Research

You can also find my articles on my Google Scholar profile.

On the application side, my work follows the three phases of a flight in air traffic control: understanding how uncertainties such as weather impact en-route operations, enhancing the efficiency and safety of TRACON operations through automation, and studying the safety and reliability of airport surface operations. Recently, I have also worked on advanced air mobility and autonomous operations, such as the NASA wildfire project, which are expected to become a major part of the future airspace system.

On the theoretical side, I focus on risk and reliability, safety and security, and the trustworthiness of AI systems, which are increasingly used in air traffic control and other safety-critical applications.

1. Understanding the Impact of Uncertainties (e.g., Weather) on En-Route Operations

In the en-route phase, I study how convective weather and other uncertainties impact flights and traffic flow:

Probabilistic aircraft trajectory prediction under weather uncertainty
Air traffic state and density prediction for flow management
Dynamic airspace sectorization with learned workload prediction
Controller cognitive workload prediction

1.1 Air Traffic Controller Cognitive Workload Level Prediction using Conformal Dynamical Graph Learning. Pang, Y., Hu, J., Lieber, C., Cooke, N., & Liu, Y. Advanced Engineering Informatics, 2023. [bib]

1.2 Data-driven trajectory prediction with weather uncertainties: A Bayesian deep learning approach. Pang, Y., Zhao, X., Yan, H., & Liu, Y. Transportation Research Part C: Emerging Technologies, 2021. [bib]

1.3 PIGAT: Physics-Informed Graph Attention Transformer for Air Traffic State Prediction. Xu, Q., Pang, Y., Zhou, X., & Liu, Y. IEEE Transactions on Intelligent Transportation Systems, 2024. [bib]

All papers in this area (12 more)

1.4 Probabilistic Aircraft Trajectory Prediction with Weather Uncertainties using Approximate Bayesian Variational Inference to Neural Networks. Pang, Y., Wang, Y., & Liu, Y. AIAA AVIATION 2020 Forum, 2020. [bib]

1.5 Conditional Generative Adversarial Networks (CGAN) for Aircraft Trajectory Prediction considering weather effects. Pang, Y. & Liu, Y. AIAA SciTech 2020 Forum, 2020. [bib]

1.6 Probabilistic Aircraft Trajectory Prediction Considering Weather Uncertainties Using Dropout As Bayesian Approximate Variational Inference. Pang, Y. & Liu, Y. AIAA SciTech 2020 Forum, 2020. [bib]

1.7 Aircraft trajectory prediction using LSTM neural network with embedded convolutional layer. Pang, Y., Hu, J., Cheng, S., & Liu, Y. 11th Annual Conference of the Prognostics and Health Management Society, 2019. [bib]

1.8 A Recurrent Neural Network Approach for Aircraft Trajectory Prediction with Weather Features From Sherlock. Pang, Y., Yao, H., Hu, J., & Liu, Y. AIAA AVIATION 2019 Forum, 2019. [bib]

1.9 Dynamic airspace sectorization with machine learning enhanced workload prediction and clustering. Xu, Q., Pang, Y., & Liu, Y. Journal of Air Transport Management, 2024. [bib]

1.10 Systems and Methods for Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning. Liu, Y., Pang, Y., & Hu, J. U.S. Patent, 2024. [bib]

1.11 Air Traffic Density Prediction using Bayesian Ensemble Graph Attention Network (BEGAN). Xu, Q., Pang, Y., & Liu, Y. Transportation Research Part C: Emerging Technologies, 2023. [bib]

1.12 Uncertainty quantification and reduction in aircraft trajectory prediction using Bayesian-Entropy information fusion. Wang, Y., Pang, Y., Chen, O., Iyer, H. N., Dutta, P., Menon, P. K., & Liu, Y. Reliability Engineering & System Safety, 2021. [bib]

1.13 Aircraft Trajectory Prediction and Risk Assessment Using Bayesian Updating. Wang, Y., Pang, Y., Liu, Y., Dutta, P., & Yang, B. J. AIAA AVIATION 2019 Forum, 2019. [bib]

1.14 Systems and Methods for Dynamic Airspace Sectorization with Machine Learning Enhanced Workload Prediction and Clustering. Liu, Y., Xu, Q., & Pang, Y. U.S. Patent, 2024. [bib]

1.15 Spatio-temporal Anomaly Detection, Diagnostics, and Prediction of the Air-traffic Trajectory Deviation using Convective Weather. Zhao, X., Yan, H., Li, J., Pang, Y., & Liu, Y. Annual Conference of the PHM Society, Vol. 11, 2019. [bib]

2. Enhancing the Efficiency and Safety of TRACON Operations through Automation

In the terminal maneuvering area, I build automation for arrival operations and quantify the safety limits of terminal procedures:

