POSITION/TITLE
SRIBD: Deputy Director of Social Behavior Big Data Lab
Associate Professor at the School of Law at the University of Hong Kong
RESEARCH FIELD
the cross-fields of China's judicial system, law and economic development, law and data science
zhuangliu@cuhk.edu.cn
EDUCATION BACKGROUND
Bachelor’s and Ph.D. degree from Peking University, and J.S.D from University of Chicago.
BIOGRAPHY
Prof. Zhuang Liu currently serves as the Deputy Director of Social Behavior Big Data Lab at Shenzhen Research Institute of Big Data, and an Associate Professor at the School of Law at the University of Hong Kong. He received his bachelor's and Ph.D. degrees from Peking University, and his J.S.D. from the University of Chicago. He focuses on the cross-fields of China’s judicial system, law and economic development, law and data science, and has published a significant amount of work on world top journals in related fields (such as Journal of Legal Studies, Journal of Legal Analysis, American Journal of Comparative Law, China Quarterly, Journal of Contemporary China, etc.). He has led several national and local research projects, including projects supported by the National Key Research and Development Plan, the National Natural Science Foundation of China, etc.
ACADEMIC PUBLICATIONS
Liu, Z. (2018). Does reason writing reduce decision bias? Experimental evidence from judges in China. The Journal of Legal Studies, 47(1), 83-118.
Tang, Y., & Liu, J. Z. (2019). Mass Publicity of Chinese Court Decisions. China Review, 19(2), 15-40.
Liu, J. Z., & Zhang, A. H. (2019). Ownership and political control: Evidence from charter amendments. International Review of Law and Economics, 60, 105853.
OTHER OPTIONAL ENTRIES
Published a significant number of papers on world top journals in related fields (such as Journal of Legal Studies, Journal of Legal Analysis, American Journal of Comparative Law, China Quarterly, Journal of Contemporary China, etc.). He has led several national and local research projects, including projects supported by the National Key Research and Development Plan, the National Natural Science Foundation of China, etc.
POSITION/TITLE
SRIBD: Deputy Director of Social Behavior Big Data Lab
Associate Professor at the School of Law at the University of Hong Kong
RESEARCH FIELD
the cross-fields of China's judicial system, law and economic development, law and data science
zhuangliu@cuhk.edu.cn
EDUCATION BACKGROUND
Bachelor’s and Ph.D. degree from Peking University, and J.S.D from University of Chicago.
BIOGRAPHY
Prof. Zhuang Liu currently serves as the Deputy Director of Social Behavior Big Data Lab at Shenzhen Research Institute of Big Data, and an Associate Professor at the School of Law at the University of Hong Kong. He received his bachelor's and Ph.D. degrees from Peking University, and his J.S.D. from the University of Chicago. He focuses on the cross-fields of China’s judicial system, law and economic development, law and data science, and has published a significant amount of work on world top journals in related fields (such as Journal of Legal Studies, Journal of Legal Analysis, American Journal of Comparative Law, China Quarterly, Journal of Contemporary China, etc.). He has led several national and local research projects, including projects supported by the National Key Research and Development Plan, the National Natural Science Foundation of China, etc.
ACADEMIC PUBLICATIONS
Liu, Z. (2018). Does reason writing reduce decision bias? Experimental evidence from judges in China. The Journal of Legal Studies, 47(1), 83-118.
Tang, Y., & Liu, J. Z. (2019). Mass Publicity of Chinese Court Decisions. China Review, 19(2), 15-40.
Liu, J. Z., & Zhang, A. H. (2019). Ownership and political control: Evidence from charter amendments. International Review of Law and Economics, 60, 105853.
OTHER OPTIONAL ENTRIES
Published a significant number of papers on world top journals in related fields (such as Journal of Legal Studies, Journal of Legal Analysis, American Journal of Comparative Law, China Quarterly, Journal of Contemporary China, etc.). He has led several national and local research projects, including projects supported by the National Key Research and Development Plan, the National Natural Science Foundation of China, etc.
POSITION/TITLE
SRIBD Research Scientist
RESEARCH FIELD
Time series, Econometric modeling, Machine learning
shandai@sribd.cn
EDUCATION BACKGROUND
Ph.D. in The Chinese University of Hong Kong
Bachelor’s degree in University of Science and Technology of China
Major Achievements/Honors
Overseas High-Caliber Personnel, Shenzhen
Overseas Research Award In The Chinese University of Hong Kong
Outstanding Graduate In University of Science and Technology of China
BIOGRAPHY
Dr. Shan Dai is currently a research scientist in Shenzhen Research Institute of Big Data. His main research interests include time series, econometric modeling and machine learning related theory and practice. He received a Bachelor's degree in Science from University of Science and Technology of China, a Ph.D. degree in Statistics from The Chinese University of Hong Kong. He was also recognized as an Overseas High-Caliber Personnel (Shenzhen). He has published several peer-reviewed papers on statistical journals and machine learning conferences including Journal of Time Series Analysis and Proceedings of the Winter Simulation Conference, and got several patents granted and accepted. Dai is currently the PI of a Shenzhen Excellent Science and Technology Innovation Talents Cultivation Project (PhD Start-Up).
