ZHAO, Licheng

POSITION/TITLE

SRIBD Research Scientist

RESEARCH FIELD

Convex and nonconvex optimization

EMAIL

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.

ZHAO, Licheng

POSITION/TITLE

SRIBD Research Scientist

RESEARCH FIELD

Convex and nonconvex optimization

EMAIL

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.

ZHAO, Licheng

POSITION/TITLE

SRIBD Research Scientist

RESEARCH FIELD

Convex and nonconvex optimization

EMAIL

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.

ZHANG, Liang

POSITION/TITLE

Research Scientist

RESEARCH FIELD

Reinforcement learning, Green building, Game theory & network optimization

EMAIL

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)

ZHANG, Liang

POSITION/TITLE

Research Scientist

RESEARCH FIELD

Reinforcement learning, Green building, Game theory & network optimization

EMAIL

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)

ZHANG, Liang

POSITION/TITLE

Research Scientist

RESEARCH FIELD

Reinforcement learning, Green building, Game theory & network optimization

EMAIL

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)

ZHANG, Liang

POSITION/TITLE

Research Scientist

RESEARCH FIELD

Reinforcement learning, Green building, Game theory & network optimization

EMAIL

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)

WU, Jianghua

POSITION/TITLE

Research Scientist 

RESEARCH FIELD

Power System Optimization, Mixed Integer Programming, Optimization Algorithms, and Machine Learning

EMAIL

wujianghua@sribd.cn

EDUCATION BACKGROUND

Ph.D. from the University of Connecticut (UConn)

Bachelor's degree from Shenyang Jianzhu University (SJZU)

BIOGRAPHY

Dr. Jianghua Wu received his Ph.D. from the University of Connecticut (UConn) in 2023. He is currently a Research Scientist with the Shenzhen Research Institute of Big Data. His main research interests are power system optimization with distributed energy sources, grid optimization considering renewable resources and energy storage systems, mixed-integer programming algorithms, and the integration of traditional optimization and machine learning.

ACADEMIC PUBLICATIONS

1.  J. Wu, P. Luh, Y. Chen, M. Bragin and B. Yan, “A Novel Optimization Approach for Subhourly Unit Commitment with Large Numbers of Units and Virtual Transactions,” IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3716–3725, Sep. 2022.

2.  J. Wu, P. Luh, Y. Chen, B. Yan and M. Bragin, “Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment,” IEEE Transactions on Power Systems, Jan. 2023. (Accepted)

3. J. Wu, Z. Wang, Y. Chen, B. Yan and M. A. Bragin, P. B. Luh, “A Hybrid Machine Learning and Optimization Approach with Feasibility Layer Assistance for Sub-hourly Unit Commitment,” (Under Review)

4. W. Lin, J. Hua, H. Jian, J. Xue, J. Wu, C. Wang and Z. Lin, “High-dimension tie-line security regions for renewable accommodations,” Energy, vol 270, May 2023, 126887.

5. J. Wu, P. B. Luh, Y. Chen, B. Yan, and M. A. Bragin, “A Decomposition and Coordination Approach for Large Sub-hourly Unit Commitment,” 2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, Aug. 2020.

WU, Jianghua

POSITION/TITLE

Research Scientist 

RESEARCH FIELD

Power System Optimization, Mixed Integer Programming, Optimization Algorithms, and Machine Learning

EMAIL

wujianghua@sribd.cn

EDUCATION BACKGROUND

Ph.D. from the University of Connecticut (UConn)

Bachelor's degree from Shenyang Jianzhu University (SJZU)

BIOGRAPHY

Dr. Jianghua Wu received his Ph.D. from the University of Connecticut (UConn) in 2023. He is currently a Research Scientist with the Shenzhen Research Institute of Big Data. His main research interests are power system optimization with distributed energy sources, grid optimization considering renewable resources and energy storage systems, mixed-integer programming algorithms, and the integration of traditional optimization and machine learning.

ACADEMIC PUBLICATIONS

1.  J. Wu, P. Luh, Y. Chen, M. Bragin and B. Yan, “A Novel Optimization Approach for Subhourly Unit Commitment with Large Numbers of Units and Virtual Transactions,” IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3716–3725, Sep. 2022.

