Yong Li (李勇)  

Tenured Associate Professor, Dept. of Electronic Engineering, Tsinghua University

 [Google Scholar]  [Curriculum Vitae] [LinkedIn] 

Future Intelligence Lab (FIB)

+86-10-62772387-201
liyong07@tsinghua.edu.cn
Rohm Building 10-202, Tsinghua University, Beijing, 100084, China.

Dr. Yong Li (M'12-SM'16) received the B.S. degree from Huazhong University of Science and Technology in 2007, and the M. S. and the Ph. D. degrees in Electrical Engineering from Tsinghua University, in 2009 and 2012, respectively. During 2012 and 2013, he was a Visiting Research Associate with Telekom Innovation Laboratories and Hong Kong University of Science and Technology respectively. During 2013 to 2014, he was a Visiting Scientist with the University of Miami. Currently, he is a Faculty Member of the Department of Electronic Engineering, Tsinghua University. His research interests are in the areas of wireless networking, mobile computing, big data and urban computing. Dr. Li has served as General Chair, TPC Chair, TPC Member for several international workshops and conferences, and he is on the editorial board of four international journals. His papers have total citations more than 17500 (Google Scholar).

Our group has several open research positions for Postdoctoral Fellowships [doc, English link, Chinese link].

Following fellowships are avaiable for postdoc applicants: Tsinghua Fellowship, Innovation Fellow, Exchange Program, etc.

We are also looking talented BS/MS/Ph. D. students, and visiting scholars who are interested in working in our lab.

News

  • 7 papers in the area of human behavior modeling, urban computing, and recommendations are accepted in WWW 2023.
  • Our work about using mobility data and epidemic model demonstrates how to balance multiple ethical values in complex settings for epidemic control is published in Nature Human Behaviour.
  • 9 KDD and 3 WWW papers in the area of behavior mining, urban science and recommendations are accepted in 2022.
  • Our work connecting the empirical findings in urban growth patterns and human mobility behavior is published in Nature Computational Science.
  • 3 KDD and 5 WWW papers in the area of urban science, knowledge graph and recommendations are accepted in 2021.
  • We have 4 papers of human behavior prediction and pandemic modelling & prevention to be presented in ACM KDD 2020, and will co-orgnize one workshop of "Prescriptive Analytics for the Physical World" and one tutorial of "Advances in Recommender Systems: From Multi-stakeholder Marketplaces to Automated RecSys" .
  • 3 papers about AutoML for Recsys, Generalized Knowledge Graph, and App Usage Modelling are accepted by WWW 2020 .
  • Our UbiComp Paper "Your Apps Give You Away: Distinguishing Smartphone Users by Their App Usage Behaviors" Won Best Paper (IMWUT Distinguished Paper Award).
  • 2 papers about reasoning the success of social e-commence are accepted by CSCW 2019 and ICWSM 2020 respectively.
  • We have 6 papers to be presented in ACM UbiComp 2019 , and will co-orgnize one workshop of "mining and learning from smartphone apps for users" and one tutorial of "smartphone app usage understanding, modelling, and prediction"
  • A larger-scale of context-aware app usage dataset was open [Details are here].
  • 2 papers are accepted for ORAL presentation at the KDD 2019 research track, which are about sparse sequence modeling and learning to regularize (accept rate: 110/1200=9.2%).
  • 4 papers are accepted by WWW 2019 about mobile data mining, data privacy and cross-domain recommendation.
  • We have 5 papers to be presented in ACM UbiComp 2018 , Con.
  • Dr. Li received Outstanding Youth Scientist of Wu Wenjun Artificial Intelligence.
  • Dr. Li received 2016-2018 Young Talent Program of China Association for Science and Technology.
  • Dr. Li received the IEEE 2016 ComSoc Asia-Pacific Outstanding Young Researchers [Award Information].
  • "Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data" was accepted by WWW 2017. Con. to Fengli and Zhen [Project HomePage].
  • "Context-aware Real-time Population Estimation for Metropolis" was accepted by UbiComp 2016, which won Honorable Mention Award. Con. to Fengli and Jie [Project HomePage].