SELECTED PUBLICATIONS (*denotes corresponding author)
- Dai, S. and Chan, N.H.* (2023). Testing of Constant Parameters for Semi-Parametric Functional Coefficient Models with Integrated Covariates. J. Time Ser. Anal., 44: 474-486.
- Zhang, M., Liu, G., Dai, S.*, He, Y. (2023). Input Uncertainty Quantification Via Simulation Bootstrapping. Proceedings of the 2023 Winter Simulation Conference (WSC). Accepted.
POSITION/TITLE
SRIBD Research Scientist
RESEARCH FIELD
Time series, Econometric modeling, Machine learning
shandai@sribd.cn
EDUCATION BACKGROUND
Ph.D. in The Chinese University of Hong Kong
Bachelor’s degree in University of Science and Technology of China
Major Achievements/Honors
Overseas High-Caliber Personnel, Shenzhen
Overseas Research Award In The Chinese University of Hong Kong
Outstanding Graduate In University of Science and Technology of China
BIOGRAPHY
Dr. Shan Dai is currently a research scientist in Shenzhen Research Institute of Big Data. His main research interests include time series, econometric modeling and machine learning related theory and practice. He received a Bachelor's degree in Science from University of Science and Technology of China, a Ph.D. degree in Statistics from The Chinese University of Hong Kong. He was also recognized as an Overseas High-Caliber Personnel (Shenzhen). He has published several peer-reviewed papers on statistical journals and machine learning conferences including Journal of Time Series Analysis and Proceedings of the Winter Simulation Conference, and got several patents granted and accepted. Dai is currently the PI of a Shenzhen Excellent Science and Technology Innovation Talents Cultivation Project (PhD Start-Up).
SELECTED PUBLICATIONS (*denotes corresponding author)
- Dai, S. and Chan, N.H.* (2023). Testing of Constant Parameters for Semi-Parametric Functional Coefficient Models with Integrated Covariates. J. Time Ser. Anal., 44: 474-486.
- Zhang, M., Liu, G., Dai, S.*, He, Y. (2023). Input Uncertainty Quantification Via Simulation Bootstrapping. Proceedings of the 2023 Winter Simulation Conference (WSC). Accepted.
POSITION/TITLE
SRIBD Research Scientist
RESEARCH FIELD
Statistical Signal Processing, Optimization Algorithms
rui.zhou@sribd.cn
EDUCATION BACKGROUND
Doctor of Philosophy (Hong Kong University of Science and Technology)
Bachelor of Engineering (Southeast University)
BIOGRAPHY
Dr. Rui Zhou received the B.Eng. degree in information engineering from Southeast University, Nanjing, China, in 2017, and the Ph.D. degree from the Hong Kong University of Science and Technology, Hong Kong, in 2021. He is currently a Research Scientist with Shenzhen Research Institute of Big Data, Shenzhen, China. His research interests include optimization algorithms, statistical signal processing, machine learning, and financial engineering.
ACADEMIC PUBLICATIONS
1. Rui Zhou, Jiaxi Ying, and Daniel P. Palomar, “Covariance Matrix Estimation Under Low-Rank Factor Model with Nonnegative Correlations,” IEEE Trans. on Signal Processing, vol. 70, pp. 4020-4030, Aug. 2022.
2. Rui Zhou and Daniel P. Palomar, Solving High-order Portfolios via Successive Convex Approximation Algorithms, IEEE Trans. on Signal Processing, vol. 69, pp. 892-904, Feb. 2021.
3. Rui Zhou, Junyan Liu, Sandeep Kumar, and Daniel P. Palomar, Student’s t VAR Modeling with Missing Data via Stochastic EM and Gibbs Sampling, IEEE Trans. on Signal Processing, vol. 68, pp. 6198-6211, Oct. 2020.
4. Rui Zhou and Daniel P. Palomar, Understanding the Quintile Portfolio, IEEE Trans. on Signal Processing, vol. 68, pp. 4030-4040, July 2020.