2.  J. Wu, P. Luh, Y. Chen, B. Yan and M. Bragin, “Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment,” IEEE Transactions on Power Systems, Jan. 2023. (Accepted)

3. J. Wu, Z. Wang, Y. Chen, B. Yan and M. A. Bragin, P. B. Luh, “A Hybrid Machine Learning and Optimization Approach with Feasibility Layer Assistance for Sub-hourly Unit Commitment,” (Under Review)

4. W. Lin, J. Hua, H. Jian, J. Xue, J. Wu, C. Wang and Z. Lin, “High-dimension tie-line security regions for renewable accommodations,” Energy, vol 270, May 2023, 126887.

5. J. Wu, P. B. Luh, Y. Chen, B. Yan, and M. A. Bragin, “A Decomposition and Coordination Approach for Large Sub-hourly Unit Commitment,” 2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, Aug. 2020.

WU, Jianghua

POSITION/TITLE

Research Scientist 

RESEARCH FIELD

Power System Optimization, Mixed Integer Programming, Optimization Algorithms, and Machine Learning

EMAIL

wujianghua@sribd.cn

EDUCATION BACKGROUND

Ph.D. from the University of Connecticut (UConn)

Bachelor's degree from Shenyang Jianzhu University (SJZU)

BIOGRAPHY

Dr. Jianghua Wu received his Ph.D. from the University of Connecticut (UConn) in 2023. He is currently a Research Scientist with the Shenzhen Research Institute of Big Data. His main research interests are power system optimization with distributed energy sources, grid optimization considering renewable resources and energy storage systems, mixed-integer programming algorithms, and the integration of traditional optimization and machine learning.

ACADEMIC PUBLICATIONS

1.  J. Wu, P. Luh, Y. Chen, M. Bragin and B. Yan, “A Novel Optimization Approach for Subhourly Unit Commitment with Large Numbers of Units and Virtual Transactions,” IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3716–3725, Sep. 2022.

2.  J. Wu, P. Luh, Y. Chen, B. Yan and M. Bragin, “Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment,” IEEE Transactions on Power Systems, Jan. 2023. (Accepted)

3. J. Wu, Z. Wang, Y. Chen, B. Yan and M. A. Bragin, P. B. Luh, “A Hybrid Machine Learning and Optimization Approach with Feasibility Layer Assistance for Sub-hourly Unit Commitment,” (Under Review)

4. W. Lin, J. Hua, H. Jian, J. Xue, J. Wu, C. Wang and Z. Lin, “High-dimension tie-line security regions for renewable accommodations,” Energy, vol 270, May 2023, 126887.

5. J. Wu, P. B. Luh, Y. Chen, B. Yan, and M. A. Bragin, “A Decomposition and Coordination Approach for Large Sub-hourly Unit Commitment,” 2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, Aug. 2020.

WU, Jianghua

POSITION/TITLE

Research Scientist 

RESEARCH FIELD

Power System Optimization, Mixed Integer Programming, Optimization Algorithms, and Machine Learning

EMAIL

wujianghua@sribd.cn

EDUCATION BACKGROUND

Ph.D. from the University of Connecticut (UConn)

Bachelor's degree from Shenyang Jianzhu University (SJZU)

BIOGRAPHY

Dr. Jianghua Wu received his Ph.D. from the University of Connecticut (UConn) in 2023. He is currently a Research Scientist with the Shenzhen Research Institute of Big Data. His main research interests are power system optimization with distributed energy sources, grid optimization considering renewable resources and energy storage systems, mixed-integer programming algorithms, and the integration of traditional optimization and machine learning.

ACADEMIC PUBLICATIONS

1.  J. Wu, P. Luh, Y. Chen, M. Bragin and B. Yan, “A Novel Optimization Approach for Subhourly Unit Commitment with Large Numbers of Units and Virtual Transactions,” IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3716–3725, Sep. 2022.

2.  J. Wu, P. Luh, Y. Chen, B. Yan and M. Bragin, “Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment,” IEEE Transactions on Power Systems, Jan. 2023. (Accepted)

3. J. Wu, Z. Wang, Y. Chen, B. Yan and M. A. Bragin, P. B. Luh, “A Hybrid Machine Learning and Optimization Approach with Feasibility Layer Assistance for Sub-hourly Unit Commitment,” (Under Review)

4. W. Lin, J. Hua, H. Jian, J. Xue, J. Wu, C. Wang and Z. Lin, “High-dimension tie-line security regions for renewable accommodations,” Energy, vol 270, May 2023, 126887.