Publications

Please check HERE for the latest update.
  • [NHB] L. Chen, F. Xu, Z. Han, K. Tang, P. Hui, J. Evann, and Yong Li. Strategic COVID-19 Vaccine Distribution can Simultaneously Elevate Social Utility and Equity, Nature Human Behaviour, 6, 1503–1514 (2022).
  • [NCS] Y. Zhang, F. Xu, Yong Li, D. Jin, J. Lu, and C. Song. Emergence of Urban Growth Patterns from Human Mobility Behavior, Nature Computational Science, 1, 791–800 (2021).
  • [ICLR] H. Shi, J. Ding, Y. Cao, Q. Yao, L. Liu, and Yong Li. Learning Symbolic Models for Graph-structured Physical Mechanism, in ICLR 2023.
  • [WWW] Y. Liu, X. Zhang, J. Ding, Y. Xi, and Yong Li. Knowledge-infused Contrastive Learning for Urban Imagery-based Socioeconomic Prediction, in The WebConf 2023 (WWW).
  • [WWW] H. Shi, Q. Yao, and Yong Li. Learning to Simulate Crowd Trajectories with Graph Networks, in The WebConf 2023 (WWW).
  • [WWW] Y. Yuan, H. Wang, J. Ding, D. Jin, and Yong Li. Learning to Simulate Daily Activities via Modeling Dynamic Human Needs, in The WebConf 2023 (WWW).
  • [WWW] G. Zhang, T. Ye, J. Yuan, D. Jin, and Yong Li. A Multi-scale Co-evolving Model for Dynamic Link Prediction in Evolving Networks, in The WebConf 2023 (WWW).
  • [WWW] G. Lin, C. Gao, Y. Zheng, J. Chang, Y. Niu, Y. Song, Z. Li, D. Jin, and Yong Li. Dual-interest Factorization-heads Attention for Sequential Recommendation, in The WebConf 2023 (WWW).
  • [WWW] Y. Quan, J. Ding, C. Gao, L. Yi, D. Jin, and Yong Li. Robust Preference-Guided Denoising for Graph based Social Recommendation, in The WebConf 2023 (WWW).
  • [WWW] Z. Zhou, Y. Liu, J. Ding, D. Jin, and Yong Li. Hierarchical Knowledge Graph Learning Enabled Socioeconomic Indicator Prediction in Location-Based Social Network, in The WebConf 2023 (WWW).
  • [AAAI] H. Wang, C. Gao, Y. Wu, D. Jin, L. Yao, and Yong Li. PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learning, in AAAI 2023.
  • [KDD] Y. Yuan, J. Ding, H. Wang, D. Jin, and Yong Li. Activity Trajectory Generation via Modeling Spatiotemporal Dynamics, in ACM KDD 2022.
  • [KDD] Q. Hao, W. Huang, F. Xu, K. Tang, and Yong Li. Reinforcement Learning Enhances the Experts: Large-scale COVID-19 Vaccine Allocation with Multi-factor Contact Network, in ACM KDD 2022.
  • [KDD] Y. Li, C. Gao, X. Du, H. Wei, H. Luo, D. Jin, and Yong Li. Automatically Discovering User Consumption Intents in Meituan, in ACM KDD 2022.
  • [KDD] C. Liu, C. Gao, Y. Yuan, C. Bai, L. Luo, X. Du, X. Shi, H. Luo, D. Jin, and Yong Li. Modeling the Effect of Persuasion Factor on User Decision for Recommendation, in ACM KDD 2022.
  • [KDD] M. Wang, H. Yan, H. Sui, F. Zuo, Y. Liu, and Yong Li. Learning to Discover Causes of Traffic Congestion with Limited Labeled Data, in ACM KDD 2022.
  • [KDD] F. Yu, W. Ao, H. Yan, G. Zhang, W. Wu, and Yong Li. Spatio-Temporal Vehicle Trajectory Recovery on Road Network Based on Traffic Camera Video Data, in ACM KDD 2022.
  • [KDD] Z. Zong, Ha. Wang, J. Wang, M. Zheng, and Yong Li. RBG: Hierarchically Solving Large-Scale Routing Problems in Logistic Systems via Reinforcement Learning, in ACM KDD 2022.
  • [KDD] T. Feng, T. Xia, X. Fan, H. Wang, Z. Zong, and Yong Li. Precise Mobility Intervention for Epidemic Control Using Unobservable Information via Deep Reinforcement Learning, in ACM KDD 2022.
  • [KDD] G. Zhang, Z. Yu, D. Jin, and Yong Li. Physics-infused Machine Learning for Crowd Simulation, in ACM KDD 2022.
  • [CHI] Z. Chen, H. Cao, X. Lan, Z. Lu, and Yong Li. Beyond Virtual Bazaar: How Social Commerce Promotes Inclusivity for the Traditionally Underserved Community in Chinese Developing Regions, in ACM CHI 2022.
  • [WWW] Y. Zheng, C. Gao, J. Chang, Y. Niu, Y. Song, D. Jin, and Yong Li. Disentangling Long and Short-Term Interests for Recommendation, in The WebConf 2022 (WWW).
  • [WWW] S. Hui, Hu. Wang, Z. Wang, and X. Yang, Z. Liu, D. Jin, and Yong Li. Knowledge Enhanced GAN for IoT Traffic Generation, in The WebConf 2022 (WWW).
  • [WWW] Y. Xi, T. Li, H. Wang, Yong Li, S. Tarkoma, and P. Hui. Beyond the First Law of Geography: Learning Representations ofSatellite Imagery by Leveraging Point-of-Interests, in The WebConf 2022 (WWW).
  • [SIGIR] J. Shuai, K. Zhang, L. Wu, P. Sun, R. Hong, M. Wang, and Yong Li. A Review-aware Graph Contrastive Learning Framework for Recommendation, in ACM SIGIR 2022.
  • [SIGIR] Y. Li, C. Gao, H. Luo, De. Jin, and Yong Li. Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation, in ACM SIGIR 2022.
  • [SIGIR] G. Lin, C. Gao, Y. Li, Y. Zheng, Z. Li, D. Jin, and Yong Li. Dual Contrastive Network for Sequential Recommendation, in ACM SIGIR 2022.
  • [UbiComp] Y. Zhang, T. Xia, F. Xu, and Yong Li. Quantifying the Causal Effect of Individual Mobility on Health Status in Urban Space, in ACM UbiComp 2022 (IMWUT).
  • [UbiComp] Q. Yu, H. Wang, Y. Liu, D. Jin, and Yong Li. Spatio-Temporal Knowledge Graph Enabled Mobility Prediction, in ACM UbiComp 2022 (IMWUT).
  • [WSDM] G. Zhang, J. Zeng, Z. Zhao, D. Jin, and Yong Li. A Counterfactual Modeling Framework for Churn Prediction, in WSDM 2022.
  • [ICDE] Y. Li, C. Gao, Q. Yao, T. Li, D. Jin, and Yong Li. DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction, in ICDE 2022.
  • [ICDE] Z. Zhu, C. Gao, X. Chen, N. Li, D. Jin, and Yong Li. Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks, in ICDE 2022.