POSITION/TITLE
Research Scientist / Research Associate
RESEARCH FIELD
Crowd Intelligence, Edge Learning, Integrated Sensing and Communication, Over-the-air Computation, Wireless Power Transfer
lixiaoyang@sribd.cn
PERSONAL WEBSITE
https://xiaoyang0118.github.io/
EDUCATION BACKGROUND
PhD degree from The University of Hong Kong
Bachelor degree from Southern University of Science and Technology
BIOGRAPHY
Dr. Xiaoyang Li obtained his PhD degree from The University of Hong Kong at 2020, and Bachelor degree from Southern University of Science and Technology (SUSTech) at 2016. From 2020 to 2022, he has served as the Presidential Distinguished Research Fellow in SUSTech. He has published more than 20 papers on top journals and conferences, and applied more than 10 patents. He has been in charge of projects from National Natural Science Foundation of China, Shenzhen Natural Science Foundation, China Postdoctoral Science Foundation, and China Academy of Information and Communication Technology. He has been elected as the Forbes 30 under 30, Guangdong Overseas Youth Talent, and Shenzhen Overseas High-caliber Personnel.
ACADEMIC PUBLICATIONS
1. X. Li, Y. Gong*, K. Huang, and Z. Niu, “Over-the-Air Integrated Sensing, Communication, and Computation in IoT Networks”, IEEE Wireless Communications, early access, 2023.
2. X. Li, F. Liu, Z. Zhou, G. Zhu, S. Wang, K. Huang, and Y. Gong*, “Integrated Sensing, communication, and computation Over-the-Air: MIMO Beamforming Design”, IEEE Transactions on Wireless Communications, early access, 2023.
3. X. Li, G. Zhu, K. Shen, K. Han, K. Huang, and Y. Gong*, “Energy Efficient Wireless Crowd Labelling: Joint Annotator Clustering and Power Control”, IEEE Transactions on Wireless Communications, early access, 2022.
4. Z. Zhou, X. Li*, C. You, K. Huang, and Y. Gong*, “Joint Sensing and Communication-Rate Control for Energy Efficient Mobile Crowd Sensing”, IEEE Transactions on Wireless Communications, vol. 22, no. 2, pp. 1314-1327, 2023.
5. X. Li, S. Wang, G. Zhu, Z. Zhou, K. Huang, and Y. Gong*, “Data Partition and Rate Control for Learning and Energy Efficient Edge Intelligence”, IEEE Transactions on Wireless Communications, vol. 21, no. 11, pp. 9127-9142, Nov. 2022.
6. X. Li, G. Zhu, K. Shen, W. Yu, Y. Gong*, and K. Huang*, “Joint Annotator-and-Spectrum Allocation in Wireless Networks for Crowd Labelling”, IEEE Transactions on Wireless Communications, vol. 19, no. 9, pp. 6116-6128, Sep. 2020.
7. X. Li, G. Zhu, Y. Gong, and K. Huang*, “Wirelessly Powered Data Aggregation for IoT via Over-the-Air Function Computation: Beamforming and Power Control”, IEEE Transactions on Wireless Communications, vol. 18, no. 7, pp. 3437-3452, July 2019.
8. X. Li, C. You, S. Andreev, Y. Gong, and K. Huang*, “Wirelessly Powered Crowd Sensing: Joint Power Transfer, Sensing, Compression, and Transmission”, IEEE Journal on Selected Areas in Communications, vol. 37, no. 2, pp. 391-406, Feb. 2019.
POSITION/TITLE
Research Scientist / Research Associate
RESEARCH FIELD
Crowd Intelligence, Edge Learning, Integrated Sensing and Communication, Over-the-air Computation, Wireless Power Transfer
lixiaoyang@sribd.cn
PERSONAL WEBSITE
https://xiaoyang0118.github.io/
EDUCATION BACKGROUND
PhD degree from The University of Hong Kong
Bachelor degree from Southern University of Science and Technology
BIOGRAPHY
Dr. Xiaoyang Li obtained his PhD degree from The University of Hong Kong at 2020, and Bachelor degree from Southern University of Science and Technology (SUSTech) at 2016. From 2020 to 2022, he has served as the Presidential Distinguished Research Fellow in SUSTech. He has published more than 20 papers on top journals and conferences, and applied more than 10 patents. He has been in charge of projects from National Natural Science Foundation of China, Shenzhen Natural Science Foundation, China Postdoctoral Science Foundation, and China Academy of Information and Communication Technology. He has been elected as the Forbes 30 under 30, Guangdong Overseas Youth Talent, and Shenzhen Overseas High-caliber Personnel.
ACADEMIC PUBLICATIONS
1. X. Li, Y. Gong*, K. Huang, and Z. Niu, “Over-the-Air Integrated Sensing, Communication, and Computation in IoT Networks”, IEEE Wireless Communications, early access, 2023.
