PUBLICATIONS

Journal Papers:

A review of data science and artificial intelligence applications in air transportation systems
Li, L.
Artificial Intelligence for Transportation, 2025, 2, 100023.
https://doi.org/10.1016/j.ait.2025.100023 [PDF]
Evaluation of urban wind effects on flight path planning of delivery drones using computational fluid dynamics simulations
Wang, J., He, X., Jiang, S., Chan, P.W., Li, C., Ou, J., Duan, P. & Li, L.
Physics of Fluids, 2025, 37(8).
https://doi.org/10.1063/5.0281373
Path pool based transformer model in reinforcement framework for dynamic urban drone delivery problem
Xiang, C., Mo, Y., Liu, W., Wu, Z., & Li, L.
Transportation Research Part C: Emerging Technologies, 2025, 177, 105165.
https://doi.org/10.1016/j.trc.2025.105165 [PDF]
A learning-based interacting multiple model filter for trajectory prediction of small multirotor drones considering differential sequences
Tang, R., Ng, K. K. H., Li, L. & Yang, Z.
Transportation Research Part C: Emerging Technologies., 2025, 174, 105115.
https://doi.org/10.1016/j.trc.2025.105115 [PDF]
A hierarchical scheme for dynamic monitoring of multi-scale multi-mode systems
Wang, J., Li, L. & Joe Qin, S.
Computers & Chemical Engineering., 2025, 198, 109107. https://doi.org/10.1016/j.compchemeng.2025.109107 [PDF]
Forecasting short-term passenger flow via CBGC-SCI: an in-depth comparative study on Shenzhen Metro
He, Y., Hong, W., Li, L., Zhang, J., Qin, J. & Luo, Q.
Machine Learning., 2025, 114, 5.
https://doi.org/10.1007/s10994-024-06711-y [PDF]
Air Corridor Planning for Urban Drone Delivery: Complexity Analysis and Comparison via Multi-Commodity Network Flow and Graph Search
He, X., Li, L., Mo, Y., Sun, Z. & Qin, S. J.
Transportation Research Part E: Logistics and Transportation Review., 2025, 193, 103859.
https://doi.org/10.1016/j.tre.2024.103859 [PDF]
Remaining useful life modelling with an escalator health condition analytic system
Zwetsloot, I. M., Lin, Y., Qiu, J., Li, L., Lee, W. K. F., Yeung, E. Y. S., … & Wong, C. C. L.
Quality and Reliability Engineering International, 2025.
https://doi.org/10.1002/qre.70049 [PDF]
Towards dynamic flight separation in final approach: A hybrid attention-based deep learning framework for long-term spatiotemporal wake vortex prediction
Chu, N., Ng, K. K. H., Zhu, X., Liu, Y., Li, L. & Hon, K. K.
Transportation Research Part C: Emerging Technologies., 2024, 169, 104876. https://doi.org/10.1016/j.trc.2024.104876 [PDF]
In-Depth Insights into the Application of Recurrent Neural Networks (RNNs) in Traffic Prediction: A Comprehensive Review
He, Y., Huang, P., Hong, W., Luo, Q., Li, L. & Tsui, K.-L.
Algorithms., 2024, 17, 398.
https://doi.org/10.3390/a17090398 [PDF]
Identification of no-fly zones for delivery drone path planning in various urban wind environments
Jiang, S., Wang, J., Li, C., Ou, J., Duan, P. & Li, L.
Physics of Fluids., 2024, 36, 085166.
https://doi.org/10.1063/5.0221281 [PDF]
Physically Interpretable Wavelet-Guided Networks With Dynamic Frequency Decomposition for Machine Intelligence Fault Prediction
Wang, H., Li, Y.-F., Men, T. & Li, L.
IEEE Transactions on Systems, Man, and Cybernetics: Systems., 2024, 54, p. 4863-4875. https://doi.org/10.1109/TSMC.2024.3389068 [PDF]
A distributed route network planning method with congestion pricing for drone delivery services in cities
He, X., Li, L., Mo, Y., Huang, J. & Qin, S. J.
Transportation Research Part C: Emerging Technologies., 2024, 160, 104536. https://doi.org/10.1016/j.trc.2024.104536 [PDF]
Fuel consumption prediction for pre-departure flights using attention-based multi-modal fusion
Lin, Y., Guo, D., Wu, Y., Li, L., Wu, E. Q. & Ge, W.