  • [SIGSPATIAL] Z. Lin, G. Zhang, Z. He, J. Feng, W. Wu, and Yong Li. Vehicle Trajectory Recovery on Road Network via Traffic Camera Video Data, ACM SIGSPATIAL 2021.
  • [SIGSPATIAL] G. Jin, H. Yan, F. Li, Yong Li, J. Huang. Hierarchical Neural Architecture Search for Travel Time Estimation, ACM SIGSPATIAL 2021.
  • [NeurIPS] C. Gao, Y. Li, Q. Yao, D. Jin, Yong Li. Progressive Feature Interaction Search for Deep Sparse Network, in NeurIPS 2021.
  • [NeurIPS] F. Xu, Q. Yao, P. Hui, Yong Li. Automorphic Equivalence-aware Graph Neural Network, in NeurIPS 2021.
  • [KDD] C. Gao, Q. Yao, D. Jin, and Yong Li. Efficient Data-specific Model Search for Collaborative Filtering, in KDD 2021.
  • [KDD] Q. Hao, F. Xu, L. Chen, P. Hui, and Yong Li. Hierarchical Reinforcement Learning for Scarce Medical Resource Allocation with Imperfect Information, in KDD 2021.
  • [KDD] Y. Ping, C. Gao, T. Liu, X. Du, H. Luo, D. Jin, and Yong Li. User Consumption Intention Prediction in Meituan, in KDD 2021.
  • [SIGIR] J. Chang, C. Gao, Y. Zhen, Y. Hui, Y. Niu, Y. Song, D. Jin, and Yong Li. Sequential Recommendation with Graph Convolutional Networks, in SIGIR 2021.
  • [ICLR] S. Liu, C. Gao, Y. Chen, D. Jin, and Yong Li. Learning Embedding Sizes for Recommender Systems, in ICLR 2021.
  • [CHI] Z. Chen, H. Cao, Y. Deng, X. Gao, J. Piao, F. Xu, Y. Zhang, and Yong Li. Learning from Home: A Mixed-Methods Analysis of Live Streaming Based Remote Education Experience in Chinese Colleges during the COVID-19 Pandemic, in CHI 2021.
  • [UbiComp] Z. Han, H. Fu, F. Xu, Z. Tu, Y. Yu, P. Hui, and Yong Li. Who Will Survive and Revive Undergoing the Epidemic: Analyses about POI Visit Behavior in Wuhan via Check-in Records, in ACM UbiComp 2021 (IMWUT).
  • [WWW] Y. Liu, Q. Yao, and Yong Li. Role-Aware Modeling for N-ary Relational Knowledge Bases, in WWW 2021.
  • [WWW] G. Zhang, Yong Li, Y. Yuan, F. Xu, H. Cao, D. Jin and Y. Xu. Community Value Prediction in Social E-commerce, in WWW 2021.
  • [WWW] J. Piao. G. Zhang, F. Xu, Z. Chen, and Yong Li. Predicting Customer Value with Social Relationships via Motif-based Graph Attention Networks, in WWW 2021.
  • [WWW] Y. Zheng, C. Gao, X. Li, X. He, Yong Li, and D. Jin, Disentangling User Interest and Conformity for Recommendation with Causal Embedding, in WWW 2021.
  • [WWW] Y. Zheng, C. Gao, L. Chen, Yong Li, and D. Jin, Diversified Recommendation Through Similarity-Guided Graph Neural Networks, in WWW 2021.
  • [AAAI] T. Xia, J. Feng, Y. Qi, F. Xu, Yong Li, D. Guo, F. Sun. AttnMove: History Enhanced Trajectory Recovery via Attentional Network, in AAAI 2021.
  • [UbiComp] Y. Zhang, F. Xu, T. Li, V. Kostakos, P. Hui, and Yong Li. Passive Health Monitoring using Large Scale Mobility Data, in ACM UbiComp 2021 (IMWUT).
  • [CSCW] H. Cao, Z. Chen, M. Cheng, S. Zhao, T. Wang, and Yong Li. You Recommend, I Buy: How and Why People Engage in Instant Messaging Based Social Commerce, in CSCW 2021.
  • [UbiComp] Z. Lin, S. Lyu, H. Cao, F. Xu, Y. Wei, P. Hui, H. Samet, and Yong Li. HealthWalks: Sensing Fine-grained Individual Health Condition via Mobility Data, in ACM UbiComp 2021 (IMWUT).
  • [ICDE] J. ZHang, C. Gao, D. Jin, Yong Li. Group-Buying Recommendation for Social E-Commerce, in ICDE 2021.
  • [ICWSM] F. Xu, G. Zhang, Y. Yuan, H. Huang, D. Yang, D. Jin, Yong Li. Would You Like to Join Us: Understanding the Invitation Acceptance in Agent-initiated Social E-commerce, in ICWSM 2021.
  • [CSCW] Z. Chen, H. Cao, F. Xu, M. Cheng, T Wang, and Yong Li. Understanding the Role of Intermediaries in Online Social E-commerces: an Exploratory Study of Beidian, in CSCW 2020.
  • [NeurIPS] J. Ding, Y. Quan, Q. Yao, Yong Li, D. Jin. Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering, in NeurIPS 2020.
  • [KDD] F. Xu, Yong Li, and S. Xu. Attentional Multi-graph Convolutional Network for Regional Economy Prediction with Open Migration Data, in KDD 2020 (research track).
  • [KDD] J. Feng, Z. Yang, F. Xu, H. Yu, M. Wang and Yong Li. Learning to Simulate Human Mobility, in KDD 2020 (ai for covid).
  • [KDD] Q. Hao, L. Chen, F. Xu and Yong Li. Understanding the Urban Pandemic Spreading of COVID-19 with Real World Mobility Data, in KDD 2020 (ai for covid).
  • [KDD] S. Song, Z. Zong, and Yong Li, X. Liu and Y. Yu. Reinforced Epidemic Control: Saving Both Lives and Economy, in KDD 2020 (ai for covid).
  • [SIGIR] C. Gao, C. Huang, D. Lin, Yong Li, and D. Jin. DPLCF: Differentially Private Local Collaborative Filtering, in SIGIR 2020 (long paper).
  • [SIGIR] B. Jin, C. Gao, X. He, Yong Li, and D. Jin. Multi-behavior Recommendation with Graph Convolution Networks, in SIGIR 2020 (long paper).
  • [SIGIR] J. Chang, C. Gao, X. He, Yong Li, and D. Jin. Bundle Recommendation with Graph Convolutional Networks, in SIGIR 2020 (short paper).
  • [UbiComp] Y. Yu, T. Xia, H. Wang, J. Feng, and Yong Li. Semantic-aware Spatio-temporal App Usage Representation via Graph Convolutional Network, in ACM UbiComp 2020 (IMWUT).
  • [UbiComp] F. Xu, Z. Lin, D. Guo, and Yong Li. SUME: Semantic-enhanced Urban Mobility Network Embedding for User Demographic Inference, in ACM UbiComp 2020 (IMWUT).
  • [UbiComp] Z. Chen, H. Cao, H. Wang, F. Xu, V. Kostakos, and Yong Li. Will You Come Back / Check-in Again? Understanding Characteristics Leading to Urban Revisitation and Re-check-in, in ACM UbiComp 2020 (IMWUT).