2. X. Li, F. Liu, Z. Zhou, G. Zhu, S. Wang, K. Huang, and Y. Gong*, “Integrated Sensing, communication, and computation Over-the-Air: MIMO Beamforming Design”, IEEE Transactions on Wireless Communications, early access, 2023.
3. X. Li, G. Zhu, K. Shen, K. Han, K. Huang, and Y. Gong*, “Energy Efficient Wireless Crowd Labelling: Joint Annotator Clustering and Power Control”, IEEE Transactions on Wireless Communications, early access, 2022.
4. Z. Zhou, X. Li*, C. You, K. Huang, and Y. Gong*, “Joint Sensing and Communication-Rate Control for Energy Efficient Mobile Crowd Sensing”, IEEE Transactions on Wireless Communications, vol. 22, no. 2, pp. 1314-1327, 2023.
5. X. Li, S. Wang, G. Zhu, Z. Zhou, K. Huang, and Y. Gong*, “Data Partition and Rate Control for Learning and Energy Efficient Edge Intelligence”, IEEE Transactions on Wireless Communications, vol. 21, no. 11, pp. 9127-9142, Nov. 2022.
6. X. Li, G. Zhu, K. Shen, W. Yu, Y. Gong*, and K. Huang*, “Joint Annotator-and-Spectrum Allocation in Wireless Networks for Crowd Labelling”, IEEE Transactions on Wireless Communications, vol. 19, no. 9, pp. 6116-6128, Sep. 2020.
7. X. Li, G. Zhu, Y. Gong, and K. Huang*, “Wirelessly Powered Data Aggregation for IoT via Over-the-Air Function Computation: Beamforming and Power Control”, IEEE Transactions on Wireless Communications, vol. 18, no. 7, pp. 3437-3452, July 2019.
8. X. Li, C. You, S. Andreev, Y. Gong, and K. Huang*, “Wirelessly Powered Crowd Sensing: Joint Power Transfer, Sensing, Compression, and Transmission”, IEEE Journal on Selected Areas in Communications, vol. 37, no. 2, pp. 391-406, Feb. 2019.
POSITION/TITLE
SRIBD Research Scientist
RESEARCH FIELD
Convex and nonconvex optimization
zhaolicheng@sribd.cn
EDUCATION BACKGROUND
2018.11.15 phd
2014.06.30 bachelor
BIOGRAPHY
Licheng Zhao received the B.S. degree in Information Engineering from Southeast University (SEU), Nanjing, China, in 2014, and the Ph.D. degree with the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology (HKUST), in 2018. Since June 2018, he has been an algorithm engineer in recommendation system with JD.COM, China. Since Dec. 2021, he has served as a research scientist in Shenzhen Research Institute of Big Data (SRIBD). His research interests are in optimization theory and efficient algorithms, with applications in signal processing, machine learning, and deep learning in recommendation system.
ACADEMIC PUBLICATIONS
Licheng Zhao, Yiwei Wang, Sandeep Kumar, and Daniel P. Palomar, “Optimization Algorithms for Graph Laplacian Estimation via ADMM and MM,” IEEE Trans. on Signal Processing, vol. 67, no. 16, pp. 4231-4244, Aug. 2019.
Licheng Zhao and Daniel P. Palomar, “A Markowitz Portfolio Approach to Options Trading,” IEEE Trans. on Signal Processing, vol. 66, no. 16, pp. 4223-4238, Aug. 2018.
Licheng Zhao and Daniel P. Palomar, “Maximin Joint Optimization of Transmitting Code and Receiving Filter in Radar and Communications,” IEEE Trans. on Signal Processing, vol. 65, no. 4, pp. 850-863, Feb. 2017.
Licheng Zhao, Junxiao Song, Prabu Babu, and Daniel P. Palomar, “A Unified Framework for Low Autocorrelation Sequence Design via Majorization-Minimization,” IEEE Trans. on Signal Processing, vol. 65, no. 2, pp. 438-453, Jan. 2017.
Licheng Zhao, Prabhu Babu, and Daniel P. Palomar, “Efficient Algorithms on Robust Low-Rank Matrix Completion Against Outliers,” IEEE Trans. on Signal Processing, vol. 64, no. 18, pp. 4767- 4780, Sept. 2016.