5. J. Wu, P. B. Luh, Y. Chen, B. Yan, and M. A. Bragin, “A Decomposition and Coordination Approach for Large Sub-hourly Unit Commitment,” 2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, Aug. 2020.

LEI, Shunbo

POSITION/TITLE

Research Scientist

EDUCATION BACKGROUND

Ph.D. (The University of Hong Kong)

B.Eng. (Huazhong University of Science and Technology)

RESEARCH FIELD

Power & energy systems, grid-interactive efficient buildings, infrastructure resilience, optimization, machine/reinforcement learning.

ACADEMIC AREA

Computer Engineering, Electrical Engineering, New Energy Science and Engineering

PERSONAL WEBSITE

http://leishunbo.info/

EMAIL

leishunbo@cuhk.edu.cn

BIOGRAPHY

Shunbo received his B.Eng. degree from Huazhong University of Science and Technology in 2013, and Ph.D. degree from The University of Hong Kong (HKU) in 2017. He was a visiting scholar with Argonne National Laboratory from 2015 to 2017, a postdoctoral researcher with HKU from 2017 to 2019, and a research fellow with the University of Michigan (UM)-Ann Arbor from 2019 to 2021. He is currently an assistant professor at The Chinese University of Hong Kong, Shenzhen. His research interests lie broadly in power and energy, optimization and learning.

Shunbo is currently serving as an editorial board member for IEEE Transactions on Smart Grid, IEEE Power Engineering Letters, Energy Reports, and Protection and Control of Modern Power Systems, the secretary for IEEE PES Loads Subcommittee, and the chair for IEEE PES Task Force on FlexGEB to Enhance Electric Service Resilience. He has served as a TPC member for conferences including IEEE SmartGridComm and IET RPG. He has also (co-)chaired panel sessions at events including IEEE PES GM and IEEE iSPEC.

Shunbo received the IEEE Transactions on Smart Grid Top 5 Outstanding Papers Award (2019-2021), the IEEE Transactions on Smart Grid Best Reviewer Award (2018, 2019 and 2021), and the Netherlands’ 4TU Centre for Resilience Engineering - Young Resilience Fellowship (2021).

ACADEMIC PUBLICATIONS

For the full list of Shunbo’s publications, please visit his Google Scholar profile: https://scholar.google.be/citations?user=ltUvPPkAAAAJ&hl=en

Selected Publications (until May 2022):