Information Fusion., 2024, 101, 101983.
https://doi.org/10.1016/j.inffus.2023.101983 [PDF]
Knowledge-informed wheel wear prediction method for high-speed train using multisource signal data
Chen, C., Zhu, F., Xu, Z., Xie, Q., Lo, S. M., Tsui, K. L. & Li, L.
IEEE Transactions on Instrumentation and Measurement., 2024, 73, 3522912. https://doi.org/10.1109/TIM.2024.3413151 [PDF]
Predicting aircraft trajectory uncertainties for terminal airspace design evaluation
Zhu, X., Hong, N., He, F., Lin, Y., Li, L. & Fu, X.
Journal of Air Transport Management., 2023, 113, 102473. https://doi.org/10.1016/j.jairtraman.2023.102473 [PDF]
Profile Abstract: An Optimization-based Subset Selection and Summarization Method for Profile Data Mining
Zhu, F., Feng, J., Xie, M., Li, L., Lei, J. & Lee, J.
IEEE Transactions on Industrial Informatics., 2023, 19, p. 9337-9348. https://doi.org/10.1109/TII.2022.3227642 [PDF]
Heat and park attendance: Evidence from “small data” and “big data” in Hong Kong
Hao, T., Chang, H., Liang, S., Jones, P., Chan, P. W., Li, L. & Huang, J.
Building and Environment., 2023, 234, 110123. https://doi.org/10.1016/j.buildenv.2023.110123 [PDF]
Prediction of estimated time of arrival for multi-airport systems via “Bubble” mechanism
Wang, L., Mao, J., Li, L., Li, X. & Tu, Y.
Transportation Research Part C: Emerging Technologies., 2023, 149, 104065. https://doi.org/10.1016/j.trc.2023.104065 [PDF]
A machine learning-enhanced design optimizer for urban cooling
Hao, T., Huang, J., He, X., Li, L. & Jones, P.
Indoor and Built Environment., 2023, 32, p. 355-374. https://doi.org/10.1177/1420326X221112857 [PDF]
Cross-chamber Data Transferability Evaluation for Fault Detection and Classification in Semiconductor Manufacturing
Zhu, F., Jia, X., Li, W., Xie, M., Li, L. & Lee, J.
IEEE Transactions on Semiconductor Manufacturing., 2023, 36, p. 68-77. https://doi.org/10.1109/TSM.2022.3222475 [PDF]
Re-examining Jane Jacobs’ Doctrine Using New Urban Data in Hong Kong
Huang, J., Cui, Y., Li, L., Guo, M., Ho, H. C., Lu, Y. & Webster, C.
Environment and Planning B: Urban Analytics and City Science., 2023, 50, p. 76-93. https://doi.org/10.1177/23998083221106186 [PDF]
Editorial: Machine Learning in Social Complex Systems
Yan, W., Jiang, Z.-Q., Li, L., Hisano, R. & Li, J.
Frontiers in Physics., 2023, 11, 1199879.
https://doi.org/10.3389/fphy.2023.1199879 [PDF]
A Simulation Study of Risk-Aware Path Planning in Mitigating the Third-Party Risk of a Commercial UAS Operation in an Urban Area
He, X., Jiang, C., Li, L. & Blom, H.
Aerospace., 2022, 9, 682.
https://doi.org/10.3390/aerospace9110682 [PDF]
Distribution Prediction of Strategic Flight Delays via Machine Learning Methods
Wang, Z., Liao, C., Hang, X., Li, L., Delahaye, D. & Hansen, M.
Sustainability (Switzerland)., 2022, 14, 15180.
https://doi.org/10.3390/su142215180 [PDF]
A City Is Not a Tree: A Multi-City Study on Street Network and Urban Life
Huang, J., Cui, Y., Chang, H., Obracht-Prondzyńska, H., Kamrowska-Zaluska, D. & Li, L.
Landscape and Urban Planning., 2022, 226, 104469. https://doi.org/10.1016/j.landurbplan.2022.104469 [PDF]
A Route Network Planning Method for Urban Air Delivery
He, X., He, F., Li, L., Zhang, L. & Xiao, G.
Transportation Research Part E: Logistics and Transportation Review., 2022, 166, 102872. https://doi.org/10.1016/j.tre.2022.102872 [PDF]
Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow
He, Y., Li, L., Zhu, X. & Tsui, K. L.