POSITION/TITLE
SRIBD Research Scientist
RESEARCH FIELD
Convex and nonconvex optimization
zhaolicheng@sribd.cn
EDUCATION BACKGROUND
2018.11.15 phd
2014.06.30 bachelor
BIOGRAPHY
Licheng Zhao received the B.S. degree in Information Engineering from Southeast University (SEU), Nanjing, China, in 2014, and the Ph.D. degree with the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology (HKUST), in 2018. Since June 2018, he has been an algorithm engineer in recommendation system with JD.COM, China. Since Dec. 2021, he has served as a research scientist in Shenzhen Research Institute of Big Data (SRIBD). His research interests are in optimization theory and efficient algorithms, with applications in signal processing, machine learning, and deep learning in recommendation system.
ACADEMIC PUBLICATIONS
Licheng Zhao, Yiwei Wang, Sandeep Kumar, and Daniel P. Palomar, “Optimization Algorithms for Graph Laplacian Estimation via ADMM and MM,” IEEE Trans. on Signal Processing, vol. 67, no. 16, pp. 4231-4244, Aug. 2019.
Licheng Zhao and Daniel P. Palomar, “A Markowitz Portfolio Approach to Options Trading,” IEEE Trans. on Signal Processing, vol. 66, no. 16, pp. 4223-4238, Aug. 2018.
Licheng Zhao and Daniel P. Palomar, “Maximin Joint Optimization of Transmitting Code and Receiving Filter in Radar and Communications,” IEEE Trans. on Signal Processing, vol. 65, no. 4, pp. 850-863, Feb. 2017.
Licheng Zhao, Junxiao Song, Prabu Babu, and Daniel P. Palomar, “A Unified Framework for Low Autocorrelation Sequence Design via Majorization-Minimization,” IEEE Trans. on Signal Processing, vol. 65, no. 2, pp. 438-453, Jan. 2017.
Licheng Zhao, Prabhu Babu, and Daniel P. Palomar, “Efficient Algorithms on Robust Low-Rank Matrix Completion Against Outliers,” IEEE Trans. on Signal Processing, vol. 64, no. 18, pp. 4767- 4780, Sept. 2016.
POSITION/TITLE
Research Scientist
RESEARCH FIELD
Reinforcement learning, Green building, Game theory & network optimization
zhangliang@sribd.cn
EDUCATION BACKGROUND
9/2011-9/2016 PhD in Department of Computing, The Hong Kong Polytechnic University
9/2007-6/2011 Bachelor degree, Huazhong University of Science and Technology
BIOGRAPHY
Dr. ZHANG Liang is research scientist in SRIBD. Before that, he is an associate researcher in Peng Cheng Laboratory and selected as Shenzhen Peacock Program C talent. Dr ZHANG graduated from The Hong Kong Polytechnic University in 2016; from 2017 to 2022, he joined JD.com and Tencent, and published a number of reinforcement learning decision-making research papers such as KDD and SIGIR, which were successfully applied in commercial advertising in JD and The Honor of King in Tencent. He has published more than 20 papers with 1100+ Google citations. His research interests include reinforcement learning and its applications, green buildings, and network optimization.
ACADEMIC PUBLICATIONS
Network optimization
1,Y. Zhao, H. Wang, H. Su, L. Zhang, R. Zhang, D. Wang, K. Xu,“Understand love of variety in wireless data market under sponsored data plans”,IEEE JSAC 2020 (CCF-A)
2,Y. Zhao, H, Su, L. Zhang, D. Wang, K. Xu, "Variety Matters: A New Model for the Wireless Data Market under Sponsored Data Plans", in Proc. of IEEE/ACM IWQoS 2019. (CCF-B)
3, Liang Zhang, Weijie Wu and Dan Wang, "TDS: Time-Dependent Sponsored Data Plan for Wireless Data Traffic Market", in Proc. of IEEE INFOCOM 2016. (CCF-A)
4, Liang Zhang, Weijie Wu and Dan Wang,"Sponsored Data Plan: A Two-Class Service Model in Wireless Data Networks", in Proc. of ACM SIGMETRICS 2015. (CCF-B, CORE* A)
5, Liang Zhang, Weijie Wu and Dan Wang, "Time Dependent Pricing in Wireless Data Networks: Flat-rates vs. Usage-based Schemes", in Proc. of IEEE INFOCOM, 2014 (CCF-A)
Green Building
6, Z Zheng, F Wang, D Wang, L Zhang, "An Urban Mobility Model with Buildings Involved:
Bridging Theory to Practice", ACM TOSN 2020 (CCF-B)
7,Z. Zheng, F. Wang, D. Wang, and L. Zhang, "Buildings affect Mobile Pattens: Developing a new Urban Mobility Model", in Proc. of ACM Buildsys’18 (Best Paper Award)
