The goal is to model intracranial neural signals to empower neural science with the ability to understand brain activities quantitatively. At this stage, we put our focus on serving epilepsy patients, shorterning their treatment cycle by automatically identifying epileptic foci.

Epilepsy is one of the most common and serious neurological disorders, affecting approximately 65 million people worldwide. One-third of patients are medication-resistant, and surgical resection of the lesion tissue through neurosurgery is necessary. We develop deep models to automatically identify epileptic waves for seizure localization. To do this, we mainly employ the SEEG method for brain signal recording, which inserts depth electrodes into the human brain and provides recordings from both cortical and subcortical structures simultaneously.

We first study epileptic diffusion patterns to identify epileptic waves for a particular individual by training from his or her historical data (BrainNet, Chen et al., KDD'22). To further achieve transferability between different individuals, we propose to learn generalized and individual-independent representations for brain signals via self-supervised pre-training based on capturing spatio-temporal correlations among signals (MBrain, Cai et al., KDD'23; PPi). With the continue extention of both our collected data and downstream tasks, we are working on pre-training foundation model with large parameters (BPT).

Papers: (Chen et al., KDD'22) (Cai et al., KDD'23)

The goal is to understand and detect abnormal vertexes (e.g., users with anomalous behaviors) in large-scale social and information networks. Generally, we study the question of can abnormality be detected by graph neural networks? Despite the fact that GNN has emerging as a powerful tool for modeling graph data, we find that most GNNs fail to identify abnormalities emprically. We further explore the reason behind this phenomenon in both spectral and spatial view of GNNs and propose the improving methods respectively (Chai et al., IJCAI'22, Sun et al., IJCAI'22).

Our work has been widely applied in many scenarios. In telecommunications field, we propose to spot telemarketing frauds, with an emphasis on unveiling the "precise fraud" phenomenon and the strategies that are used by fraudsters to precisely select targets (Yang et al., TKDE'19). Our study is conducted on a one-month complete dataset of telecommunication metadata in Shanghai with 54 million anonymous users and 698 million call logs.

In financial field, we unearth the correlation between users' anomalous behaviors and their communication network structure in an online lending platform. Moreover, we propose a novel problem: how to identify muti-type fraudsters (Yang et al., CIKM'19)? Our proposed framework can uniformly identify two types of frauds: default borrowers, who will default on a loan to the platform, and cheating agents, who recruit and teach borrowers to cheat by providing false information and faking application materials.

Papers: (Chai et al., IJCAI'22), (Sun et al., IJCAI'22), (Yang et al., TKDE'19), (Yang et al., CIKM'19)

Codes: [AMNet], [PathNet]

Benchmark: [DGraph]

Time series modeling has attracted extensive research efforts; however, achieving both reliable efficiency and interpretability from a unified model still remains a challenging problem.

Our recent work proposes to model time series from the perspective of graphs. More specifically, we aim to capture the intrinsic factors and their transitions behind the time series, and describe how these factors affect the time series evolution. To achieve this, we respectively propose the shapelet based method (Time2Graph, Cheng et al., AAAI'20; Time2Graph+, Cheng et al., TKDE'21) and a dynamic graph neural network based model (EvoNet, Hu et al., WSDM'21). Our proposed methods not only achieves clear improvements comparing with state-of-the-art baselines in many tasks, but also provide valuable insights towards explaining the results of prediction results.

Our work has been applied in real-world scenarios, such as network traffic anomaly monitor, as a common service of Alicloud, and electricity-theft behavior detection (Hu et al., WWW'20), collaborated with Alibaba and State Grid Corporation of China.

Papers: (Cheng et al., TKDE'21), (Cheng et al., AAAI'20), (Hu et al., WSDM'21), (Hu et al., WWW'20)

Codes: [Time2Graph], [Time2Graph+], [EvoNet]

Network data in real-world tends to be error-prone due to incomplete sampling or imperfect measurements. This in turn results in inaccurate results when performing network analysis or modeling, such as node classification and link prediction, on these flawed networks.

Our research aims to reconstruct a reliable network from a flawed one, a process referred to network enhancement. More specifically, network enhancement aims to detect the noisy links that are observed in the network but should not exist in the real world, as well as to complement the missing links that do indeed exist in the real world yet remain unobserved.