  1. Shunbo Lei, David Pozo, Ming-Hao Wang, Qifeng Li, Yupeng Li, and Chaoyi Peng, "Power Economic Dispatch against Extreme Weather Conditions: The Price of Resilience," Renewable and Sustainable Energy Reviews, 157: 111994, Apr. 2022.
  2. Aditya Keskar*, Shunbo Lei*, Taylor Webb, Sarah Nagy, Ian A. Hiskens, Johanna L. Mathieu, and Jeremiah X. Johnson, "Assessing the Performance of Global Thermostat Adjustment in Commercial Buildings for Load Shifting Demand Response," Environmental Research: Infrastructure and Sustainability, 2: 015003, Mar. 2022. (*Co-first and co-corresponding authors)
  3. Shunbo Lei, Johanna L. Mathieu, and Rishee K. Jain, "Performance of Existing Models in Baselining Demand Response from Commercial Building HVAC Fans," ASME Journal of Engineering for Sustainable Buildings and Cities, 2(2): 021002, May 2021.
  4. Rong-Peng Liu, Shunbo Lei*, Chaoyi Peng, Wei Sun, and Yunhe Hou, "Data-based Resilience Enhancement Strategies for Electric-Gas Systems against Sequential Extreme Weather Events," IEEE Transactions on Smart Grid, 11(6): 5383-5395, Nov 2020. (*Corresponding author)
  5. Shunbo Lei, Chen Chen, Yue Song, and Yunhe Hou, "Radiality Constraints for Resilient Reconfiguration of Distribution Systems: Formulation and Application to Microgrid Formation," IEEE Transactions on Smart Grid, 11(5): 3944-3956, Sep 2020.
  6. Shunbo Lei, David Hong, Johanna L. Mathieu, and Ian A. Hiskens, "Baseline Estimation of Commercial Building HVAC Fan Power Using Tensor Completion," in Proceedings of the Power Systems Computation Conference (PSCC), Porto, Portugal, Jun 2020.
  7. Shunbo Lei, Chen Chen, Yupeng Li, and Yunhe Hou, "Resilient Disaster Recovery Logistics of Distribution Systems: Co-Optimize Service Restoration with Repair Crew and Mobile Power Source Dispatch," IEEE Transactions on Smart Grid, 10(6): 6187-6202, Nov 2019. (2019-2021 IEEE Transactions on Smart Grid Top 5 Outstanding Papers Award)
  8. Shunbo Lei, Chen Chen, Hui Zhou, and Yunhe Hou, "Routing and Scheduling of Mobile Power Sources for Distribution System Resilience Enhancement," IEEE Transactions on Smart Grid, 10(5): 5650-5662, Sep 2019.
  9. Yang Liu, Shunbo Lei*, and Yunhe Hou, "Restoration of Power Distribution Systems with Multiple Data Centers as Critical Loads," IEEE Transactions on Smart Grid, 10(5): 5294-5307, Sep 2019. (*Corresponding author)
  10. Shunbo Lei, Jianhui Wang, Chen Chen, and Yunhe Hou, "Mobile Emergency Generator Pre-Positioning and Real-Time Allocation for Resilient Response to Natural Disasters," IEEE Transactions on Smart Grid, 9(3): 2030-2041, May 2018. (ESI Top 1% Highly Cited Paper)
  11. Shunbo Lei, Jianhui Wang, and Yunhe Hou, "Remote-Controlled Switch Allocation Enabling Prompt Restoration of Distribution Systems," IEEE Transactions on Power Systems, 33(3): 3129-3142, May 2018.
  12. Shunbo Lei, Yunhe Hou, Feng Qiu, and Jie Yan, "Identification of Critical Switches for Integrating Renewable Distributed Generation by Dynamic Network Reconfiguration," IEEE Transactions on Sustainable Energy, 9(1): 420-432, Jan 2018.
  13. Shunbo Lei, Yunhe Hou, Xi Wang, and Kai Liu, "Unit Commitment Incorporating Spatial Distribution Control of Air Pollutant Dispersion," IEEE Transactions on Industrial Informatics, 13(3): 995-1005, Jun 2017.
LEI, Shunbo

POSITION/TITLE

Research Scientist

EDUCATION BACKGROUND

Ph.D. (The University of Hong Kong)

B.Eng. (Huazhong University of Science and Technology)

RESEARCH FIELD

Power & energy systems, grid-interactive efficient buildings, infrastructure resilience, optimization, machine/reinforcement learning.

ACADEMIC AREA

Computer Engineering, Electrical Engineering, New Energy Science and Engineering

PERSONAL WEBSITE

http://leishunbo.info/

EMAIL

leishunbo@cuhk.edu.cn

BIOGRAPHY

Shunbo received his B.Eng. degree from Huazhong University of Science and Technology in 2013, and Ph.D. degree from The University of Hong Kong (HKU) in 2017. He was a visiting scholar with Argonne National Laboratory from 2015 to 2017, a postdoctoral researcher with HKU from 2017 to 2019, and a research fellow with the University of Michigan (UM)-Ann Arbor from 2019 to 2021. He is currently an assistant professor at The Chinese University of Hong Kong, Shenzhen. His research interests lie broadly in power and energy, optimization and learning.

Shunbo is currently serving as an editorial board member for IEEE Transactions on Smart Grid, IEEE Power Engineering Letters, Energy Reports, and Protection and Control of Modern Power Systems, the secretary for IEEE PES Loads Subcommittee, and the chair for IEEE PES Task Force on FlexGEB to Enhance Electric Service Resilience. He has served as a TPC member for conferences including IEEE SmartGridComm and IET RPG. He has also (co-)chaired panel sessions at events including IEEE PES GM and IEEE iSPEC.

Shunbo received the IEEE Transactions on Smart Grid Top 5 Outstanding Papers Award (2019-2021), the IEEE Transactions on Smart Grid Best Reviewer Award (2018, 2019 and 2021), and the Netherlands’ 4TU Centre for Resilience Engineering - Young Resilience Fellowship (2021).

