Variant Hierarchical Label Generation for Proposal Classification”, 2021 IEEE International Conference on Data Mining. (ICDM). 2021 [paper] [report]
2020
Yuanchun Zhou, Weijun Wang, Ziyue Qiao, Meng Xiao, and Yi Du*. “A survey on the construction methods and applications of sci-tech big data knowledge graph.” Scientia Sinica Informationis 50, no. 7 (2020): 957-987. [paper]
2019
Chao Yang, Meng Xiao, Xuan Ding, Wenwen Tian, Yong Zhai, Jie Chen, Lei Liu, and Xinyue Ye*. Exploring human mobility patterns using geo-tagged social media data at the group level. Journal of Spatial Science, 2019, 64(2): 221-238. [paper]
Special thanks to all Co-first Authors (marked by ^) and Corresponding Authors (marked by *)
Upcomming Events
2024-01-27 : I have been invited as the Special Guest on the panel of Large Language Model. (国科之星沙龙)
Details:
Interdisciplinary integration is a major feature of the current development of science and technology, an important source of the emergence of new disciplines, an effective way to cultivate innovative talents and an inherent need for economic and social development. As a new round of technological revolution and industrial transformation accelerates, some important scientific issues and key core technologies have shown signs of revolutionary breakthroughs, and new branches of disciplines and new growth points are constantly emerging.
In order to implement the national innovation-driven development strategy, promote cross-disciplinary integration development, enhance the multi-disciplinary vision and innovation and entrepreneurship capabilities of outstanding young scientific and technological talents, and build an interdisciplinary exchange platform focused on outstanding young talents, the "National Science Star" is specially held Youth Science and Technology Innovation Salon.
2023-08-04 : Data-Centric AI: Refining Representation Space from Decision-Making to Sequential-Generation Perspectives
In the realm of artificial intelligence, the quest for optimal representation space has been a critical pursuit, as it directly impacts the performance of various learning tasks. In this presentation, we delve into the concept of data-centric AI and explore two distinct approaches to refine the feature space: feature selection learning and feature generation learning. Approaching this study from two fundamental perspectives – the decision-making and sequential generation perspectives – we aim to shed light on the efficacy of each technique in enhancing the representation space. Through extensive evaluation of experimental results, it becomes evident that both feature selection learning and feature generation learning exhibit promising capabilities in shaping the representation space. This transformative capacity directly influences the performance of AI models, ultimately leading to superior results in various applications. By addressing the core tenets of data-centric AI, this research strives to highlight the crucial role played by feature selection and feature generation learning in refining the representation space. Armed with this knowledge, researchers and practitioners can make informed decisions when choosing the most suitable technique for specific AI tasks, ultimately advancing the field of artificial intelligence and its practical applications.
Dongjie Wang is currently pursuing his Ph.D. in the Department of Computer Science at the University of Central Florida. His primary research interests lie in the fields of data mining and machine learning, with a specific focus on automated data science systems, applied to large‐scale data problems, such as smart cities, anomaly detection, root cause analysis, automated urban planning, and user behavior analysis. He has published over 25+ papers in top‐tier data mining and artificial intelligence conferences and journals, such as TKDE, SIGKDD, AAAI, and KAIS. His research has been recognized with Best Paper Runner‐up Awards from SIGSPATIAL 2020 and ICDM 2021, respectively.
Acknowledgment & Project Info
Postdoctoral Fellowship Program, China Postdoctoral Science Foundation. (PI, 2024-2026, GZC20232736)
the Special Research Assistant Funded Project of the Chinese Academy of Sciences. Chinese Academy of Sciences. (PI, 2024-2026)
Young Elite Scientists Sponsorship Program by Beijing Association for Science and Technology. (PI, 2024-2026)
China Postdoctoral Science Foundation Funded Project. (PI, 2024-2026, 2023M743565)
Mining software for photoelectric catalytic materials based on domain knowledge graph. Ministry of Science and Technology of the People´s Republic of China. National Key Research and Development Program for Young Scientists. (project participant, 2023-2025, 2022YFF0712200)
Building of Domain Specific Big Data Knowledge Graph. Natural Science Foundation of China. Key Program. (project participant, 2019-2023, No.61836013)
A study of key technologies for visual analysis of large-scale space-time data correlation. Beijing Municipal Science & Technology Commission. Beijing Natural Science Foundation projects. (project participant, 2021-2023, No. 4212030)
Design and Research of Open Sharing Policy and Platform of NSFC. Natural Science Foundation of China. Special Project. (project participant, 2020-2021, L1924075)
Research and Application of New Innovation Method System based on CrowdIntelligent. Ministry of Science and Technology of the People´s Republic of China. Innovation Method Special Fund (project participant, 2020-2021, No.2019IM020100)
Eye health knowledge graph construction and intelligent application. Chinese Academy of Sciences. STS project. (project participant, 2021-2022, KFJ-STS-QYZD-2021-11-001)
Academic Services
Chair:
ICDMW-DCAI: 1st(2023)
PC Member:
KDD: 2023, 2024
ICLR: 2024
ACM MM: 2024
ACM WWW: 2024
SDM: 2024
CIKM: 2023 2024
DASFAA: 2024
IJCNN: 2023 2024
IEEE BigData: 2024
From my perspective, Data Mining and Artificial Intelligence stand as significantly practical research fields. My students and I aspire to delve deeper into these domains, tackling a broader range of intriguing problems that the real world presents. Through our collective efforts, I envision us not only uncovering novel insights but also contributing meaningful solutions to challenges faced by society today.