  • [IJCAI] J. Feng, Z. Lin, T. Xia, F. Sun, D. Guo, and Yong Li. A Sequential Convolution Network for Population Flow Prediction with Explicitly Correlation Modelling, in IJCAI 2020.
  • [IJCAI] M. Zhang, T. Li, Yong Li, and P. Hui. Multi-View Joint Graph Representation Learning for Urban Region Embedding, in IJCAI 2020.
  • [UbiComp] D. Wu, T. Xiao, X. Liao, J. Luo, C. Wu, and Yong Li. When Sharing Economy Meets IoT: Towards Fine-grained Urban Air Quality Monitoring through Mobile Crowdsensing on Bike-share System, in ACM UbiComp 2020 (IMWUT).
  • [WWW] Y. Liu, Q. Yao and Yong Li. Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases, in WWW 2020, long paper.
  • [WWW] T. Li, M. Zhang, H. Cao, Yong Li, S. Tarkoma and P. Hui. ”What Apps Did You Use?“: Understanding the Long-term Evolution of Mobile App Usage, in WWW 2020, long paper.
  • [WWW] Q. Yao, X. Chen, J. Kwok, Yong Li and C. Hsieh. Efficient One-shot Interaction Functions Search for Collaborative Filtering, in WWW 2020, long paper.
  • [UbiComp] J. Feng, C. Rong, F. Sun, D. Guo, and Yong Li. PMF: A Privacy-preserving Human Mobility Prediction Framework via Federated Learning, in ACM UbiComp 2020 (IMWUT).
  • [ICDE] Y. Zheng, C. Gao, X. He, Yong Li, D. Jin. Incorporating Price into Recommender System, in ICDE 2020 (long paper).
  • [ICDE] H. Shi, Q. Yao, Q. Guo, Y. Li, L. Zhang, J. Ye，Yong Li, Y. Liu. Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network, in ICDE 2020 (short paper).
  • [UbiComp] J. Ding, G. Yu, Yong Li, H. Gao, D. Jin. Learning from Hometown and Current City: POI Recommendation for Cross-city Travelers via Leveraging User Interest Drift and Transfer, in ACM UbiComp 2020 (IMWUT).
  • [UbiComp] Y. Fan, Z. Tu, Yong Li, X. Chen, L. Zhang, L. Su, D. Jin. Personalized Context-aware Collaborative Online Activity Prediction, in ACM UbiComp 2020 (IMWUT).
  • [ICWSM] H. Cao, Z. Chen, F. Xu, T. Wang, Yong Li. When E-Commerce Meets Intimacy: An Empirical Study of Social Commerce Site Beidian, in ICWSM 2020.
  • [CSCW] F. Xu, Z. Han, J. Piao, Yong Li. "I Think You’ll Like It": Modelling the Online Purchase Behavior in Social E-commerce, in CSCW 2019.
  • [KDD] H. Shi, C. Zhang, Q. Yao, Yong Li, F. Sun, D. Jin. State-Sharing Sparse Hidden Markov Models for Personalized Sequences, in KDD 2019, research track, oral paper.
  • [KDD] Y. Chen, B. Chen, X. He, C. Gao, Yong Li, J. Lou, Y. Wang. λOpt: Learn to Regularize Recommender Models in Finer Levels, in KDD 2019, research track, oral paper.
  • [SIGIR] T. Lin, C. Gao Yong Li. CROSS: Cross-platform Recommendation for Online Shopping in Social Media, in SIGIR 2019, long paper.
  • [WWW] J. Feng, M. Zhang, H. Wang, Z. Yang, C. Zhang, Yong Li, D. Jin. DPLink: User Identity Linkage via Deep Neural Network From Heterogeneous Mobility Data, in WWW 2019, long paper.
  • [WWW] C. Gao, X. Chen, F. Feng, K. Zhao, X. He, Yong Li, D. Jin. Cross-domain Recommendation Without Sharing User-relevant Data, in WWW 2019, long paper.
  • [WWW] T. Xia, Y. Yu, F. Xu, Yong Li. Understanding Urban Dynamics via State-sharing Hidden Markov Model, in WWW 2019, short paper.
  • [WWW] F. Xu, Z. Tu, H. Huang, S. Chang, Yong Li. No More than What I Post: Preventing Linkage Attacks on Check-in Services, in WWW 2019, short paper.
  • [IJCAI] J. Ding, Y. Quan, X. He, Yong Li, D. Jin. Reinforced Negative Sampling for Recommendation with Exposure Data, in IJCAI 2019.
  • [IJCAI] H. Yan, X. Chen, C. Gao, Yong Li, D. Jin. DeepAPF: Deep Attentive Probabilistic Factorization for Multi-site Video Recommendation, in IJCAI 2019.
  • [IJCAI] M. Zhang, T. Li, H. Shi, Yong Li, H. Pan. A Decomposition Approach for Urban Anomaly Detection Across Spatiotemporal Data, in IJCAI 2019.
  • [UbiComp] T. Xia, Yong Li. Revealing Urban Dynamics by Learning Online and Offline Behaviours Together, in ACM UbiComp 2019 (IMWUT).
  • [UbiComp] C. Gao, C. Huang, Y. Yu, Yong Li, D. Jin. Privacy-preserving Cross-domain Location Recommendation, in ACM UbiComp 2019 (IMWUT).
  • [UbiComp] Z. Tu, Y. Fan, Yong Li, X. Chen, L. Su, D. Jin. From Fingerprint to Footprint: Cold-start Location Recommendation by Learning User Interest from App Data, in ACM UbiComp 2019 (IMWUT).
  • [UbiComp] H. Wang, Yong Li, S. Zeng, G. Wang, P. Zhang, P. Hui, D. Jin. Modeling Spatio-Temporal App Usage for a Large User Population, in ACM UbiComp 2019 (IMWUT).
  • [UbiComp] X. Chen, Y. Wang, S. Pan, Yong Li, P. Zhang. CAP: Context-aware App-usage Prediction, in ACM UbiComp 2019 (IMWUT).
  • [MobiSys] Y. Yan, Z. Li, A. Chen, C. Wilson, T. Xu, E. Zhai, Yong Li, Y. Liu. Understanding and Detecting Overlay-based Android Malware at Market Scales, in MobiSys 2019.
  • [AAAI] Z. Lin, J. Feng, Z. Lu, Yong Li, F. Sun, A. Guo, D. Jin. DeepSTN+: Context-aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis, in AAAI 2019.
  • [AAAI] Z. Zong, J. Feng, K. Liu, H. Shi, Yong Li, F. Sun, A. Guo, D. Jin. DeepDPM: Dynamic Population Mapping via Deep Neural Network, in AAAI 2019.
  • [UbiComp] H. Cao, Z. Chen, Yong Li, V. Kostakos. Revisitation in Urban Space vs. Online: a Comparison Across POIs, Websites, and Smartphone Apps, in ACM UbiComp 2019 (IMWUT).
  • [ICDE] C. Gao, X. He, D. Gan, X. Chen, F. Feng, Yong Li, T. Chua, D. Jin. Learning Recommender Systems from Multi-Behavior Data, in ICDE 2019.
  • [CSCW] F. Xu, G. Zhang, J. Huang, Yong Li, D. Yang, Y. Zhao, F. Meng. Understanding Motivations behind Inaccurate Check-ins, in CSCW 2018.