Trajectory-based co-optimization of arrival scheduling and descent design
Fuel-optimal descent procedures under wind uncertainty
Multi-aircraft and procedure-aware trajectory prediction
Machine-learning-enhanced landing scheduling and autonomous landing
Runway capacity under communication and human uncertainties
Collision avoidance control laws and their capacity limits

2.1 Trajectory-Based Co-Optimization of Arrival Scheduling and Descent Path Design in the Terminal Maneuvering Area. Pang, Y.* & Clarke, J. Under review at Aerospace Science and Technology. [bib]

2.2 Modeling the Impact of Communication and Human Uncertainties on Runway Capacity in Terminal Airspace. Pang, Y.*, Kendall, A., & Clarke, J. Journal of Air Transport Management, 2026. [bib]

2.3 Optimal TRACON Descent Procedures under Wind Uncertainty and Fuel Savings Factors. Pang, Y.* & Clarke, J. Under review at AIAA Journal of Aircraft. [bib]

2.4 Trajectory-Based Optimization for Air Traffic Control in the Terminal Maneuvering Area. Pang, Y.*, Delahaye, D., & Clarke, J. Under review at Transportation Research Part C. [bib]

All papers in this area (9 more)

2.5 Geometric Trajectory Optimization for TRACON Arrivals: An NLP Approach with ATC Vectoring Maneuver Modeling. Pang, Y., Delahaye, D., & Clarke, J. Annual Modeling and Simulation Conference 2026, 2026. [bib]

2.6 A Reactive Control Law for Aircraft Collision Avoidance and Its Capacity Limits. Pang, Y.*, Kendall, A., & Clarke, J. Working paper, in preparation. [bib]

2.7 V-STAR: VectorNet-Augmented Spatio-Temporal Attention and Reasoning for Procedure-Aware Aircraft Trajectory Prediction. Pang, Y., Hu, J., Iyer, H., Zhao, X., & Clarke, J. Under review at Transportation Research Part C. [bib]

2.8 Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties. Pang, Y., Zhao, P., Hu, J., & Liu, Y. Transportation Research Part C: Emerging Technologies, 2024. [bib]

2.9 Bayesian Spatio-Temporal Graph Transformer Network (B-STAR) for Multi-Aircraft Trajectory Prediction. Pang, Y., Zhao, X., Hu, J., Yan, H., & Liu, Y. Knowledge-Based Systems, 2022. [bib]

2.10 Data-driven governing equation identification of near terminal air traffic flow dynamics. Xu, Q., Pang, Y., & Liu, Y. Journal of Air Transport Management, 2025. [bib]

2.11 Systems and Methods for Machine Learning-Enhanced Aircraft Landing Scheduling Under Uncertainties. Liu, Y., Pang, Y., & Hu, J. U.S. Patent, 2024. [bib]

2.12 A Voice Communication-Augmented Simulation Framework for Aircraft Trajectory Simulation. Wang, Y., Pang, Y., Gorceski, S., Kostiuk, P., Mohen, M. T., Menon, P. K., & Liu, Y. IEEE Transactions on Intelligent Transportation Systems, 2021. [bib]

2.13 Deep Q-Network With Lagrangian Relaxation for Autonomous Aircraft Landing. Momit, M. A., Jiang, W., Hussain, B. Z., Ammar, M., Bhujel, S., Pang, Y., et al., & Hu, J. AIAA SCITECH 2026 Forum, 2026. [bib]

3. Safety and Reliability Studies of Airport Surface Operations

On the airport surface, I turn pilot and controller voice communication and surveillance data into safety and efficiency tools:

Surface-movement collision risk assessment from pilot-ATC radio calls
Real-time runway incursion alerting from ATC speech and ADS-B
Runway and taxiway exit prediction for landing aircraft

3.1 Data-Driven Runway and Taxiway Exits Prediction of Landing Aircraft: A Case Study at Hartsfield-Jackson Atlanta International Airport. Porcayo, A., Pang, Y.*, Thomas, M., & Clarke, J. Journal of Air Transport Management, 2026. [bib]

3.2 From Voice to Safety: Language AI Powered Pilot-ATC Communication Understanding for Airport Surface Movement Collision Risk Assessment. Pang, Y.*, Kendall, A. P., Porcayo, A., Barsotti, M., Jain, A., & Clarke, J. Transportation Research Part C: Emerging Technologies, 2026. [bib]

All papers in this area (2 more)

3.3 A Probabilistic Runway Occupancy Conflict Alerting Framework for Real-Time Runway Incursion Detection from ATC Speech and ADS-B. Dong, Z., Pang, Y.*, & Clarke, J. Under review at AIAA Journal of Aerospace Information Systems. [bib]