From one perspective, we turn the network enhancement problem into edge sequences generation, and employ a deep reinforcement learning framework to solve it, which takes advantage of downstream task to guide the network denoising process (NetRL, Xu et al., TKDE'21). From another perspective, we construct a self-supervised learning framework that identifies missing links and nosiy links simultaneously by leveraging the mutual influence of them (E-Net, Xu et al., TKDE'20)

Moreover, we study the model robustness against adversarial attacks. Our work shows that even without any information about the target model, one can still perform effective attacks (Xu et al., AAAI'22a). To handle such perturbations, we further propose an unsupervised defense technique to robustify pre-trained deep graph models (Xu et al., AAAI'22b).

Papers: (Xu et al., TKDE'20), (Xu et al., TKDE'21), (Xu et al., AAAI'22a), (Xu et al., AAAI'22b).

Codes & benchmark: [NetRL], [E-Net], [Graph Robustness Benchmark]

2023

Taoran Fang, Yunchao Zhang, Yang Yang, Chunping Wang, and Lei Chen. Universal Prompt Tuning for Graph Neural Networks. In Proceedings of the Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS'23), 2023.

Daoze Zhang*, Zhizhang Yuan*, Yang Yang, Junru Chen, Jingjing Wang, and Yafeng Li. Brant: Foundation Model for Intracranial Neural Signal. In Proceedings of the Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS'23), 2023 (*: equal contribution).

Zhizhang Yuan*, Daoze Zhang*, Yang Yang, Junru Chen, and Yafeng Li. PPi: Pretraining Brain Signal Model for Patient-independent Seizure Detection. In Proceedings of the Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS'23), 2023 (*: equal contribution).

Jiarong Xu, Renhong Huang, Xin Jiang, Yuxuan Cao, Carl Yang, Chunping Wang, and Yang Yang. Better with Less: A Data-Centric Prespective on Pre-Training Graph Neural Networks. In Proceedings of the Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS'23), 2023.

Haoran Deng, Yang Yang, Jiahe Li, Haoyang Cai, Shiliang Pu, and Weihao Jiang. Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds. In Proceedings of the Twenty-Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'23), 2023. [PDF] [Code]

Donghong Cai*, Junru Chen*, Yang Yang, Teng Liu, and Yafeng Li. MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals. In Proceedings of the Twenty-Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'23), 2023 (*: equal contribution). [PDF]

Yuxuan Cao*, Jiarong Xu*, Carl Yang, Jiaan Wang, Yunchao Mercer Zhang, Chunping Wang, Lei Chen, and Yang Yang. When to Pre-Train Graph Neural Networks? An Answer from Data Generation Perspective! In Proceedings of the Twenty-Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'23), 2023 (*: equal contribution). [PDF] [Code]

Xuan Yang, Yang Yang, Chenhao Tan, Yinghe Lin, Zhengzhe Fu, Fei Wu, and Yueting Zhuang. Unfolding and Modeling the Recovery Process after COVID Lockdowns. In Nature Scientific Reports, 2023. [PDF] [Online] [Poster]

Taoran Fang, Zhiqing Xiao, Chunping Wang, Jiarong Xu, Xuan Yang, and Yang Yang. DropMessage: Unifying Random Dropping for Graph Neural Networks. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'23), 2023. [PDF] [Code] (Distinguished Paper Award)

Ziwei Chai, Yang Yang, Jiawang Dan, Sheng Tian, Changhua Meng, Weiqiang Wang, and Yifei Sun. Towards Learning to Discover Money Laundering Sub-network in Massive Transaction Network. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'23), 2023. [PDF]

2022

Junru Chen*, Yang Yang*, Tao Yu, Yingying Fan, Xiaolong Mo, and Carl Yang. BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning. In Proceedings of the Twenty-Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'22), 2022 (*: equal contribution). [PDF]

Xuanwen Huang, Yang Yang, Yang Wang, Chunping Wang, Zhisheng Zhang, Jiarong Xu, Lei Chen and Michalis Vazirgiannis. DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection. In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS’22), 2022. [PDF] [DGraph Data]

Xuan Yang, Yang Yang, Jintao Su, Yifei Sun, Shen Fan, Zhongyao Wang, Jun Zhan, and Jingmin Chen. Who's Next: Rising Star Prediction via Diffusion of User Interest in Social Networks. In IEEE Transaction on Knowledge and Data Engineering (TKDE), 2022 (accepted). [PDF]