IEEE Transactions on Intelligent Transportation Systems., 2022, 23, p. 18155-18174. https://doi.org/10.1109/TITS.2022.3150600 [PDF]
Early-Stage Lifetime Prediction for Lithium-Ion Batteries: A Deep Learning Framework Jointly Considering Machine-Learned and Handcrafted Data Features
Fei, Z., Zhang, Z., Yang, F., Tsui, K.-L. & Li, L.
Journal of Energy Storage., 2022, 52, 104936.
https://doi.org/10.1016/j.est.2022.104936 [PDF]
A Queuing Network Model of a Multi-Airport System Based on Point-Wise Stationary Approximation
Zhao, X., Wang, Y., Li, L. & Delahaye, D.
Aerospace., 2022, 9, 390.
https://doi.org/10.3390/aerospace9070390 [PDF]
Warranty Reserve Management: Demand Learning and Funds Pooling
Wang, X.-L., Zhong, Y., Li, L., Xie, W. & Ye, Z.-S.
Manufacturing & Service Operations Management., 2022, 24, p. 2221-2239. https://doi.org/10.1287/msom.2022.1086 [PDF]
Short-Term Nationwide Airport Throughput Prediction With Graph Attention Recurrent Neural Network
Zhu, X., Lin, Y., He, Y., Tsui, K.-L., Chan, P. W. & Li, L.
Frontiers in Artificial Intelligence., 2022, 5, 884485.
https://doi.org/10.3389/frai.2022.884485 [PDF]
How Do New Transit Stations Affect People’s Sentiment and Activity? A Case Study Based on Social Media Data in Hong Kong
Chang, H., Huang, J., Yao, W., Zhao, W. & Li, L.
Transport Policy., 2022, 120, p. 139-155.
https://doi.org/10.1016/j.tranpol.2022.03.011 [PDF]
Tracking Traffic Congestion and Accidents Using Social Media Data: A Case Study of Shanghai
Chang, H., Li, L., Huang, J., Zhang, Q. & Chin, K. S.
Accident Analysis and Prevention., 2022, 169, 106618. https://doi.org/10.1016/j.aap.2022.106618 [PDF]
An Overview and General Framework for Spatiotemporal Modeling and Applications in Transportation and Public Health
Li, L., Tsui, K.-L. & Zhao, Y.
Artificial Intelligence, Big Data and Data Science in Statistics: Challenges and Solutions in Environmetrics, the Natural Sciences and Technology., 2022, p. 195-226. https://doi.org/10.1007/978-3-031-07155-3_8 [PDF]
An Incremental Clustering Method for Anomaly Detection in Flight Data
Zhao, W., Li, L., Alam, S. & Wang, Y.
Transportation Research Part C: Emerging Technologies., 2021, 132, 103406. https://doi.org/10.1016/j.trc.2021.103406 [PDF]
From Aircraft Tracking Data to Network Delay Model: A Data-Driven Approach Considering En-Route Congestion
Lin, Y., Li, L., Ren, P., Wang, Y. & Szeto, W. Y.Transportation Research Part C: Emerging
Technologies., 2021, 131, 103329.
https://doi.org/10.1016/j.trc.2021.103329 [PDF]
Correction to “High-Speed Rail Suspension System Health Monitoring Using Multi-Location Vibration Data”
Hong, N., Li, L., Yao, W., Zhao, Y., Yi, C., Lin, J. & Tsui, K. L.
IEEE Transactions on Intelligent Transportation Systems., 2021, 22, p. 6088 9526275. https://doi.org/10.1109/TITS.2021.3092455 [PDF]
Flight Time Prediction for Fuel Loading Decisions with a Deep Learning Approach
Zhu, X. & Li, L.
Transportation Research Part C: Emerging Technologies., 2021, 128, 103179. https://doi.org/10.1016/j.trc.2021.103179 [PDF]
Early Prediction of Battery Lifetime via a Machine Learning Based Framework
Fei, Z., Yang, F., Tsui, K.-L., Li, L. & Zhang, Z.
Energy., 2021, 225, 120205.
https://doi.org/10.1016/j.energy.2021.120205 [PDF]
The Image of the City on Social Media: A Comparative Study Using “Big Data” and “Small Data” Methods in the Tri-City Region in Poland
Huang, J., Obracht-Prondzynska, H., Kamrowska-Zaluska, D., Sun, Y. & Li, L.