ACADEMIC PUBLICATIONS

For the full list of Shunbo’s publications, please visit his Google Scholar profile: https://scholar.google.be/citations?user=ltUvPPkAAAAJ&hl=en

Selected Publications (until May 2022):

  1. Shunbo Lei, David Pozo, Ming-Hao Wang, Qifeng Li, Yupeng Li, and Chaoyi Peng, "Power Economic Dispatch against Extreme Weather Conditions: The Price of Resilience," Renewable and Sustainable Energy Reviews, 157: 111994, Apr. 2022.
  2. Aditya Keskar*, Shunbo Lei*, Taylor Webb, Sarah Nagy, Ian A. Hiskens, Johanna L. Mathieu, and Jeremiah X. Johnson, "Assessing the Performance of Global Thermostat Adjustment in Commercial Buildings for Load Shifting Demand Response," Environmental Research: Infrastructure and Sustainability, 2: 015003, Mar. 2022. (*Co-first and co-corresponding authors)
  3. Shunbo Lei, Johanna L. Mathieu, and Rishee K. Jain, "Performance of Existing Models in Baselining Demand Response from Commercial Building HVAC Fans," ASME Journal of Engineering for Sustainable Buildings and Cities, 2(2): 021002, May 2021.
  4. Rong-Peng Liu, Shunbo Lei*, Chaoyi Peng, Wei Sun, and Yunhe Hou, "Data-based Resilience Enhancement Strategies for Electric-Gas Systems against Sequential Extreme Weather Events," IEEE Transactions on Smart Grid, 11(6): 5383-5395, Nov 2020. (*Corresponding author)
  5. Shunbo Lei, Chen Chen, Yue Song, and Yunhe Hou, "Radiality Constraints for Resilient Reconfiguration of Distribution Systems: Formulation and Application to Microgrid Formation," IEEE Transactions on Smart Grid, 11(5): 3944-3956, Sep 2020.
  6. Shunbo Lei, David Hong, Johanna L. Mathieu, and Ian A. Hiskens, "Baseline Estimation of Commercial Building HVAC Fan Power Using Tensor Completion," in Proceedings of the Power Systems Computation Conference (PSCC), Porto, Portugal, Jun 2020.
  7. Shunbo Lei, Chen Chen, Yupeng Li, and Yunhe Hou, "Resilient Disaster Recovery Logistics of Distribution Systems: Co-Optimize Service Restoration with Repair Crew and Mobile Power Source Dispatch," IEEE Transactions on Smart Grid, 10(6): 6187-6202, Nov 2019. (2019-2021 IEEE Transactions on Smart Grid Top 5 Outstanding Papers Award)
  8. Shunbo Lei, Chen Chen, Hui Zhou, and Yunhe Hou, "Routing and Scheduling of Mobile Power Sources for Distribution System Resilience Enhancement," IEEE Transactions on Smart Grid, 10(5): 5650-5662, Sep 2019.
  9. Yang Liu, Shunbo Lei*, and Yunhe Hou, "Restoration of Power Distribution Systems with Multiple Data Centers as Critical Loads," IEEE Transactions on Smart Grid, 10(5): 5294-5307, Sep 2019. (*Corresponding author)
  10. Shunbo Lei, Jianhui Wang, Chen Chen, and Yunhe Hou, "Mobile Emergency Generator Pre-Positioning and Real-Time Allocation for Resilient Response to Natural Disasters," IEEE Transactions on Smart Grid, 9(3): 2030-2041, May 2018. (ESI Top 1% Highly Cited Paper)
  11. Shunbo Lei, Jianhui Wang, and Yunhe Hou, "Remote-Controlled Switch Allocation Enabling Prompt Restoration of Distribution Systems," IEEE Transactions on Power Systems, 33(3): 3129-3142, May 2018.
  12. Shunbo Lei, Yunhe Hou, Feng Qiu, and Jie Yan, "Identification of Critical Switches for Integrating Renewable Distributed Generation by Dynamic Network Reconfiguration," IEEE Transactions on Sustainable Energy, 9(1): 420-432, Jan 2018.
  13. Shunbo Lei, Yunhe Hou, Xi Wang, and Kai Liu, "Unit Commitment Incorporating Spatial Distribution Control of Air Pollutant Dispersion," IEEE Transactions on Industrial Informatics, 13(3): 995-1005, Jun 2017.
LEI, Shunbo

POSITION/TITLE

Research Scientist

EDUCATION BACKGROUND

Ph.D. (The University of Hong Kong)

B.Eng. (Huazhong University of Science and Technology)

RESEARCH FIELD

Power & energy systems, grid-interactive efficient buildings, infrastructure resilience, optimization, machine/reinforcement learning.