  • [IJCAI] J. Ding, G. Yu, X. He, Y. Quan, Yong Li, T. Chua, D. Jin. Improving Implicit Recommender Systems with View Data, in IJCAI 2018.
  • [UbiComp] H. Cao, J. Feng, Yong Li, V. Kostakos. Uniqueness in the City: Urban Morphology and Location Privacy, in ACM UbiComp 2018 (IMWUT).
  • [UbiComp] Z. Tu, R. Li, Yong Li, G. Wang, L. Su, D. Jin. Your Apps Give You Away: Distinguishing Smartphone Users by Their App Usage Behaviors, in ACM UbiComp 2018 (IMWUT), Distinguished Paper Award.
  • [WWW] J. Feng, Yong Li, C. Zhang, F. Sun, F. Meng, A. Guo, D. Jin. DeepMove: Predicting Human Mobility with Attentional Recurrent Networks, in WWW 2018.
  • [WWW] Ding, F. Feng, X. He, G. Yu, Yong Li, D. Jin. An Improved Sampler for Bayesian Personalized Ranking by Leveraging View Data, in WWW 2018 Poster, Best Poster Paper Award.
  • [UbiComp] F. Xu, Yong Li, T. Xia, H. Liu, Y, Li, F. Sun, F. Meng. Detecting Popular Temporal Modes in Population-scale Unlabelled Mobility Data, in ACM UbiComp 2018 (IMWUT).
  • [NDSS] H. Wang, C. Gao Yong Li, G. Wang, D. Jin, J. Sun. De-anonymization of Mobility Trajectories: Dissecting the Gaps between Theory and Practice, in NDSS 2018.
  • [UbiComp] D. Y, Yong Li, F. Xu, P. Zhang, V. Kostakos. Smartphone App Usage Prediction Using Points of Interest, in ACM UbiComp 2018 (IMWUT).
  • [UbiComp] X. Huang, Yong Li, Y. Wang, Y. Xiao, L. Zhang. CTS: A Cellular-based Trajectory Tracking System with GPS-level Accuracy, in ACM UbiComp 2018 (IMWUT).
  • [UbiComp] D. Wu, D. Arkhipov, T. Przepiorka, Yong Li, Q. Liu. No More Waiting: Enhancing Mobile Access to Online Social Networks with Opportunistic Optimization, in ACM UbiComp 2017 (IMWUT).
  • [CIKM] H. Wang, C. Gao, Yong Li, Z. Zhang, D. Jin, From Fingerprint to Footprint: Revealing Physical World Privacy Leakage by Cyberspace Cookie Logs , in ACM CIKM 2017.
  • [CIKM] H. Yan, Z. Lin, G. Wang Yong Li, H. Zheng, B. Zhao, D. Jin. , On Migratory Behavior in Video Consumption, in ACM CIKM 2017.
  • [SIGIR] C. Yang, H. Yan, D. Yu, Yong Li, D. Chiu, Multi-site User Behavior Modeling and Its Application in Video Recommendation, in ACM SIGIR 2017.
  • [WWW] F. Xu, Z. Tu, Yong Li, P. Zhang, X. Fu, D. Jin. Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data, in WWW 2017.
  • [ICWSM] H. Yan, Z. Lin, G. Wang Yong Li, H. Zheng, B. Zhao, D. Jin. On Migratory Behavior in Video Consumption, in ICWSM 2017.
  • [ICDCS] H. Flores, X. Su, V. Kostakos, J. Riekki, E. Lagerspetz, S. Tarkoma, P. Hui, Yong Li, J. Manner, Modeling Mobile Code Acceleration in the Cloud, in IEEE ICDCS 2017.
  • [SECON] Z. Tu, K. Zhao, Yong Li, F. Xu, L. Su, D. Jin. Beyond k-anonymity: Protect Your Trajectory From Semantic Attack, in IEEE SECON 2017.
  • [INFOCOM] L. Chen, Yong Li, et al. Oblivious Neighbor Discovery for Wireless Devices with Directional Antennas, IEEE Infocom 2016.
  • [UbiComp] F. Xu, J. Feng, P. Zhang Yong Li. Context-aware Real-time Population Estimation for Metropolis, in ACM UbiComp 2016, Honorable Mention Award
  • [ASONAM] H. Wang, Yong Li, Y. Chen, D. Jin. Co-Location Social Networks: Linking the Physical World and Cyberspace, IEEE/ACM ASONAM 2016.
  • [IMC] H. Wang, F. Xu, Yong Li, P. Zhang, D. Jin. Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment, to appear in ACM IMC 2015.
  • [INFOCOM] H. Wang, Yong Li, Y. Zhang, D. Jin. Virtual Machine Migration Planning in Software-Defined Networks, IEEE Infocom 2015.
  • [TOIS] J. Ding, Yong Li, G. Yu, X. He, and D. Jin. Improving Implicit Recommender Systems with Auxiliary Data, ACM Transactions on Transactions on Information Systems, to appear.
  • [TKDE] J. Ding, G. Yu, X. He, Yong Li and D. Jin. Sampler Design for Bayesian Personalized Ranking by Leveraging View Data, IEEE Transactions on Knowledge and Data Engineering, to appear.
  • [TKDE] X. Tong, Yong Li, Y. Yu, F. Xu, Q. Liao, D. Jin. Understanding Urban Dynamics via State-sharing Hidden Markov Model, IEEE Transactions on Knowledge and Data Engineering, to appear.
  • [TKDE] C. Gao, X. He, Yong Li, D. Jin, et al. Learning to Recommend with Multiple Cascading Behaviors, IEEE Transactions on Knowledge and Data Engineering, to appear.
  • [TKDE] H. Yan, C. Yang, D. Yu, Yong Li D. Jin and D. Chiu. Multi-site User Behavior Modeling and Its Application in Video Recommendation, IEEE Transactions on Knowledge and Data Engineering, to appear.
  • [TKDE] H. Shi, Yong Li H. Cao, X. Zhou, C. Zhang, V. Kostakos. Semantics-Aware Hidden Markov Model for Human Mobility, IEEE Transactions on Knowledge and Data Engineering, to appear.
  • [TKDE] J. Ding, G. Yu, X. He, Yong Li D. Jin. Sampler Design for Bayesian Personalized Ranking by Leveraging View Data, IEEE Transactions on Knowledge and Data Engineering, to appear.
  • [JSAC] P. Chemouil, P. Hui, W. Kellerer, Yong Li, R. Stadler, D. Tao, Y. Wen, Y. Zhang. Artificial Intelligence and Machine Learning for Networking and Communications, IEEE Journal on Selected Areas in Communications, 37(6): 1185-1191 (2019).
  • [JSAC] X. Chen, Y. Zhao, Yong Li, N. Ge, X. Chen, S. CHen. Social Security Aided D2D Communications: Performance Bound and Implementation Mechanism, IEEE Journal on Selected Areas in Communications to appear.