3.4 Physics-Based Learning for Aircraft Waiting Time Prediction. Xu, Q., Pang, Y., Zhang, Z., & Liu, Y. AIAA AVIATION 2022 Forum, 2022. [bib]

4. Advanced Air Mobility and Autonomous Operations

Beyond crewed aviation, I study advanced air mobility, remotely piloted aircraft, and autonomous delivery operations:

Reliability of remotely piloted aircraft under communication uncertainties
Communication and autonomy level tradeoffs in autonomous operations
Last-mile and same-day delivery routing with capacity and time guarantees
UAV package delivery aerodynamics
Multi-agent reinforcement learning for decentralized decision-making

4.1 The Reliability of Remotely Piloted Aircraft System Performance under Aeronautical Communication Uncertainties. Pang, Y.*, Kendall, A., & Clarke, J. Reliability Engineering & System Safety, 2026. [bib]

4.2 Optimizing Same-Day Delivery: A Framework Incorporating Vehicle Capacity and Time Guarantees. Liang, J., Pang, Y.*, & Clarke, J. Under review at International Journal of Systems Science: Operations & Logistics. [bib]

4.3 Decentralized graph-based multi-agent reinforcement learning using reward machines. Hu, J., Xu, Z., Wang, W., Qu, G., Pang, Y., & Liu, Y. Neurocomputing, 2023. [bib]

All papers in this area (3 more)

4.4 An Optimization Formulation for Last-Mile Delivery Using Ring Widget Structure. Liang, J., Pang, Y.*, & Clarke, J. Working paper, in preparation. [bib]

4.5 Communication and Autonomy Level Tradeoff for Autonomous Systems Resource Allocation. Pang, Y.*, Kendall, A., & Clarke, J. Working paper, in preparation. [bib]

4.6 Aerodynamic Effects of Fuselage-Package Separation Distance on UAV Performance. Bhujel, S., Pang, Y., Snyder, P., Tang, C., & Hu, J. AIAA AVIATION 2026 Forum, 2026. [bib]

5. Reliability & Trustworthy AI

Beneath the applications sits methodological work on trustworthy machine learning and reliability engineering:

Explainable risk analysis from aviation safety reports
Bayesian uncertainty quantification for deep learning
Physics-informed learning for fracture and structural health monitoring
Adversarial robustness of Bayesian neural networks
Maintenance scheduling under uncertainty

5.1 A Hybrid Framework for Explainable Aviation Risk: Causal Chain Extraction from Safety Reports with Physics-Based Failure Quantification and Counterfactual Analysis. Pang, Y., Tian, Y., Xie, J., Zhao, X., Hu, J., Jacobsen, H., & Clarke, J. To be submitted to Advanced Engineering Informatics. [bib]

5.2 Epistemic and aleatoric uncertainty quantification for crack detection using a Bayesian Boundary Aware Convolutional Network. Rathnakumar, R., Pang, Y., & Liu, Y. Reliability Engineering & System Safety, 2023. [bib]

5.3 Posterior Regularized Bayesian Neural Network Incorporating Soft and Hard Knowledge Constraints. Huang, J., Pang, Y., Zhao, X., Liu, Y., & Yan, H. Knowledge-Based Systems, 2022. [bib]

All papers in this area (6 more)

5.4 Robust Satellite Image Classification with Bayesian Deep Learning. Pang, Y., Xu, N., & Liu, Y. 2022 Integrated Communication, Navigation and Surveillance Conference (ICNS), 2022. [bib]

5.5 Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks. Pang, Y., Cheng, S., Hu, J., & Liu, Y. CVPR 2021 Workshop on Adversarial Machine Learning in Real-World Computer Vision Systems, 2021. [bib]

5.6 Bayesian Approach for Uncertainty Quantification of Neural Networks-Based Crack Diagnostics. Rathnakumar, R., Liu, Y., & Pang, Y. AIAA SCITECH 2025 Forum, 2025. [bib]

5.7 FBC-ANet: A Semantic Segmentation Model for UAV Forest Fire Images Combining Boundary Enhancement and Context Awareness. Zhang, L., Wang, M., Ding, Y., Wan, T., Qi, B., & Pang, Y. Drones, 2023. [bib]

5.8 Optimal maintenance scheduling under uncertainties using Linear Programming-enhanced Reinforcement Learning. Hu, J., Wang, Y., Pang, Y., & Liu, Y. Engineering Applications of Artificial Intelligence, 2022. [bib]

5.9 Fracture Pattern Prediction with Random Microstructure using Physics-Informed Deep Neural Networks. Wei, H., Yao, H., Pang, Y., & Liu, Y. Engineering Fracture Mechanics, 2022. [bib]