Jiarong Xu, Yang Yang, Junru Chen, Xin Jiang, Chunping Wang, Jiangang Lu, and Yizhou Sun. Unsupervised Adversarially Robust Representation Learning on Graphs. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'22), 2022. [PDF] [Code]

Ziwei Chai*, Siqi You*, Yang Yang, Shiliang Pu, Jiarong Xu, Haoyang Cai, and Weihao Jiang. Can Abnormality be Detected by Graph Neural Networks? In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22), 2022 (*: equal contribution). [PDF] [Code]

Yifei Sun, Haoran Deng, Yang Yang, Chunping Wang, Jiarong Xu, Renhong Huang, Linfeng Cao, Yang Wang, and Lei Chen. Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network. In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22), 2022. [PDF] [Code]

Jiarong Xu, Yizhou Sun, Xin Jiang, Yanhao Wang, Chunping Wang, Jiangang Lu, and Yang Yang. Blindfolded Attackers Still Threatning: Strict Black-Box Adversarial Attacks on Graphs. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'22), 2022. [PDF] [Code]

Lei Chen, Guanying Li, Zhongyu Wei, Yang Yang, Baohua Zhou, Qi Zhang, Xuanjing Huang. A Progressive Framework for Role-Aware Rumor Resolution. In Proceedings of the 29th International Conference on Computational Linguistics (COLING'22), 2022, pages 2748–2758. [PDF]

2021

Jiarong Xu, Yang Yang, Shiliang Pu, Yao Fu, Jun Feng, Weihao Jiang, Jiangang Lu, and Chunping Wang. NetRL: Task-aware Network Denoising via Deep Reinforcement Learning. In IEEE Transaction on Knowledge and Data Engineering (TKDE), 2021. [PDF] [Code]

Ziqiang Cheng, Yang Yang, Shuo Jiang, Wenjie Hu, Zhangchi Ying, Ziwei Chai, and Chunping Wang. Time2Graph+: Bridging Time Series and Graph Representation Learning via Multiple Attentions. In IEEE Transaction on Knowledge and Data Engineering (TKDE), 2021. [PDF] [Code]

Xuanwen Huang, Yang Yang, Ziqiang Cheng, Shen Fan, Zhongyao Wang, Juren Li, Jun Zhang, and Jingmin Chen. How Powerful are Interest Diffusion on Purchasing Prediction: A Case Study of Taocode. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'21), 2021. [PDF]

Ping Shao, Yang Yang, Shengyao Xu, and Chunping Wang. Network Embedding via Motifs. In ACM Transactions on Knowledge Discovery from Data (TKDD), 2021. [PDF] [Code]

Wenjie Hu, Yang Yang, Ziqiang Cheng, Carl Yang, and Xiang Ren. Time-Series Event Prediction with Evolutionary State Graph. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM'21), 2021. [PDF] [Code] [Demo]

Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, and Jie Tang. Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning. In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS’21), 2021. [PDF] [Graph Robustness Benchmark]

2020

Ziqiang Cheng, Yang Yang, Wei Wang, Wenjie Hu, Yueting Zhuang, and Guojie Song. Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'20), 2020. [PDF] [Code]

Wenjie Hu, Yang Yang, Jianbo Wang, Xuanwen Huang, and Ziqiang Cheng. Understanding Electricity-Theft Behavior via Multi-Source Data. In Proceedings of the 29th World Wide Web Conference (WWW'20), 2020. [PDF] [Slides]

Jiarong Xu, Yang Yang, Chunping Wang, Zongtao Liu, Jing Zhang, Lei Chen, and Jiangang Lu. Robust Network Enhancement from Flawed Networks. In IEEE Transaction on Knowledge and Data Engineering (TKDE), 2020. [PDF] [Code]

Xiaobin Tang, Jing Zhang, Bo Chen, Yang Yang, Hong Chen, and Cuiping Li. BERT-INT: A BERT-based Interaction Model For Knowledge Graph Alignment. In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'20), 2020. [PDF]

Wenjie Hu, Yang Yang, Liang Wu, Zongtao Liu, Zhanlin Sun, and Bingshen Yao. Capturing Evolution Genes for Time Series Data. Preprint. [arxiv]

Rui Feng, Yang Yang, Yuehan Lyu, Chenhao Tan, Yizhou Sun, and Chunping Wang. Learning Fair Representations via an Adversarial Framework. Preprint. [arxiv]

