TITLE
Director of General Software and Technology, Shenzhen Research Institute of Big Data
Lab Director for General Optimization Solver Development Lab
Adjunct Professor of Practice, School of Data Science, CUHK(Shenzhen)
EDUCATION BACKGROUND
Ph.D. Operations Research and Computer Science, Mass Institute of Technology, 1995
M.S. Master of Engineering in Computer Science, McMaster University, 1991
B.S. Software Engineering in Computer Science, Peking University, 1990
RESEARCH FIELD
The Primal-dual Subproblem Column Generation Approach for the Pairing Optimizer, Airline Operations Recovery/Pricing and Revenue Management, Continuous Linear Programs, Algorithms for Manufacturing Systems, Applied Statistics, Combinatorial Optimization
xiaodongluo@cuhk.edu.cn
BIOGRAPHY
Professor Xiaodong Luo is the director of general software and technology at Shenzhen research institute of big data. He is also a professor of practice at the Chinese University of Hong Kong Shenzhen. He studied software engineering at Peking University with a bachelor’s degree and then graduated from McMaster University with a master’s degree, majoring in Computer Science. Professor Luo got his Ph.D. in Operations Research and Computer Science at Mass Institute of Technology.
Professor Luo has over twenty-five years of industry experience, with close to twenty years in the airline industry and six years in Supply Chain management. In crew scheduling, Professor Luo spearheaded the implementation of the primal-dual subproblem column generation approach for the pairing optimizer. For airline operations recovery, Professor Luo worked with Sabre recovery ops product team as well as 3-4 summer interns on improving the speed and solution quality of the Sabre recovery suite of products. Also, He was a part-time lecturer for the computer science undergraduate course “UML-Object Oriented Analysis and programming.”
He can model complex business problems, conduct algorithmic design and coding, perform product support as well as help customers adopting advanced decision support systems. He has made improvements to many optimization engines by speeding them up, making them more scalable, and enabling them to produce more robust, better-quality solutions. He has nurtured many academic connections, collaborating with many high-quality operational researchers, many of these collaborations bring advancement to the state of the art of the field. Over the years, he has directed more than a dozen successful summer intern projects and has helped many of his co-workers get off the ground with applied research. He has won quite a few awards, has written more than a dozen technical papers, and has given many technical talks.
ACADEMIC PUBLICATIONS
- “A GNN-Guided Predict-and-Search Framework for Mixed-Integer Linear Programming，” Qinyu Han, Linxin Yang, Qian Chen, Akang Wang, Ruoyu Sun, Xiaodong Luo，Proceedings of Machine Learning Research Conf. Paper (accepted)，2023
- “Aircraft Routing Recovery Optimization with Cruise Speed Control,” Haohao Liu, Zhouchun Huang, Xiaodong Luo, Yinxiao Hu, Jie Ding，Aeronautical Computing Technique, 2023/01/30
- “The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights,” Xiaodong Luo and many other co-authors, Proceedings of Machine Learning Research，2022/02/01
- “Variable Pricing: An Integrated Airline Pricing and Revenue Management Model,” Miju Ahn, Xiaodong Luo and Sergey Shebalov. Journal of Revenue & Pricing Management, April 2020.
- “An Iterative Cost-driven Copy Generation Approach for Aircraft Recovery Problem,” Zhouchun Huang, Xiaodong Luo, Xianfei Jin and Sureshan Karichery. Preprint, submitted for publication to Transportation Research, Part B, September 2019.
- “Joint forecasting for airline pricing and revenue management,” Kavitha Balaiyan, Rk Amit, Atul Kumar, Xiaodong Luo and Amit Agarwal. Journal of Revenue & Pricing Management, Volume 14(number 6), March 2019.
- “Airline Crew Augmentation: Decades of Improvements from Sabre,” Xiaodong Luo, Yogesh Dashora and Tina Shaw. INFORMS Journal on Applied Analytics, Vol. 45, No. 5, October 2015.
- “Iterative Methods for Large Markov Decision Problems,” Xiaodong Luo. Preprint, January 2015.
- “Efficient Implementation of Quasi- Maximum-Likelihood Detection Based on Semidefinite Relaxation,” Mikalai Kisialiou, Xiaodong Luo and Zhi-Quan Tom Luo. IEEE Transactions on Signal Processing, 57(12):4811-4822, December 2009.
- “An efficient quasi-maximum likelihood decoder for PSK signals,” and Zhi-Quan (Tom) Luo, Xiaodong Luo and Mikalai Kisialiou, Proceedings for 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing. (ICASSP '03), 6-10 April 2003.