ACADEMIC AREA

Computer Engineering, Electrical Engineering, New Energy Science and Engineering

PERSONAL WEBSITE

http://leishunbo.info/

EMAIL

leishunbo@cuhk.edu.cn

BIOGRAPHY

Shunbo received his B.Eng. degree from Huazhong University of Science and Technology in 2013, and Ph.D. degree from The University of Hong Kong (HKU) in 2017. He was a visiting scholar with Argonne National Laboratory from 2015 to 2017, a postdoctoral researcher with HKU from 2017 to 2019, and a research fellow with the University of Michigan (UM)-Ann Arbor from 2019 to 2021. He is currently an assistant professor at The Chinese University of Hong Kong, Shenzhen. His research interests lie broadly in power and energy, optimization and learning.

Shunbo is currently serving as an editorial board member for IEEE Transactions on Smart Grid, IEEE Power Engineering Letters, Energy Reports, and Protection and Control of Modern Power Systems, the secretary for IEEE PES Loads Subcommittee, and the chair for IEEE PES Task Force on FlexGEB to Enhance Electric Service Resilience. He has served as a TPC member for conferences including IEEE SmartGridComm and IET RPG. He has also (co-)chaired panel sessions at events including IEEE PES GM and IEEE iSPEC.

Shunbo received the IEEE Transactions on Smart Grid Top 5 Outstanding Papers Award (2019-2021), the IEEE Transactions on Smart Grid Best Reviewer Award (2018, 2019 and 2021), and the Netherlands’ 4TU Centre for Resilience Engineering - Young Resilience Fellowship (2021).

ACADEMIC PUBLICATIONS

For the full list of Shunbo’s publications, please visit his Google Scholar profile: https://scholar.google.be/citations?user=ltUvPPkAAAAJ&hl=en

Selected Publications (until May 2022):