2019

Yang Yang, Yuhong Xu, Yizhou Sun, Yuxiao Dong, Fei Wu, and Yueting Zhuang. Mining Fraudsters and Fraudulent Strategies in Large-Scale Mobile Social Networks. In IEEE Transaction on Knowledge and Data Engineering (TKDE), 2019. [PDF]

Zongtao Liu, Yang Yang, Wei Huang, Zhongyi Tang, Ning Li, and Fei Wu. How Do Your Neighbors Disclose Your Information: Social-Aware Time Series Imputation. In Proceedings of the Twenty-Eighth World Wide Web Conference (WWW'19), 2019. [PDF] [Code] [BIB]

Ziqiang Cheng, Yang Yang, Chenhao Tan, Denny Cheng, Alex Cheng, and Yueting Zhuang. What Makes a Good Team? A Large-scale Study on the Effect of Team Composition in Honor of Kings. In Proceedings of the Twenty-Eighth World Wide Web Conference (WWW'19, short paper), 2019. [PDF] [BIB] [long version on arxiv]

Yang Yang*, Yuhong Xu*, Chunping Wang, Yizhou Sun, Fei Wu, Yueting Zhuang, and Ming Gu. Understanding Default Behavior in Online Lending. In Proceedings of the Twenty-Eighth Conference on Information and Knowledge Management (CIKM'19), 2019 (*: equal contribution). [PDF] [BIB]

Rui Feng, Yang Yang, Yizhou Sun, and Chunping Wang. A Unified Network Embedding Algorithm for Multi-type Similarity Measures. In 1st International Workshop on Graph Representation Learning and its Applications (GRLA'19), 2019. [PDF] [BIB]

2018

Yang Yang, Zongtao Liu, Chenhao Tan, Fei Wu, Yueting Zhuang, and Yafeng Li. To Stay or to Leave: Churn Prediction for Urban Migrants in the Initial Period. In Proceedings of the Twenty-Seventh World Wide Web Conference (WWW'18), 2018, pages 967-976. [PDF] [Slides] [Data] [BIB]

Yang Yang, Chenhao Tan, Zongtao Liu, Fei Wu, and Yueting Zhuang. Urban Dreams of Migrants: A Case Study of Migrant Integration in Shanghai. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), 2018, pages 507-514. [PDF] [BIB]

Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. Dynamic Network Embedding by Modeling Triadic Closure Process. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), 2018, pages 571-578. [PDF] [Code] [BIB]

Rui Feng*, Yang Yang*, Wenjie Hu, Fei Wu, and Yueting Zhuang. Representation Learning for Scale-free Networks. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), 2018, pages 282-289 (*: equal contribution). [PDF] [Code] [BIB]

Yupeng Gu, Yizhou Sun, Yanen Li, and Yang Yang. RaRE: Social Rank Regulated Large-scale Network Embedding. In Proceedings of the Twenty-Seventh World Wide Web Conference (WWW'18), 2018, pages 359-368. [PDF] [BIB]

Menghan Wang, Xiaolin Zheng, Yang Yang, and Kun Zhang. Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), 2018, pages 2516-2523. [PDF] [BIB]

Jun Feng, Minlie Huang, Li Zhao, Yang Yang, and Xiaoyan Zhu. Reinforcement Learning for Relation Extraction from Noisy Data. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), 2018, pages 5779-5786. [PDF] [BIB]

2017

Yang Yang, Jie Tang, and Juanzi Li. Learning to Infer Competitive Relationships in Heterogeneous Networks. In ACM Transactions on Knowledge Discovery from Data (TKDD), 2017. [PDF] [BIB]

Yuxiao Dong, Nitesh V. Chawla, Jie Tang, Yang Yang, and Yang Yang. User Modeling on Demographic Attributes in Large-Scale Mobile Social Networks. In ACM Transactions on Information Systems (TOIS), 2017, Volume 35, Issue 4. [PDF]

Xinyang Jiang, Siliang Tang, Yang Yang, Zhou Zhao, Fei Wu, and Yueting Zhuang. Detecting Temporal Proposal for Action Localization with Tree-structured Search Policy. In Proceedings of the 25th Conference on ACM Multimedia (ACM Multimedia'17), 2017, pages 1069-1077.