- “A New Algorithm for State-Constrained Separated Continuous Linear Programs,” Xiaodong Luo and Dimitris Bertsimas. SIAM Journal on Control and Optimizations, Volume 37, Number 1, pp. 177-210, 1998.
- “Conditions for a Projection-Type Error Bound for the Linear Complementarity Problem to Be Global,” Paul Tseng and Xiaodong Luo. Linear Algebra and Its Applications, 253 (1-3) (1997) pp. 251-278.
- “Continuous linear programming: theory, algorithms and applications,” Xiao-Dong Luo. Ph.D. Thesis, Massachusetts Institute of Technology, Sloan School of Management, 1995.
- “Extension of Hoffman’s Error Bound to Polynomial Systems,” Zhi-Quan Luo and Xiaodong Luo. SIAM Journal on Optimization, Vol. 4, No. 2, pp. 383-392, May 1994.
- “Worst Case Complexity of Potential Reduction Algorithms for Linear Programming,” Dimitris Bertsimas and Xiaodong Luo. Mathematical Programming 77(2), January 1993.
- “An error analysis of the fast recursive least squares algorithms,” Xiaodong Luo and Shanzhen Qiao. Technical report no. 231, Comm. Res. Lab., McMaster University, Hamilton, Ontario, Canada, 1991
POSITION/TITLE
Research Scientist at Shenzhen Research Institute of Big Data
RESEARCH FIELD
Integer Programming, Global Optimization, Learning to Optimize, and Grid Optimization
EDUCATION BACKGROUND
Carnegie Mellon University, PhD in Process Systems Engineering
Tianjin University, Bachelor of Science in Chemical Engineering
Nankai University, Bachelor of Arts in Finance
wangakang@sribd.cn
PERSONAL WEBSITE
https://akangw.github.io/
HONORS AND AWARDS
First place in the primal track of NeurIPS 2021 ML4CO, NeurIPS, 2021
Second place in the 2022 RAS Problem Solving Competition, INFORMS Railway Applications, 2022
BIOGRAPHY
Dr. Akang Wang joined Shenzhen Research Institute of Big Data in 2021 and is currently working as a research scientist. In 2020, he received his PhD degree in the area of Process Systems Engineering at Carnegie Mellon University. His current research interests include integer programming, global optimization, learning to optimize, and grid optimization. His research is supported by NSFC, GuangDong Basic and Applied Basic Research Foundation, Shenzhen Science and Technology Program and National Key R&D Program of China. Dr. Wang has published journal/conference papers in Journal of Global Optimization, European Journal of Operational Research, ICLR, etc. He won the first place in the primal track of NeurIPS 2021 ML2CO competition and the second place in the 2022 RAS Problem Solving Competition of INFORMS. Besides, he served as reviewers for journals such as Integer Programming and Combinatorial Optimization, European Journal of Operational Research, Networks.
POSITION/TITLE
Research Scientist at Shenzhen Research Institute of Big Data
RESEARCH FIELD
Integer Programming, Global Optimization, Learning to Optimize, and Grid Optimization
EDUCATION BACKGROUND
Carnegie Mellon University, PhD in Process Systems Engineering
Tianjin University, Bachelor of Science in Chemical Engineering
Nankai University, Bachelor of Arts in Finance
wangakang@sribd.cn
PERSONAL WEBSITE
https://akangw.github.io/
HONORS AND AWARDS
First place in the primal track of NeurIPS 2021 ML4CO, NeurIPS, 2021
Second place in the 2022 RAS Problem Solving Competition, INFORMS Railway Applications, 2022
BIOGRAPHY
Dr. Akang Wang joined Shenzhen Research Institute of Big Data in 2021 and is currently working as a research scientist. In 2020, he received his PhD degree in the area of Process Systems Engineering at Carnegie Mellon University. His current research interests include integer programming, global optimization, learning to optimize, and grid optimization. His research is supported by NSFC, GuangDong Basic and Applied Basic Research Foundation, Shenzhen Science and Technology Program and National Key R&D Program of China. Dr. Wang has published journal/conference papers in Journal of Global Optimization, European Journal of Operational Research, ICLR, etc. He won the first place in the primal track of NeurIPS 2021 ML2CO competition and the second place in the 2022 RAS Problem Solving Competition of INFORMS. Besides, he served as reviewers for journals such as Integer Programming and Combinatorial Optimization, European Journal of Operational Research, Networks.