8,L. Zhang, A. H. Lam and D. Wang, "Strategy proof Thermal Comfort Voting in Buildings,
in Proc. of ACM BuildSys’14
Reinforcement Learning and its applications
9, D. Zhao, L. Zhang*, B. Zhang, L. Zheng, Y. Bao, W. Yan, "MaHRL: Multi-goals Abstraction based Deep Hierarchical Reinforcement Learning for Recommendations", in Proc. of ACM SIGIR 2020 (CCF-A)
10, Y. Su, L. Zhang*, Q. Dai, B. Zhang, J. Yan, S. Xu, D. Wang, Y. He, Y. Bao, and W. Yan, "An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration", in Proc. of IJCAI 2020 (CCF-A)
11, Y. Wang, L. Zhang(co-first author), Q. Dai, F. Sun, B. Zhang, Y. He, Y. Bao and W. Yan , "Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction", in Proc. of ACM CIKM 2019 (CCF-B)
12, X. Zhao, L. Xia, L. Zhang, Z. Ding, D. Yin, J. Tang, "Deep Reinforcement Learning for Page-wise Recommendations", in Proc. of ACM RecSys 2018 (CCF-B，google scholar 270+)
13, X. Zhao, L. Zhang, Z. Ding, L. Xia, J. Tang, and D. Yin. "Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning". in Proc. of ACM SIGKDD 2018. (CCF-A google scholar 280+)
14,W. Lu, F. Chung, K. Lai, L. Zhang,"Recommender system based on scarce information mining", Neural Networks, 2017 (CCF-B)
15, Q Dai, X Shen, Z Zheng, L Zhang, Q Li, D Wang, "Adversarial training regularization for negative sampling based network embedding", Information Sciences 2021 (CCF-B)
16, Q. Dai, X. Shen, L. Zhang, Q. Li, D. Wang, "Adversarial Training Methods for Network Embedding", in Proc. of ACM WWW 2019 (CCF-A)
POSITION/TITLE
Research Scientist
RESEARCH FIELD
Reinforcement learning, Green building, Game theory & network optimization
zhangliang@sribd.cn
EDUCATION BACKGROUND
9/2011-9/2016 PhD in Department of Computing, The Hong Kong Polytechnic University
9/2007-6/2011 Bachelor degree, Huazhong University of Science and Technology
BIOGRAPHY
Dr. ZHANG Liang is research scientist in SRIBD. Before that, he is an associate researcher in Peng Cheng Laboratory and selected as Shenzhen Peacock Program C talent. Dr ZHANG graduated from The Hong Kong Polytechnic University in 2016; from 2017 to 2022, he joined JD.com and Tencent, and published a number of reinforcement learning decision-making research papers such as KDD and SIGIR, which were successfully applied in commercial advertising in JD and The Honor of King in Tencent. He has published more than 20 papers with 1100+ Google citations. His research interests include reinforcement learning and its applications, green buildings, and network optimization.
ACADEMIC PUBLICATIONS
Network optimization
1,Y. Zhao, H. Wang, H. Su, L. Zhang, R. Zhang, D. Wang, K. Xu,“Understand love of variety in wireless data market under sponsored data plans”,IEEE JSAC 2020 (CCF-A)
2,Y. Zhao, H, Su, L. Zhang, D. Wang, K. Xu, "Variety Matters: A New Model for the Wireless Data Market under Sponsored Data Plans", in Proc. of IEEE/ACM IWQoS 2019. (CCF-B)
3, Liang Zhang, Weijie Wu and Dan Wang, "TDS: Time-Dependent Sponsored Data Plan for Wireless Data Traffic Market", in Proc. of IEEE INFOCOM 2016. (CCF-A)
4, Liang Zhang, Weijie Wu and Dan Wang,"Sponsored Data Plan: A Two-Class Service Model in Wireless Data Networks", in Proc. of ACM SIGMETRICS 2015. (CCF-B, CORE* A)
5, Liang Zhang, Weijie Wu and Dan Wang, "Time Dependent Pricing in Wireless Data Networks: Flat-rates vs. Usage-based Schemes", in Proc. of IEEE INFOCOM, 2014 (CCF-A)
Green Building
6, Z Zheng, F Wang, D Wang, L Zhang, "An Urban Mobility Model with Buildings Involved:
Bridging Theory to Practice", ACM TOSN 2020 (CCF-B)
7,Z. Zheng, F. Wang, D. Wang, and L. Zhang, "Buildings affect Mobile Pattens: Developing a new Urban Mobility Model", in Proc. of ACM Buildsys’18 (Best Paper Award)
8,L. Zhang, A. H. Lam and D. Wang, "Strategy proof Thermal Comfort Voting in Buildings,
in Proc. of ACM BuildSys’14
Reinforcement Learning and its applications
9, D. Zhao, L. Zhang*, B. Zhang, L. Zheng, Y. Bao, W. Yan, "MaHRL: Multi-goals Abstraction based Deep Hierarchical Reinforcement Learning for Recommendations", in Proc. of ACM SIGIR 2020 (CCF-A)