  1. Shunbo Lei, David Pozo, Ming-Hao Wang, Qifeng Li, Yupeng Li, and Chaoyi Peng, "Power Economic Dispatch against Extreme Weather Conditions: The Price of Resilience," Renewable and Sustainable Energy Reviews, 157: 111994, Apr. 2022.
  2. Aditya Keskar*, Shunbo Lei*, Taylor Webb, Sarah Nagy, Ian A. Hiskens, Johanna L. Mathieu, and Jeremiah X. Johnson, "Assessing the Performance of Global Thermostat Adjustment in Commercial Buildings for Load Shifting Demand Response," Environmental Research: Infrastructure and Sustainability, 2: 015003, Mar. 2022. (*Co-first and co-corresponding authors)
  3. Shunbo Lei, Johanna L. Mathieu, and Rishee K. Jain, "Performance of Existing Models in Baselining Demand Response from Commercial Building HVAC Fans," ASME Journal of Engineering for Sustainable Buildings and Cities, 2(2): 021002, May 2021.
  4. Rong-Peng Liu, Shunbo Lei*, Chaoyi Peng, Wei Sun, and Yunhe Hou, "Data-based Resilience Enhancement Strategies for Electric-Gas Systems against Sequential Extreme Weather Events," IEEE Transactions on Smart Grid, 11(6): 5383-5395, Nov 2020. (*Corresponding author)
  5. Shunbo Lei, Chen Chen, Yue Song, and Yunhe Hou, "Radiality Constraints for Resilient Reconfiguration of Distribution Systems: Formulation and Application to Microgrid Formation," IEEE Transactions on Smart Grid, 11(5): 3944-3956, Sep 2020.
  6. Shunbo Lei, David Hong, Johanna L. Mathieu, and Ian A. Hiskens, "Baseline Estimation of Commercial Building HVAC Fan Power Using Tensor Completion," in Proceedings of the Power Systems Computation Conference (PSCC), Porto, Portugal, Jun 2020.
  7. Shunbo Lei, Chen Chen, Yupeng Li, and Yunhe Hou, "Resilient Disaster Recovery Logistics of Distribution Systems: Co-Optimize Service Restoration with Repair Crew and Mobile Power Source Dispatch," IEEE Transactions on Smart Grid, 10(6): 6187-6202, Nov 2019. (2019-2021 IEEE Transactions on Smart Grid Top 5 Outstanding Papers Award)
  8. Shunbo Lei, Chen Chen, Hui Zhou, and Yunhe Hou, "Routing and Scheduling of Mobile Power Sources for Distribution System Resilience Enhancement," IEEE Transactions on Smart Grid, 10(5): 5650-5662, Sep 2019.
  9. Yang Liu, Shunbo Lei*, and Yunhe Hou, "Restoration of Power Distribution Systems with Multiple Data Centers as Critical Loads," IEEE Transactions on Smart Grid, 10(5): 5294-5307, Sep 2019. (*Corresponding author)
  10. Shunbo Lei, Jianhui Wang, Chen Chen, and Yunhe Hou, "Mobile Emergency Generator Pre-Positioning and Real-Time Allocation for Resilient Response to Natural Disasters," IEEE Transactions on Smart Grid, 9(3): 2030-2041, May 2018. (ESI Top 1% Highly Cited Paper)
  11. Shunbo Lei, Jianhui Wang, and Yunhe Hou, "Remote-Controlled Switch Allocation Enabling Prompt Restoration of Distribution Systems," IEEE Transactions on Power Systems, 33(3): 3129-3142, May 2018.
  12. Shunbo Lei, Yunhe Hou, Feng Qiu, and Jie Yan, "Identification of Critical Switches for Integrating Renewable Distributed Generation by Dynamic Network Reconfiguration," IEEE Transactions on Sustainable Energy, 9(1): 420-432, Jan 2018.
  13. Shunbo Lei, Yunhe Hou, Xi Wang, and Kai Liu, "Unit Commitment Incorporating Spatial Distribution Control of Air Pollutant Dispersion," IEEE Transactions on Industrial Informatics, 13(3): 995-1005, Jun 2017.
LEI, Shunbo

POSITION/TITLE

Research Scientist

EDUCATION BACKGROUND

Ph.D. (The University of Hong Kong)

B.Eng. (Huazhong University of Science and Technology)

RESEARCH FIELD

Power & energy systems, grid-interactive efficient buildings, infrastructure resilience, optimization, machine/reinforcement learning.

ACADEMIC AREA

Computer Engineering, Electrical Engineering, New Energy Science and Engineering

PERSONAL WEBSITE

http://leishunbo.info/

EMAIL

leishunbo@cuhk.edu.cn

BIOGRAPHY

Shunbo received his B.Eng. degree from Huazhong University of Science and Technology in 2013, and Ph.D. degree from The University of Hong Kong (HKU) in 2017. He was a visiting scholar with Argonne National Laboratory from 2015 to 2017, a postdoctoral researcher with HKU from 2017 to 2019, and a research fellow with the University of Michigan (UM)-Ann Arbor from 2019 to 2021. He is currently an assistant professor at The Chinese University of Hong Kong, Shenzhen. His research interests lie broadly in power and energy, optimization and learning.

Shunbo is currently serving as an editorial board member for IEEE Transactions on Smart Grid, IEEE Power Engineering Letters, Energy Reports, and Protection and Control of Modern Power Systems, the secretary for IEEE PES Loads Subcommittee, and the chair for IEEE PES Task Force on FlexGEB to Enhance Electric Service Resilience. He has served as a TPC member for conferences including IEEE SmartGridComm and IET RPG. He has also (co-)chaired panel sessions at events including IEEE PES GM and IEEE iSPEC.

Shunbo received the IEEE Transactions on Smart Grid Top 5 Outstanding Papers Award (2019-2021), the IEEE Transactions on Smart Grid Best Reviewer Award (2018, 2019 and 2021), and the Netherlands’ 4TU Centre for Resilience Engineering - Young Resilience Fellowship (2021).