  • [JSAC] Yong Li, D. Jin, P. Hui, H. Zhu. Optimal Base Station Scheduling for Device-to-Device Communication Underlaying Cellular Networks, IEEE Journal on Selected Areas in Communications 34(1): 27-40 (2016).
  • [JSAC] Y. Zhao, Yong Li, H. Zhang, N. Ge, J. Lu. Fundamental Tradeoffs on Energy-Aware D2D Communication Underlaying Cellular Networks: A Dynamic Graph Approach, IEEE Journal on Selected Areas in Communications 34(4): 864-882 (2016).
  • [JSAC] Y. Niu, C. Gao, Yong Li, D. Jin, L. Su, et al. Exploiting Device-to-Device Communications in Joint Scheduling of Access and Backhaul for mmWave Small Cells, IEEE Journal on Selected Areas in Communications 33(10): 2052-2069 (2015).
  • [TON] Z. Tu, F. Xu, Yong Li, P. Zhang, D. Jin. A New Privacy Breach: User Trajectory Recovery From Aggregated Mobility Data, IEEE/ACM Transactions on Networking.
  • [TON] F. Xu, Yong Li, H. Wang, P. Zhang, D. Jin. Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment, IEEE/ACM Transactions on Networking, to appear.
  • [TON] L. Chen, Yong Li, et al. On Oblivious Neighbor Discovery in Distributed Wireless Networks with Directional Antennas: Theoretical Foundation and Algorithm Design, IEEE/ACM Transactions on Networking, to appear.
  • [TON] D. Wu, Q. Liu, Yong Li, J. A. McCann, A. C. Regan, N. Venkatasubramanian. Adaptive Lookup of Open WiFi using Crowdsensing, IEEE/ACM Transactions on Networking, 2016.
  • [TMC] H. Wang, Yong Li, C. Gao, G. Wang, D. Jin. Anonymization and De-anonymization of Mobility Trajectories: Dissecting the Gaps between Theory and Practice, IEEE Transactions on Mobile Computing, to appear.
  • [TMC] Z. Tu, Yong Li, P. Hui, L. Su, D. Jin. Personalized Mobile App Prediction by Learning User's Interest from Social Media, IEEE Transactions on Mobile Computing, to appear.
  • [TMC] Y. Liu, Yong Li, Y. Niu, and D. Jin. Joint Optimization of Path Planning and Resource Allocation in Mobile Edge Computing, IEEE Transactions on Mobile Computing, to appear.
  • [TMC] D. Xu, Yong Li, Y. Li, T. Xia, J. Li, S. Tarkoma, P. Hui. Portfolio Optimization in Traffic Offloading: Concept, Model, and Algorithms, IEEE Transactions on Mobile Computing, to appear.
  • [TMC] F. Xu, Yong Li, Z. Tu, S. Chang and H. Huang. No More than What I Post: Preventing Linkage Attacks on Check-in Services, IEEE Transactions on Mobile Computing, to appear.
  • [TMC] H. Cao, F. Xu, J. Sankaranarayanan, Yong Li, H. Samet. Habit2vec: Trajectory Semantic Embedding for Living Pattern Recognition in Population, IEEE Transactions on Mobile Computing, to appear.
  • [TMC] H. Wang, Yong Li, G. Wang, D. Jin. Linking Multiple User Identities of Multiple Services From Massive Mobility Traces, IEEE Transactions on Mobile Computing, to appear.
  • [TMC] H. Shi, Yong Li. Discovering Periodic Patterns for Large Scale Mobile Traffic Data: Method and Applications, IEEE Transactions on Mobile Computing, to appear.
  • [TMC] H. Flores, P. Hui, P. Nurmi, E. Lagerspetz, S. Tarkoma, J. Manner, V. Kostakos, Yong Li, X. Su. Evidence-aware Mobile Computational Offloading, IEEE Transactions on Mobile Computing, to appear.
  • [TMC] J. Ding, R. Xu, Yong Li, H. Pan, D. Jin. Measurement-driven Modeling for Connection Density and Traffic Distribution in Large-scale Urban Mobile Networks, IEEE Transactions on Mobile Computing, to appear.
  • [TMC] C. Gao, Yong Li, S. Chen. A Two-Level Game Theory Approach for Joint Relay Selection and Resource Allocation in Network Coding Assisted D2D Communications, IEEE Transactions on Mobile Computing, to appear.
  • [TMC] Y. Zhao, Yong Li, N. Ge. Overlapping Coalition Formation Game for Resource Allocation in Network Coding Aided D2D Communications, IEEE Transactions on Mobile Computing, to appear.
  • [TMC] Yong Li, D. Jin, P. Hui, S. Chen. Contact-Aware Data Replication in Roadside Unit Aided Vehicular Delay Tolerant Networks, IEEE Transactions on Mobile Computing, 15(2): 306-321 (2016).
  • [TMC] Yong Li, D. Jin, Z. Wang, P. Hui, L. Zeng, S. Chen. Multiple Mobile Data Offloading Through Disruption Tolerant Networks, IEEE Transactions on Mobile Computing, Vol. 13, No. 7, June 2014.
  • [TMC] Yong Li, D. Jin, Z. Wang, P. Hui, S. Chen. A Markov Jump Process Model for Urban Vehicular Mobility: Modeling and Applications, IEEE Transactions on Mobile Computing, 13(9):1911-1926 (2014).
  • [TMC] Yong Li, P. Hui, D. Jin, L. Su, L. Zeng. Optimal Distributed Malware Defense in Mobile Networks with Heterogeneous Devices, IEEE Transactions on Mobile Computing, 13(2): 377-391 (2014).
  • [COMST] M. Waqas, Yong Li, M. Ahmed, J. Depeng, Z. Han. A Survey on Socially-Aware Device-to-Device Communications, IEEE Communications Surveys and Tutorials, IEEE Communications Surveys and Tutorials, to appear.
  • [COMST] M. Haus, W. Waqas. A. Ding, Yong Li, T. Sasu, J. Ott. Security and Privacy in Device-to-Device (D2D) Communication, IEEE Communications Surveys and Tutorials, to appear.
  • [COMST] D. Xu, Yong Li, X. Chen, J. Li, P. Hui, S. Chen, J. Crowcroft. A Survey of Opportunistic Offloading, IEEE Communications Surveys and Tutorials, to appear.
  • [COMST] M. Achakzai, Yong Li, W. Msheraz, D. Jin, Z. Han, J. Crowcroft. A Survey on Socially-Aware Device-to-Device Communications, IEEE Communications Surveys and Tutorials, to appear.
  • [TC] H. Wang, Yong Li, P. Hui, D. Jin, J. Wu. Saving Energy in Partially Deployed Software Defined Networks, IEEE Transactions on Computers 65(5): 1578-1592 (2016).