Yang Yang and Jie Tang. Computational Models for Social Influence and Diffusion In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), 2017. (Tutorial)

2016

Yang Yang, Jia Jia, Boya Wu, and Jie Tang. Social Role-Aware Emotion Contagion in Image Social Networks. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), 2016, pages 65-71. [PDF] [Data]

Jun Feng, Minlie Huang, Yang Yang, and Xiaoyan Zhu. GAKE: Graph Aware Knowledge Emebdding. In Proceedings of the 27th International Conference on Computational Linguistics (COLING'16), 2016, pages 641-651. [PDF]

Boya Wu, Jia Jia, Yang Yang , Peijun Zhao, Jie Tang, and Qi Tian. Inferring Emotional Tags From Social Images With User Demographics. In IEEE Transactions on Multimedia (TMM) , 2016, accepted. [PDF]

2015

Yang Yang, Yizhou Sun, Jie Tang, Bo Ma, and Juanzi Li. Entity Matching across Heterogeneous Sources. In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'15), 2015, pages 1395-1404. [PDF] [Data&Code]

Yang Yang, Jie Tang, Cane Wing-Ki Leung, Yizhou Sun, Qicong Chen, Juanzi Li, and Qiang Yang. RAIN: Social Role-Aware Information Diffusion. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15), 2015, page 367-373. [PDF] [Data&Code] [Data] [BIB]

Yang Yang and Jie Tang. Beyond Query: Interactive User Intention Understanding. In Proceedings of the 2015 IEEE International Conference on Data Mining (ICDM'15), 2015, pages 519-528. [PDF]

Boya Wu, Jia Jia, Yang Yang, Peijun Zhao, and Jie Tang. Understanding the Emotions Behind Social Images: Inferring with User Demographics. In Proceedings of 2015 IEEE International Conference on Multimedia and Expo (ICME'15), 2015. [PDF]

2014 and prior

Yang Yang, Jia Jia, Shumei Zhang, Boya Wu, Qicong Chen, Juanzi Li, Chunxiao Xing, and Jie Tang. How Do Your Friends on Social Media Disclose Your Emotions? In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14), 2014, pages 306-312. [PDF] [Data&Code]

Yang Yang, Walter Luyten, Lu Liu, Marie-Francine Moens, Jie Tang, and Juanzi Li. Forecasting Potential Diabetes Complications. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14), 2014, pages 313-319. [PDF] [Data&Code]

Yuxiao Dong, Yang Yang, Jie Tang, Yang Yang, and Nitesh V. Chawla. Inferring User Demographics and Social Strategies in Mobile Social Networks. In Proceedings of the Twentyth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'14), 2014, pages 15-24. [PDF] [Data&Code]

• See also the United Nations Global Pulse post about this work.

Yang Yang, Jianfei Wang, Yutao Zhang, Wei Chen, Jing Zhang, Honglei Zhuang, Zhilin Yang, Bo Ma, Zhanpeng Fang, Sen Wu, Xiaoxiao Li, Debing Liu, and Jie Tang. SAE: Social Analytic Engine for Dynamic Networks. In Proceedings of the Ninteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'13, demo paper), 2013, pages 1502-1505. [PDF] [Poster]

Yang Yang, Jie Tang, Jacklyne Keomany, Yanting Zhao, Ying Ding, Juanzi Li, and Liangwei Wang. Mining Competitive Relationships by Learning across Heterogeneous Networks. In Proceedings of the Twenty-First Conference on Information and Knowledge Management (CIKM'12), 2012, pages 1432-1441. [PDF]

Jie Tang, Bo Wang, Yang Yang, Po Hu, Yanting Zhao, Xinyu Yan, Bo Gao, Minlie Huang, Peng Xu, Weichang Li, and Adam K. Usadi. PatentMiner: Topic-driven Patent Analysis and Mining. In Proceedings of the Eighteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'12), 2012, pages 1366-1374. [PDF] [Slides] [Poster] [System] [Video]

Tieyun Qian, Yang Yang, Shuo Wang. Refining Graph Partitioning for Social Network Clustering. In Proceeding of the Eleventh International Conference on Web Information System Engineering (WISE'10), 2010, pages 77-90. [PDF]

Journal Editors:

Associate Editor of IEEE Transactions on Big Data (TBD)

Conference Organization:

Sponsor co-chair of WWW 2021

Program chair of SMP 2020

Tutorial and data mining forum at SMP 2018

BigNet Wokshop at CIKM 2016

KDD Forum at SMP 2016

Conference PC Members:

KDD 2022, KDD 2021, KDD 2020, WSDM 2020, WSDM 2019 (awarded as Outstanding PC), KDD 2019, AAAI 2019, KDD 2018, WWW 2017, WSDM 2017, CIKM 2017, ICWSM 2017, WSDM 2016, CIKM 2016, ASONAM 2015.

