Yong Li (李勇)
Associate Professor, Dept. of Electronic Engineering, Tsinghua University
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 big data, mobile computing, wireless communications and networking.
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 7200 Google Scholar). Among them, ten are ESI Highly Cited Papers in Computer Science, and five receive conference Best Paper (run-up) Awards.
We are also looking talented BS/MS/Ph. D. students, and visiting scholars who are interested in working in our lab.
- 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].
- IEEE Senior Member, ACM Member;
- 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.
- Associate Editor: IEEE Transactions on Network Service and Management；
- Associate Editor: ACM IMWUT (UBICOMP, CCF A类)；
- SPC: IJCAI 2019, AAAI 2019；
- PC: WWW 2020, AAAI 2020, KDD 2019, WWWW 2019, SIGIR 2019, SIGIR 2018, WWWW 2018, ICWSM 2018...；
- TPC Chair/Co-Chair: ICC 2018 Symposum of Wireless Networks, IWCMC 2018, ChinaCom 2015, Simplex 2013 (WWW workshop)；
- TPC Member: INFOCOM'18, WWW'17, 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
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.
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)