    • Professional Activities

    • IEEE Senior Member, ACM Member;
    • Associate Editor: ACM IMWUT (UBICOMP) (2016 - now);
    • Associate Editor: IEEE Transactions on Network Science and Engineering (2019 - now);
    • Associate Editor: IEEE Transactions on Network Service and Management (2017 - 2021);
    • Editor of ML Series, IEEE Journal on Selected Areas in Communications (2020 - now);
    • Guest Editor: IEEE JSAC Special Issue on Artificial Intelligence and Machine Learning for Networking;
    • Guest Editor: IEEE Communications Magazine Special Issue on Mobile Big Data for Urban Analytics;
    • Guest Editor: IEEE Access Special Issue on Socially Enabled Networking and Computing;
    • SPC: KDD 2023, IJCAI 2023, AAAI 2022, IJCAI 2022, AAAI 2021, IJCAI 2021, AAAI 2020, IJCAI 2020, IJCAI 2019;
    • PC: KDD 2023, WWW 2023, SIGIR 2013, KDD 2022, AAAI 2022, WWW 2022, SIGIR 2022, KDD 2021, AAAI 2021, WWW 2021, IJCAI 2021, SIGIR 2021, WWW2020, IJCAI 2019, AAAI 2019, KDD 2019, WWW 2019, SIGIR 2019, KDD 2018, SIGIR 2018, WWW 2018, ICWSM 2018;
    • TPC Chair/Co-Chair: ICC 2018 Symposum of Wireless Networks, IWCMC 2018, ChinaCom 2015, Simplex 2013 (WWW workshop);
    • TPC Member: INFOCOM 2018, WWW 2017, ICWSM 2017, INFOCOM 2017, PAM 2017, Netsys 2017, ICC 2017, ICWSM 2016, WWW 2016, WCNC 2016, ICC 2016, ICCCN 2015, GLOBECOM 2015-2014, CHANTS 2015, GSCIT 2014, EECSI 2014, IWCMC 2014, CSNT 2014, WSSW 2013, ExtermeCom 2012-2013, IEEE/IFIP EUC 2012;
    • Publicity Co-Chair: ACM CHANTS 2014;
    • Conference Paper Reviewer of INFOCOM 2010-2013, GLOBECOM 2013, IWCMC 2012, SECON 2012, ICC 2012, ANSS 2012, GLOBECOM 2011, MASS 2011, ICC 2011, MCS 2010 CCNC 2011, GLOBECOM 2010, ICC 2010, ICOIN 2010, VTC 2010 Spring, PIMRC 2009, APCC 2009;
    • Journal Paper Reviewer of IEEE/ACM Transactions on Networking, IEEE Transactions on Mobile Computing, IEEE Transactions on Wireless Communications, IEEE Communications Letter, Electronics Letters, Journal of Computer Engineering Research, International Journal of Communication Systems, International Journal of Automation and Computing, Ad Hoc Networks, European Transactions on Telecommunications.

    Projects

    Mobile Wireless Big Data Managing, Mining and Modeling (MBD-M3)

    By focusing on characterizing the mobile wireless traffic, web accessing, and information usage traces based on large-scale and long-time mobile big data, which is collected from the commercial mobile operator with 380 thousand base stations and 15 million users spanning over a year from 15 major cities of China, we qualitatively visualize and quantitatively characterize the spatio-temporal human behaviors in the physical-cyber system including mobility regularity, traffic consumption patterns, social friendship activity, online information and commodity consumption, etc. Based on these fundamental findings and credible models, we further investigate how to utilize these important insights on how to deal with the problems encountered with the current mobile networks including traffic congestion offloading, green communications, solidified architecture, etc. Specific techniques include Hadoop/spark programming, machine learning, big data mining and processing algorithms of clustering, classification, graphing, etc.