Landscape and Urban Planning., 2021, 206, p. 103977. https://doi.org/10.1016/j.landurbplan.2020.103977 [PDF]
High-Speed Rail Suspension System Health Monitoring Using Multi-Location Vibration Data
Hong, N., Li, L., Yao, W., Zhao, Y., Yi, C., Lin, J. & Tsui, K. L.
IEEE Transactions on Intelligent Transportation Systems., 2020, 21, p. 2943-2955. https://doi.org/10.1109/TITS.2019.2921785 [PDF]
An Unpunctual Preventive Maintenance Policy under Two-Dimensional Warranty
Wang, X., Li, L. & Xie, M.
European Journal of Operational Research., 2020, 282, p. 304-318. https://doi.org/10.1016/j.ejor.2019.09.025 [PDF]
On Dynamically Monitoring Aggregate Warranty Claims for Early Detection of Reliability Problems
Li, C., Wang, X., Li, L., Xie, M. & Wang, X.
IISE Transactions., 2020, 52, p. 568-587 20 p. https://doi.org/10.1080/24725854.2019.1647477 [PDF]
Measuring the Resilience of an Airport Network
Wang, Y., Zhan, J., Xu, X., Li, L., Chen, P. & Hansen, M.
Chinese Journal of Aeronautics., 2019, 32, p. 2694-2705. https://doi.org/10.1016/j.cja.2019.08.023 [PDF]
Cost Analysis of a Piece-Wise Renewing Free Replacement Warranty Policy
Wang, X., He, K., He, Z., Li, L. & Xie, M.
Computers & Industrial Engineering., 2019, 135, p. 1047-1062. https://doi.org/10.1016/j.cie.2019.07.015 [PDF]
Optimal Preventive Maintenance Strategy for Leased Equipment under Successive Usage-Based Contracts
Wang, X., Li, L. & Xie, M.
International Journal of Production Research., 2019, 57, p. 5705-5724. https://doi.org/10.1080/00207543.2018.1542181 [PDF]
Calibrating Classification Probabilities with Shape-Restricted Polynomial Regression
Wang, Y., Li, L. & Dang, C.
IEEE Transactions on Pattern Analysis and Machine Intelligence., 2019, 41, p. 1813-1827. https://doi.org/10.1109/TPAMI.2019.2895794 [PDF]
A Multi-Agent Approach for Reactionary Delay Prediction of Flights
Guleria, Y., Cai, Q., Alam, S. & Li, L.
IEEE Access., 2019, 7, p. 181565-181579 8924720. https://doi.org/10.1109/ACCESS.2019.2957874 [PDF]
On Optimal Upgrade Strategy for Second-Hand Multi-Component Systems Sold with Warranty
Wang, X., Xie, M. & Li, L.
International Journal of Production Research., 2019, 57, p. 847-864. https://doi.org/10.1080/00207543.2018.1488087 [PDF]
Flight Trajectory Data Analytics for Characterization of Air Traffic Flows: A Comparative Analysis of Terminal Area Operations between New York, Hong Kong and Sao Paulo
Murça, M. C. R., Hansman, R. J., Li, L. & Ren, P.
Transportation Research Part C: Emerging Technologies., 2018, 97, p. 324-347. https://doi.org/10.1016/j.trc.2018.10.021 [PDF]
Characterizing Air Traffic Networks via Large-Scale Aircraft Tracking Data: A Comparison between China and the US Networks
Ren, P. & Li, L.
Journal of Air Transport Management., 2018, 67, p. 181-196. https://doi.org/10.1016/j.jairtraman.2017.12.005 [PDF]
Anomaly Detection via a Gaussian Mixture Model for Flight Operation and Safety Monitoring
Li, L., Hansman, R. J., Palacios, R. & Welsch, R.
Transportation Research Part C: Emerging Technologies., 2016, 64, p. 45-57. https://doi.org/10.1016/j.trc.2016.01.007 [PDF]
Ranking the Vulnerable Components of Aircraft by Considering Performance Degradations
Pei, Y., Wang, W. & Li, L.
Journal of Aircraft., 2016, 53, p. 1400-1410.
https://doi.org/10.2514/1.C033683
Flight Operations Monitoring through Cluster Analysis: A Case Study
Charruaud, F. & Li, L.