ACADEMIC PUBLICATIONS

For the full list of Shunbo’s publications, please visit his Google Scholar profile: https://scholar.google.be/citations?user=ltUvPPkAAAAJ&hl=en

Selected Publications (until May 2022):

  1. Shunbo Lei, David Pozo, Ming-Hao Wang, Qifeng Li, Yupeng Li, and Chaoyi Peng, "Power Economic Dispatch against Extreme Weather Conditions: The Price of Resilience," Renewable and Sustainable Energy Reviews, 157: 111994, Apr. 2022.
  2. Aditya Keskar*, Shunbo Lei*, Taylor Webb, Sarah Nagy, Ian A. Hiskens, Johanna L. Mathieu, and Jeremiah X. Johnson, "Assessing the Performance of Global Thermostat Adjustment in Commercial Buildings for Load Shifting Demand Response," Environmental Research: Infrastructure and Sustainability, 2: 015003, Mar. 2022. (*Co-first and co-corresponding authors)
  3. Shunbo Lei, Johanna L. Mathieu, and Rishee K. Jain, "Performance of Existing Models in Baselining Demand Response from Commercial Building HVAC Fans," ASME Journal of Engineering for Sustainable Buildings and Cities, 2(2): 021002, May 2021.
  4. Rong-Peng Liu, Shunbo Lei*, Chaoyi Peng, Wei Sun, and Yunhe Hou, "Data-based Resilience Enhancement Strategies for Electric-Gas Systems against Sequential Extreme Weather Events," IEEE Transactions on Smart Grid, 11(6): 5383-5395, Nov 2020. (*Corresponding author)
  5. Shunbo Lei, Chen Chen, Yue Song, and Yunhe Hou, "Radiality Constraints for Resilient Reconfiguration of Distribution Systems: Formulation and Application to Microgrid Formation," IEEE Transactions on Smart Grid, 11(5): 3944-3956, Sep 2020.
  6. Shunbo Lei, David Hong, Johanna L. Mathieu, and Ian A. Hiskens, "Baseline Estimation of Commercial Building HVAC Fan Power Using Tensor Completion," in Proceedings of the Power Systems Computation Conference (PSCC), Porto, Portugal, Jun 2020.
  7. Shunbo Lei, Chen Chen, Yupeng Li, and Yunhe Hou, "Resilient Disaster Recovery Logistics of Distribution Systems: Co-Optimize Service Restoration with Repair Crew and Mobile Power Source Dispatch," IEEE Transactions on Smart Grid, 10(6): 6187-6202, Nov 2019. (2019-2021 IEEE Transactions on Smart Grid Top 5 Outstanding Papers Award)
  8. Shunbo Lei, Chen Chen, Hui Zhou, and Yunhe Hou, "Routing and Scheduling of Mobile Power Sources for Distribution System Resilience Enhancement," IEEE Transactions on Smart Grid, 10(5): 5650-5662, Sep 2019.
  9. Yang Liu, Shunbo Lei*, and Yunhe Hou, "Restoration of Power Distribution Systems with Multiple Data Centers as Critical Loads," IEEE Transactions on Smart Grid, 10(5): 5294-5307, Sep 2019. (*Corresponding author)
  10. Shunbo Lei, Jianhui Wang, Chen Chen, and Yunhe Hou, "Mobile Emergency Generator Pre-Positioning and Real-Time Allocation for Resilient Response to Natural Disasters," IEEE Transactions on Smart Grid, 9(3): 2030-2041, May 2018. (ESI Top 1% Highly Cited Paper)
  11. Shunbo Lei, Jianhui Wang, and Yunhe Hou, "Remote-Controlled Switch Allocation Enabling Prompt Restoration of Distribution Systems," IEEE Transactions on Power Systems, 33(3): 3129-3142, May 2018.
  12. Shunbo Lei, Yunhe Hou, Feng Qiu, and Jie Yan, "Identification of Critical Switches for Integrating Renewable Distributed Generation by Dynamic Network Reconfiguration," IEEE Transactions on Sustainable Energy, 9(1): 420-432, Jan 2018.
  13. Shunbo Lei, Yunhe Hou, Xi Wang, and Kai Liu, "Unit Commitment Incorporating Spatial Distribution Control of Air Pollutant Dispersion," IEEE Transactions on Industrial Informatics, 13(3): 995-1005, Jun 2017.