10, Y. Su, L. Zhang*, Q. Dai, B. Zhang, J. Yan, S. Xu, D. Wang, Y. He, Y. Bao, and W. Yan, "An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration", in Proc. of IJCAI 2020 (CCF-A)
11, Y. Wang, L. Zhang(co-first author), Q. Dai, F. Sun, B. Zhang, Y. He, Y. Bao and W. Yan , "Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction", in Proc. of ACM CIKM 2019 (CCF-B)
12, X. Zhao, L. Xia, L. Zhang, Z. Ding, D. Yin, J. Tang, "Deep Reinforcement Learning for Page-wise Recommendations", in Proc. of ACM RecSys 2018 (CCF-B，google scholar 270+)
13, X. Zhao, L. Zhang, Z. Ding, L. Xia, J. Tang, and D. Yin. "Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning". in Proc. of ACM SIGKDD 2018. (CCF-A google scholar 280+)
14,W. Lu, F. Chung, K. Lai, L. Zhang,"Recommender system based on scarce information mining", Neural Networks, 2017 (CCF-B)
15, Q Dai, X Shen, Z Zheng, L Zhang, Q Li, D Wang, "Adversarial training regularization for negative sampling based network embedding", Information Sciences 2021 (CCF-B)
16, Q. Dai, X. Shen, L. Zhang, Q. Li, D. Wang, "Adversarial Training Methods for Network Embedding", in Proc. of ACM WWW 2019 (CCF-A)
POSITION/TITLE
Data Scientist
EDUCATION BACKGROUND
Bachelor in Applied Mathematics at Peking University
RESEARCH INTEREST
administrative law, judicial reform
zhangyuxia@sribd.cn
MAJOR ACHIEVEMENTS/HONOR
Worked as consultants and investment directors in leading companies including KPMG, BGI, Country Garden, etc.; experience in start-up in the field of artificial intelligence; experience in the requirement analysis and construction for application systems.
BIOGRAPHY
Zhang Yuxia graduated from the Department of Applied Mathematics at Peking University. He currently serves as a data scientist at Shenzhen Research Institute of Big Data and is responsible for the construction of the large-scale legal database. He has worked as consultants and investment directors in leading companies including KPMG, BGI, Country Garden, etc.; and has gained rich experience in start-up in the field of artificial intelligence as well as experience in the requirement analysis and construction for application
POSITION/TITLE
Data Scientist
EDUCATION BACKGROUND
Bachelor in Applied Mathematics at Peking University
RESEARCH INTEREST
administrative law, judicial reform
zhangyuxia@sribd.cn
MAJOR ACHIEVEMENTS/HONOR
Worked as consultants and investment directors in leading companies including KPMG, BGI, Country Garden, etc.; experience in start-up in the field of artificial intelligence; experience in the requirement analysis and construction for application systems.
BIOGRAPHY
Zhang Yuxia graduated from the Department of Applied Mathematics at Peking University. He currently serves as a data scientist at Shenzhen Research Institute of Big Data and is responsible for the construction of the large-scale legal database. He has worked as consultants and investment directors in leading companies including KPMG, BGI, Country Garden, etc.; and has gained rich experience in start-up in the field of artificial intelligence as well as experience in the requirement analysis and construction for application
POSITION/TITLE
SRIBD Research Scientist
RESEARCH FIELD
Wireless communications, IoT, AI and applications
hangdavidli@sribd.cn
EDUCATION BACKGROUND
Ph.D., Electrical Engineering, Texas A&M University, 2016
M.S., Communication and Information Engineering, Beihang University, 2011
B.S., Electronic Information Engineering, Beihang University, 2008
BIOGRAPHY
Hang Li received the Ph.D. degree from Texas A&M University, College Station, TX, USA, in 2016. He was a postdoctoral research associate with Texas A&M University (Sept. 2016-Aug. 2017) and University of California-Davis (Sept. 2017-Mar. 2018). After being a visiting research scholar (Apr. 2018 – June 2019) at Shenzhen Research Institute of Big Data, Shenzhen, China, he has been a research scientist since June 2019. His current research interests include wireless networks, Internet of things, stochastic optimization, and applications of machine learning.