IEEE Intelligent Systems., 2015, 30, p. 24-29.
https://doi.org/10.1109/MIS.2015.111 [PDF]
Analysis of Flight Data Using Clustering Techniques for Detecting Abnormal Operations
Li, L., Das, S., Hansman, R. J., Palacios, R. & Srivastava, A. N.
Journal of Aerospace Information Systems., 2015, 12, p. 587-598. https://doi.org/10.2514/1.I010329 [PDF]

Conference Papers:

Data-driven approaches for satellite SADA system health monitoring with limited data
Zhu, X., Li, L., Mo, Y., Dong, Y., Shen, X., Chen, X. & Qin, S. J.
2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), 2024, p. 3225-3230.
https://doi.org/10.1109/CASE59546.2024.10711383 [PDF]
Dynamic Feature Extraction and Prediction for High Dimensional Time Series with Seasonality
Li, B., Wang, Y., Zhao, Y., Li, L. & Dong, Y.
2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), 2024, p. 368-373.
https://doi.org/10.1109/CASE59546.2024.10711621 [PDF]
Modelling Urban Air Mobility Demand: The Example of the Île-de-France Region
Vale de Almeida Norte, M., Fulton, M., Harvey, A., Plevier, C., Oosterholt, L., Mendez Garcia, M., Wichers, F. & Li, L.
AIAA AVIATION 2023 Forum., 2023, AIAA 2023-4105.
https://doi.org/10.2514/6.2023-4105
Aircraft Mass Estimation Using Quick Access Recorder Data
He, F., Li, L., Zhao, W. & Xiao, G.
2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC) (pp. 1-5). IEEE. https://doi.org/10.1109/DASC.2018.8569866 [PDF]
A Data-Driven Fuel Consumption Estimation Model for Airspace Redesign Analysis
Hong, N. & Li, L.
2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC) (pp. 1-8). IEEE. https://doi.org/10.1109/DASC.2018.8569564 [PDF]
An Adaptive Online Learning Model for Flight Data Cluster Analysis
Zhao, W., He, F., Li, L. & Xiao, G.
2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC) (pp. 1-7). IEEE. https://doi.org/10.1109/DASC.2018.8569600 [PDF]
An unpunctual preventive maintenance policy for repairable items sold with a two-dimensional warranty
Wang, X., Li, L., & Xie, M.
Proceedings of the 10th IMA International Conference on Modelling in Industrial Maintenance and Reliability, 2018, pp. 166-171.
[PDF]
Condition Monitoring of Wheel Wear for High-Speed Trains: A Data-Driven Approach
Xu, P., Yao, W., Zhao, Y., Yi, C., Li, L., Lin, J. & Tsui, K. L.
2018 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 1-8). IEEE.
https://doi.org/10.1109/ICPHM.2018.8448864 [PDF]
Happiness and High-rise Living: Sentiment Analysis of Geo-Located Twitter Data in Hong Kong’s Housing Estates
Huang, J., ZHANG, Q., LI, L., Yang, Y., CHIARADIA, A., Pryor, M. & Webster, C.
Proceedings of the 52nd ISOCARP Congress., 2016, p. 380-387.
Comparison of Algorithms for Anomaly Detection in Flight Recorder Data of Airline Operations
Das, S., Li, L., Srivastava, A. N. & Hansman, R. J.
12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference., 2012, AIAA 2012-5593. https://doi.org/10.2514/6.2012-5593
Anomaly Detection in Onboard-Recorded Flight Data Using Cluster Analysis
Li, L., Gariel, M., Hansman, R. J. & Palacios, R.
2011 IEEE/AIAA 30th Digital Avionics Systems Conference (pp. 4A4-1). IEEE. https://doi.org/10.1109/DASC.2011.6096068 [PDF]
Aircraft-Based Complexity Assessment for Radar Controllers in the Multi-Sector Planner Experiment
Li, L., Cho, H., Hansman, R. J. & Palacios, R.
10th AIAA Aviation Technology, Integration and Operations Conference 2010, ATIO 2010., 2010, Vol. 1.
https://doi.org/10.2514/6.2010-9005
Airspace Structure, Future ATC Systems, and Controller Complexity Reduction
Histon, J., Li, L. & Hansman, R. J.