POSITION/TITLE
Senior Engineer
RESEARCH FIELD
The development and application of optimization solver
wanglongfei@sribd.cn
EDUCATION BACKGROUND
Master in Management Science and Engineering, Peking University
Bachelor in Engineering, Peking University
BIOGRAPHY
Longfei Wang received the master degree from Peking University. His major research direction is system optimization and simulation. Before that, he received bachelor degree in Engineering and a double bachelor degree in Economics from Peking university.
ACADEMIC PUBLICATIONS
- Wang L, Song J, Shi L. Dynamic emergency logistics planning: models and heuristic algorithm[J]. Optimization Letters, 2015, 9(8): 1533-1552.
- Shi Z, Wang L, Liu P, et al. Minimizing completion time for order scheduling: Formulation and heuristic algorithm[J]. IEEE Transactions on Automation Science and Engineering, 2015, 14(4): 1558-1569.
POSITION/TITLE
Senior Engineer
RESEARCH FIELD
The development and application of optimization solver
wanglongfei@sribd.cn
EDUCATION BACKGROUND
Master in Management Science and Engineering, Peking University
Bachelor in Engineering, Peking University
BIOGRAPHY
Longfei Wang received the master degree from Peking University. His major research direction is system optimization and simulation. Before that, he received bachelor degree in Engineering and a double bachelor degree in Economics from Peking university.
ACADEMIC PUBLICATIONS
- Wang L, Song J, Shi L. Dynamic emergency logistics planning: models and heuristic algorithm[J]. Optimization Letters, 2015, 9(8): 1533-1552.
- Shi Z, Wang L, Liu P, et al. Minimizing completion time for order scheduling: Formulation and heuristic algorithm[J]. IEEE Transactions on Automation Science and Engineering, 2015, 14(4): 1558-1569.
POSITION/TITLE
Senior Engineer
RESEARCH FIELD
Integer Programming, Production Scheduling, Data-driven Optimization
wangyuan@cuhk.edu.cn
EDUCATION BACKGROUND
Northeastern University, PhD in Systems Engineering.
Northeastern University, Bachelor in Control Theory and Engineering.
BIOGRAPHY
Dr. Wang Yuan joined the Shenzhen Big Data Research Institute in November 2022. He received his bachelor’s and PhD degrees in System Engineering from Northeastern University in 2013 and 2019 respectively. In 2019, he worked as an algorithm engineer (intern) at Huawei Noah's Ark Lab, where he was mainly responsible for algorithm research and development for Huawei's multi-factory production planning problems. From 2019 to 2022, he worked as a postdoctoral researcher at The Chinese University of Hong Kong (Shenzhen), focusing on data-driven optimization methods to solve multi-airport 4D trajectory planning problems in air traffic flow management.
PUBLICATIONS
[1] Wang Y, Luo X, Zhang F, et al. GPU-based model predictive control for continuous casting spray cooling control system using particle swarm optimization[J]. Control Engineering Practice, 2019, 84: 349-364.
[2] Wang Y, Luo X, Yu Y, et al. Evaluation of heat transfer coefficients in continuous casting under large disturbance by weighted least squares Levenberg-Marquardt method[J]. Applied Thermal Engineering, 2017, 111: 989-996.
[3] Cui H, Luo X, Wang Y. Scheduling of steelmaking-continuous casting process using deflected surrogate Lagrangian relaxation approach and DC algorithm[J]. Computers & Industrial Engineering, 2020, 140: 106271.
[4] Cui H, Luo X, Wang Y. Scheduling of steelmaking-continuous casting process with different processing routes using effective surrogate Lagrangian relaxation approach and improved concave–convex procedure[J]. International Journal of Production Research, 2021: 1-26.
[5] Yu Y, Luo X, Wang Y, et al. Estimation of boundary condition of two-dimensional nonlinear PDE with application to continuous casting[J]. Computers & Mathematics with Applications, 2020, 80(12): 3082-3097.
[6] Zhang Z, Wang Y, Liu C. Multihoming effect on the two-sided platform of second-hand cars[J]. Computers & Industrial Engineering, 2023: 109160.
POSITION/TITLE
Senior Engineer
RESEARCH FIELD
Integer Programming, Production Scheduling, Data-driven Optimization
wangyuan@cuhk.edu.cn
EDUCATION BACKGROUND
Northeastern University, PhD in Systems Engineering.
Northeastern University, Bachelor in Control Theory and Engineering.