He has conducted peer-reviews for many conferences (like WCNC, INFOCOM, GLOBECOM, etc.) and journals (like IEEE Internet of Things Journal, IEEE JSAC, IEEE TWC, etc.). He also is recognized as Overseas High-Caliber Personnel (Level C) by Human Resources and Social Security Administration of Shenzhen Municipality in 2020. He has more than 50 publications in top conferences and journals.
ACADEMIC PUBLICATIONS
1. Jiaxin Liu, Xiongyan Tang, Guangxu Zhu, Xinzhou Cheng, Liqiang Zhao, and Hang Li, ``Multi-feature traffic prediction based on signaling information for cellular networks,” IEEE Trans. Vehicular Technology, vol. 73, no. 2, pp. 2280-2291, Feb. 2024.
2. Shuai Ma, Weining Qiao, Youlong Wu, Hang Li, Guangming Shi, Dahua Gao, Yuanming Shi, Shiyin Li, and Naofal Al-Dhahir, ``Task-oriented explainable semantic communications,'' IEEE Trans. Wireless Commun., vol. 22, no. 12, pp. 9248-9262, Dec. 2023.
3. Xuemei Wang, Jiachen Wang, Hang Li, Xiaoyang Li, Chao Shen, and Guangxu Zhu, ``UKFWiTr: a single-link indoor tracking method based on WiFi CSI,'' IEEE Wireless Communications and Networking Conference (WCNC), Glasgow, UK, Mar. 26-29, 2023.
4. 陆彦辉，柳寒，李航，朱光旭，“基于多鉴别器生成对抗网络的时间序列生成模型”，通信学报，第43卷，第10期，第167-176页，2022年10月.
5. Man Chu, Hang Li, Xuewen Liao, and Shuguang Cui, “Reinforcement learning based multi-access control and battery prediction with energy harvesting in IoT systems,” IEEE Internet of Things J., vol. 6, no. 2, pp. 2009-2020, Apr. 2019.
POSITION/TITLE
SRIBD Research Scientist
RESEARCH FIELD
Wireless communications, IoT, AI and applications
hangdavidli@sribd.cn
EDUCATION BACKGROUND
Ph.D., Electrical Engineering, Texas A&M University, 2016
M.S., Communication and Information Engineering, Beihang University, 2011
B.S., Electronic Information Engineering, Beihang University, 2008
BIOGRAPHY
Hang Li received the Ph.D. degree from Texas A&M University, College Station, TX, USA, in 2016. He was a postdoctoral research associate with Texas A&M University (Sept. 2016-Aug. 2017) and University of California-Davis (Sept. 2017-Mar. 2018). After being a visiting research scholar (Apr. 2018 – June 2019) at Shenzhen Research Institute of Big Data, Shenzhen, China, he has been a research scientist since June 2019. His current research interests include wireless networks, Internet of things, stochastic optimization, and applications of machine learning.
He has conducted peer-reviews for many conferences (like WCNC, INFOCOM, GLOBECOM, etc.) and journals (like IEEE Internet of Things Journal, IEEE JSAC, IEEE TWC, etc.). He also is recognized as Overseas High-Caliber Personnel (Level C) by Human Resources and Social Security Administration of Shenzhen Municipality in 2020. He has more than 50 publications in top conferences and journals.
ACADEMIC PUBLICATIONS
1. Jiaxin Liu, Xiongyan Tang, Guangxu Zhu, Xinzhou Cheng, Liqiang Zhao, and Hang Li, ``Multi-feature traffic prediction based on signaling information for cellular networks,” IEEE Trans. Vehicular Technology, vol. 73, no. 2, pp. 2280-2291, Feb. 2024.
2. Shuai Ma, Weining Qiao, Youlong Wu, Hang Li, Guangming Shi, Dahua Gao, Yuanming Shi, Shiyin Li, and Naofal Al-Dhahir, ``Task-oriented explainable semantic communications,'' IEEE Trans. Wireless Commun., vol. 22, no. 12, pp. 9248-9262, Dec. 2023.
3. Xuemei Wang, Jiachen Wang, Hang Li, Xiaoyang Li, Chao Shen, and Guangxu Zhu, ``UKFWiTr: a single-link indoor tracking method based on WiFi CSI,'' IEEE Wireless Communications and Networking Conference (WCNC), Glasgow, UK, Mar. 26-29, 2023.
4. 陆彦辉，柳寒，李航，朱光旭，“基于多鉴别器生成对抗网络的时间序列生成模型”，通信学报，第43卷，第10期，第167-176页，2022年10月.
5. Man Chu, Hang Li, Xuewen Liao, and Shuguang Cui, “Reinforcement learning based multi-access control and battery prediction with energy harvesting in IoT systems,” IEEE Internet of Things J., vol. 6, no. 2, pp. 2009-2020, Apr. 2019.