2023, awarded AAAI Distinguished Paper

2016, awarded Outstanding Doctoral Thesis of Chinese Institute of Electronics

2016, awarded Excellent Graduate of Tsinghua University with Ph.D. degree

2016, awarded Excellent Graduate of Beijing City with Ph.D. degree

2014-2015, awarded National Scholarships

2013, awarded Alumnus Scholarship of Tsinghua University

2008-2009, ACM International Collegiate Programming Contest, Asia Regional, 5 gold medals

When not doing research, I enjoy playing Magic The Gathering (a world-wide trading card game). I won a Pro Tour Qualifier Tournament in 2014.

Ph.D. students

Xuanwen Huang (5th year)

Yifei Sun (5th year)

Junru Chen (4th year)

Zhengzhe Fu (4th year)

Fanzhe Fu (4th year)

Shihao Tu (4th year)

Taoran Fang (3rd year)

Ziwei Chai (3rd year)

Juren Li (3rd year)

Chenglu Pan (3rd year, co-advising)

Dezheng Bao (1st year)

Lekui Zhou (graduated in 2020, work @ Huawei)

Jiarong Xu (graudated in 2021, assistant professor @ Fudan University)

Master students

Zhendong Fu (3rd year)

Zhisheng Zhang (3rd year)

Yiqin Zhang (3rd year)

Huijuan Wang (3rd year)

Quanjin Tao (3rd year)

Hanyang Yuan (3rd year)

Renhong Huang (2nd year)

Yuxuan Cao (2nd year)

Haoran Deng (2nd year)

Xuhang Zhu (2nd year)

Youmin Chen (2nd year)

Boning Zhang (2nd year)

Yupeng Zhang (2nd year)

Zhizhang Yuan (1st year)

Daoze Zhang (1st year)

Hanchen Su (1st year)

Zhijian Bao (1st year)

Yize Zhu (1st year)

Yuhong Xu (graduated in 2019, work @ NetEase)

Wenjie Hu (graduated in 2020, work @ Alibaba)

Zongtao Liu (graduated in 2020, work @ Alibaba)

Wei Huang (graduated in 2020, work @ ChinaTelcom)

Chao Zhou (graduated in 2020, work @ NetEase)

Ziqiang Cheng (graduated in 2021, work @ ByteDance)

Ping Shao (graduated in 2021, work @ Alibaba)

Siqi You (graduated in 2021, work @ Meituan)

Tao Yu (graduated in 2021, work @ Hikvision)

Jintao Su (graduated in 2022, work @ Tencent)

Xuan Yang (graduated in 2023, PhD @ Duke University)

Zhexuan Lu (graduated in 2023, work @ Uranus Research)

Teng Ke (graduated in 2023, work @ Alibaba)

Donghong Cai (graduated in 2023, work @ Alibaba)

Jiali Xie (graduated in 2023, work @ Lingxi Games)

Wei Dong (graduated in 2023, work @ Nanhu Academy)

Yihao Shang (graduated in 2023, work @ Alibaba)

Undergraduate students

I list ones working with me at least 6 months. Please let me know if I miss anyone.

Jiahe Li

Xiao Feng

Jiayu Liu

Kaiqiao Han

Shuning Shang

Xiaokang Shen

Tianjie Zhang

Xinyi Zheng

Wei Zhou

Rui Feng (graduated in 2019, PhD @ Georgia Tech)

Tianqing Fang (graduated in 2019, PhD @ HKUST)

Zhanlin Sun (graduated in 2019, master @ CMU)

Jiani Yang (graduated in 2021, PhD @ ZJU)

Yingying Fan (graduated in 2021, PhD @ ETH)

Yunchao Zhang (graduated in 2023, PhD @ CUHK)

Zhouxiang Fang (graduated in 2023, master @ Johns Hopkins University)

Ruining He (graduated in 2023, master @ University of Chicago)

Zeyu Lai (graduated in 2023, PhD @ ZJU)

Teng Liu (graduated in 2023, master @ ETH)