29th Digital Avionics Systems Conference (pp. 4-A). IEEE. https://doi.org/10.1109/DASC.2010.5655354 [PDF]
Combining ATC Subjective Cognitive Complexity Evaluation Metrics in a Single Indicator
de Albuquerque Filho, E. A. F., Trabasso, L. G., Scarpel, R., Hansman, R. J. & Li, L.
10th AIAA Aviation Technology, Integration and Operations Conference 2010, ATIO 2010., 2010, Vol. 1.
https://doi.org/10.2514/6.2010-9006
Parametric Design of Low Emission Hybrid-Lift Cargo Aircraft
Donaldson, A. D., Dorbian, C. S., He, C., Li, L., Lovegren, J. A., Pyrgiotis, N. & Simaiakis, I.
48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition., 2010, 2010-1395.
https://doi.org/10.2514/6.2010-1395

Patents:

Grid Based Path Search Method for UAV Delivery operations in Urban Environment
HE, F., LI, L. & ZHANG, L.,
(2024), US Patent No. US11,915,599, Priority No. 17/468,615 [PDF]
Method of Presenting Flight Data of an Aircraft and a Graphical User Interface for Use with the Same
LI, L., CHARRUAUD, F. & ZHAO, W.,
(2022), US Patent No. US11,299,288, Priority No. 16/358,902 [PDF]
Route Network Planning for Drone Logistics in Urban Environment
LI, L., HE, F., HE, X. & ZHANG, L.,
(2021), Priority No. 17/468,628 (Filed)

Awards:

Silver Medal at the 50th International Exhibition of Inventions Geneva, 2025, Silver Medal.
LI, L., KUO, W., JIA, X., HE, X.
Silver Medal at the 50th International Exhibition of Inventions Geneva, 2025, Silver Medal.
QIN, S.-Z., LI, L., DONG, Y., LIU, Y.
Stanford’s top 2% most highly cited scientists 2024, 2024, Stanford’s top 2% most highly cited scientists.
LI, L.
the MIT ARCLab Prize for AI Innovation in Space 2024, 2024, 6th Place.
YE, Y., ZHU, X., LI, L.
Fellow of the Royal Aeronautical Society, 2024, Fellow.
LI, L.
Silver Medal at the 49th International Exhibition of Inventions of Geneva, 2024, Silver Medal.
LI, L., ZHU, X., ZHAO, W., CHARRUAUD, F., YE, Y., LI, S.
Silver Medal at the 49th International Exhibition of Inventions of Geneva, 2024, Silver Medal.
LI, L., HE, X., HE, F., Zhang, L.
The 1st Low-Altitude Economy Flight Management Challenge, 2024, 2nd Place.
SU, J., HE, X., LI, L., CHEN, M.
Gold with Congratulations of Jury Award at the 3rd Asia Exhibition of Innovations & Inventions Hong Kong, 2023, Gold with Congratulations of Jury Award.
LI, L.
Silver Award at the 3rd Asia Exhibition of Innovations & Inventions Hong Kong, 2023, Silver Award.
LI, L., HE, X., HE, F.
Merit award in 2023 China Intelligent Transportation Innovation Challenge, 2023, Merit Award.
LI, L., LI, Y., ZHU, X., Wang, H., Qian, M., Li, D., Shao, Y.
Stanford’s top 2% most highly cited scientists 2023, 2023, Stanford’s top 2% most highly cited scientists.
LI, L.
Stanford’s top 2% most highly cited scientists 2022, 2022, Stanford’s top 2% most highly cited scientists.
LI, L.
Senior Member of IEEE, 2022, Senior Member.
LI, L.
Silver Award Geneva International Exhibition of Inventions, 2022, Silver Award.
QIN, S. J., LAM, H. F., ZHANG, Q., YANG, Y., LI, L.
Best Presentation Award in the 3rd International Conference on Smart and Sustainable Planning for Cities and Regions (SSPCR), 2019, Best Presentation Award.
SUN, Y., Huang, J., LI, L., Kamrowska-Zaluska, D., Obracht-Prondzynska, H.
The 37th AIAA/IEEE Digital Avionics Systems Conference Best of Session (ATM-D: Analytics) Award, 2018, Best of Session (ATM-D: Analytics) Award.
HONG, N., LI, L.
Outstanding Scholar Award by Shenzhen Science and Technology Innovation Commission, 2017, Outstanding Scholar Award.
LI, L.