    Social-Aware Next Generation Mobile Networks (SAN-5G)

    Aiming to establish a new paradigm to solve the problem of universal resource allocations for next generation mobile networks (5G), this project investigates a social behavior aware optimization framework that couples the social layer of human behaviors with physical layer of wireless communications. First, we model the mobility pattern and social behavior for users through mining the big data, which contains the location information, traffic consumption, service access, etc. Then, we establish social behavior aware optimization theory framework for mobile cellular networks based on the profound understanding of the interplay between social behavior and universal resource allocations. Finally, for typical social service scenarios, we evaluate the system performances through quantitative analysis and quantitative gains, which demonstrates the effectiveness of social behavior aware for network optimization framework and corresponding mechanisms and methods. Specific techniques involving this project include networking system modeling, social big data analysis, optimization and game theory, etc.

    Software-Defined Next Generation Networks (SDN-5G)

    Software defined network (SDN), an innovative paradigm for future networks, advocates separating the control plane and data plane, and abstracting the control functions of the network into a logically centralized control plane. Aiming to establish a new paradigm for 5G mobile networks, software-defined 5G mobile networks extend the concept of SDN controller to take the control functions of the physical layer into consideration as well, not just those of the network layer. By abstracting the control functions of access and core networks jointly, a logically centralized programmable control plane achieves the fine-grained controlling and flexible programmability, which is capable of achieving the design goals of convergence of heterogeneous networks, fine-grained controllability, efficient programmability for network evolution, and network and service customizability. We investigate this project by both system and theoretical approach. Specific techniques include cloud computing system (OpenStack, Neutron), SDN components (Floodlight, ODL, OVS), mobile core network elements, practical network optimization and system prototype.

    Students

      Please check HERE for the latest update.
      Current Students: I am fortunate and enjoying working together with these exceptional students.
    • Yong Niu, mmWave and D2D Communications and Networking, Ph. D (co-advised with Prof. Depeng Jin)
    • Yujie Liu, Network Update for Software-Defined Networks and NFV, Ph. D (co-advised with Prof. Jian Yuan)
    • Jiaqiang Liu, Software-Defined Networks for Mobile and IoT, Ph. D (co-advised with Prof. Depeng Jin)
    • Wei Feng, mmWave Communications and Networks, Ph. D (co-advised with Prof. Depeng Jin)
    • Yulei Zhao, D2D Communications for 5G, Ph. D (co-advised with Prof. Ning Ge)
    • Huandong Wang, Software-Defined Networks and Mobile Big Data, Ph. D (co-advised with Prof. Depeng Jin)
    • Huan Yan, Software-Defined Networks and North Bound API, Ph. D (co-advised with Prof. Depeng Jin)
    • Fengli Xu, Mobile Big Data Mining and User Behavior Modelling, Ph. D.
    • Jingtao Ding, Mobile Data Traffic Analysis and Modelling, Ph. D (co-advised with Prof. Depeng Jin)
    • Jie Feng, Mobile Big Data, Ph. D.
    • Chen Gao, Mobile Data Analysis, Ph. D (co-advised with Prof. Depeng Jin)
    • Fan Xie, Software-Defined Networks for WAN, Master (co-advised with Prof. Depeng Jin)
    • Shaoran Xiao, Software-Defined Networks for Cloud, Master.
    • Hangyu Fan, Software-Defined Networks for Cloud, Master.
    • Chuhan Gao, D2D & mmWave Communications, Undergraduate (will graduate in 2016)
    • Tianyi Geng, Mobile Big Data Mining, Undergraduate (will graduate in 2016)
    • Ran Xu, Vehicular Networks and Mobile Cloud Computing, Undergraduate (will graduate in 2016)
    • Wenxin Wang, Software-Defined Networks, Undergraduate (will graduate in 2016)
    • Mingyang Zhang, Mobile Traffic Mining and Modelling, Undergraduate (will graduate in 2016)
    • Hongzhi Shi, Mobile Big Data Mining and Modeling, Undergraduate (will graduate in 2017)
    • Chenghao Liu, Vehicular Cloud Computing, Undergraduate (will graduate in 2017)
    • Xihui Liu, Mobile Data Traffic Mining and Modeling, Undergraduate (will graduate in 2017)
      • Former Students & Alumni: Thanks for their contributions and I really enjoy working with them.
    • Bo Cui, (graduated in 2015, join B4 group of Google Sydney.)
    • Haoming Zhang, Undergraduate student (graduated in 2015, join CMU for Master study)
    • Xueshi Hou, Undergraduate student (graduated in 2015, will join UCSD for PhD study)
    • Chuanmeizi Wang, Undergraduate student (graduated in 2015, join USC for PhD study)
    • Bentao Zhang, Undergraduate student (graduated in 2014, will join UCSD for PhD study)
    • Mengjiong Qian, Master student (graduated in 2013, join USC for PhD study )
    • Xinlei Chen, Master student (graduated in 2012, join CMU for PhD study)
    • Siyu Chen, Undergraduate student (graduated in 2013, join CMU for Master study)
    • Wenyu Ren, Undergraduate student (graduated in 2013, join UIUC for PhD study)
    • Jingwei Zhang, Undergraduate student (graduated in 2013, join Columbia University for PhD study)
    • Hang Qu, Undergraduate student (graduated in 2012, join Stanford for PhD study)
    • Xu Zhang, Undergraduate student (graduated in 2012, join Duck for PhD study)
    • Yichong Wang, Undergraduate student (graduated in 2012, join UIUC for PhD study)
    • Li Qiu, Undergraduate student (graduated in 2012, join PSU for PhD study)
    • Guolong Su, Undergraduate student (graduated in 2011, join MIT for PhD study)
    • Huasha Zhao, Undergraduate student (graduated in 2011, join Berckly for PhD study)
    • Yurong Jiang, Undergraduate student (graduated in 2010, join USC for PhD study)