BIOGRAPHY
Dr. Wang Yuan joined the Shenzhen Big Data Research Institute in November 2022. He received his bachelor’s and PhD degrees in System Engineering from Northeastern University in 2013 and 2019 respectively. In 2019, he worked as an algorithm engineer (intern) at Huawei Noah's Ark Lab, where he was mainly responsible for algorithm research and development for Huawei's multi-factory production planning problems. From 2019 to 2022, he worked as a postdoctoral researcher at The Chinese University of Hong Kong (Shenzhen), focusing on data-driven optimization methods to solve multi-airport 4D trajectory planning problems in air traffic flow management.
PUBLICATIONS
[1] Wang Y, Luo X, Zhang F, et al. GPU-based model predictive control for continuous casting spray cooling control system using particle swarm optimization[J]. Control Engineering Practice, 2019, 84: 349-364.
[2] Wang Y, Luo X, Yu Y, et al. Evaluation of heat transfer coefficients in continuous casting under large disturbance by weighted least squares Levenberg-Marquardt method[J]. Applied Thermal Engineering, 2017, 111: 989-996.
[3] Cui H, Luo X, Wang Y. Scheduling of steelmaking-continuous casting process using deflected surrogate Lagrangian relaxation approach and DC algorithm[J]. Computers & Industrial Engineering, 2020, 140: 106271.
[4] Cui H, Luo X, Wang Y. Scheduling of steelmaking-continuous casting process with different processing routes using effective surrogate Lagrangian relaxation approach and improved concave–convex procedure[J]. International Journal of Production Research, 2021: 1-26.
[5] Yu Y, Luo X, Wang Y, et al. Estimation of boundary condition of two-dimensional nonlinear PDE with application to continuous casting[J]. Computers & Mathematics with Applications, 2020, 80(12): 3082-3097.
[6] Zhang Z, Wang Y, Liu C. Multihoming effect on the two-sided platform of second-hand cars[J]. Computers & Industrial Engineering, 2023: 109160.
POSITION/TITLE
Engineer
RESEARCH FIELD
The development and application of optimization solver, Reinforcement Learning
chenhongjun@sribd.cn
EDUCATION BACKGROUND
East China Normal University, Master's degree in Computer Science and Technology.
East China Normal University, Bachelor's degree in Software Engineering."
BIOGRAPHY
Chen Hongjun received his bachelor’s and master’s degrees in engineering from East China Normal University in 2019 and 2022 respectively. He joined the Shenzhen Big Data Research Institute in July 2022 as an engineer, with research interests in operations research, reinforcement learning, and exploring optimization methods combined with machine learning.
PUBLICATIONS
Li W, Chen H, Jin B, et al. Multi-Agent Path Finding with Prioritized Communication Learning[C]. IEEE International Conf. Robotics and Automation (ICRA), 2022: 10695-10701
POSITION/TITLE
Engineer
RESEARCH FIELD
The development and application of optimization solver, Reinforcement Learning
chenhongjun@sribd.cn
EDUCATION BACKGROUND
East China Normal University, Master's degree in Computer Science and Technology.
East China Normal University, Bachelor's degree in Software Engineering."
BIOGRAPHY
Chen Hongjun received his bachelor’s and master’s degrees in engineering from East China Normal University in 2019 and 2022 respectively. He joined the Shenzhen Big Data Research Institute in July 2022 as an engineer, with research interests in operations research, reinforcement learning, and exploring optimization methods combined with machine learning.
PUBLICATIONS
Li W, Chen H, Jin B, et al. Multi-Agent Path Finding with Prioritized Communication Learning[C]. IEEE International Conf. Robotics and Automation (ICRA), 2022: 10695-10701
POSITION/TITLE
Senior Engineer
RESEARCH FIELD
Linear Programming, Vehicle Routing Problem, Deep Reinforcement Learning
weijiangwen@sribd.cn
EDUCATION BACKGROUND
Shanghai University, Bachelor in Information and Computing Science
HONORS AND AWARDS
THE 2021 FRANZ EDELMAN FINALIST AWARD
BIOGRAPHY
Jiangwen Wei joined the Shenzhen Research Institute of Big Data in September 2023 and currently working as a senior engineer. In 2006, He received his bachelor degree in Information and Computing Science at Shanghai University. His current research interests include Linear Programming, Vehicle Routing Problem and Deep Reinforcement Learning. He and his team received THE 2021 FRANZ EDELMAN FINALIST AWARD by the work "Alibaba VRP Algorithms Solved Its Hour-Level Delivery Problem". He was awarded one invention patent for "Data Processing Method, Data Processing Device, and Server."
POSITION/TITLE
SRIBD Engineer of Optimization Solver Development
huilin.zang@sribd.cn
POSITION/TITLE
SRIBD Engineer of Optimization Solver Development
huilin.zang@sribd.cn
