Yutian Pang, PhD

Yutian Pang, PhD

Postdoctoral Fellow, Aerospace Engineering & Engineering Mechanics

Sitemap

A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

portfolio

publications

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.

Published in Annual Conference of the PHM Society, Vol. 11, 2019

Detects, diagnoses, and predicts spatio-temporal trajectory deviations caused by convective weather.

BibTeX
@inproceedings{zhao2019spatio,
  title = {Spatio-temporal Anomaly Detection, Diagnostics, and Prediction of the Air-traffic Trajectory Deviation using Convective Weather},
  author = {Zhao, X. and Yan, H. and Li, J. and Pang, Y. and Liu, Y.},
  booktitle = {Annual Conference of the PHM Society, Vol. 11},
  year = {2019}
}

Aircraft Trajectory Prediction and Risk Assessment Using Bayesian Updating

Wang, Y., Pang, Y., Liu, Y., Dutta, P., & Yang, B. J.

Published in AIAA AVIATION 2019 Forum, 2019

Bayesian updating for aircraft trajectory prediction and en-route risk assessment.

BibTeX
@inproceedings{wang2019aircraft,
  title = {Aircraft Trajectory Prediction and Risk Assessment Using Bayesian Updating},
  author = {Wang, Y. and Pang, Y. and Liu, Y. and Dutta, P. and Yang, B. J.},
  booktitle = {AIAA AVIATION 2019 Forum},
  pages = {2936},
  year = {2019}
}

A Recurrent Neural Network Approach for Aircraft Trajectory Prediction with Weather Features From Sherlock

Pang, Y., Yao, H., Hu, J., & Liu, Y.

Published in AIAA AVIATION 2019 Forum, 2019

Recurrent networks that predict aircraft trajectories using weather features from NASA’s Sherlock data warehouse.

BibTeX
@inproceedings{pang2019recurrent,
  title = {A Recurrent Neural Network Approach for Aircraft Trajectory Prediction with Weather Features From Sherlock},
  author = {Pang, Y. and Yao, H. and Hu, J. and Liu, Y.},
  booktitle = {AIAA AVIATION 2019 Forum},
  pages = {3413},
  year = {2019}
}

Aircraft trajectory prediction using LSTM neural network with embedded convolutional layer

Pang, Y., Hu, J., Cheng, S., & Liu, Y.

Published in 11th Annual Conference of the Prognostics and Health Management Society, 2019

An LSTM network with embedded convolutional layers for aircraft trajectory prediction.

BibTeX
@inproceedings{pang2019aircraft,
  title = {Aircraft trajectory prediction using LSTM neural network with embedded convolutional layer},
  author = {Pang, Y. and Hu, J. and Cheng, S. and Liu, Y.},
  booktitle = {11th Annual Conference of the Prognostics and Health Management Society},
  year = {2019}
}

Probabilistic Aircraft Trajectory Prediction Considering Weather Uncertainties Using Dropout As Bayesian Approximate Variational Inference

Pang, Y. & Liu, Y.

Published in AIAA SciTech 2020 Forum, 2020

Uses dropout as Bayesian approximate inference to quantify weather-driven uncertainty in trajectory prediction.

BibTeX
@inproceedings{pang2020probabilistic,
  title = {Probabilistic Aircraft Trajectory Prediction Considering Weather Uncertainties Using Dropout As Bayesian Approximate Variational Inference},
  author = {Pang, Y. and Liu, Y.},
  booktitle = {AIAA SciTech 2020 Forum},
  pages = {1413},
  year = {2020}
}

Conditional Generative Adversarial Networks (CGAN) for Aircraft Trajectory Prediction considering weather effects

Pang, Y. & Liu, Y.

Published in AIAA SciTech 2020 Forum, 2020

Conditional GANs that generate weather-conditioned aircraft trajectory predictions.

BibTeX
@inproceedings{pang2020conditional,
  title = {Conditional Generative Adversarial Networks (CGAN) for Aircraft Trajectory Prediction considering weather effects},
  author = {Pang, Y. and Liu, Y.},
  booktitle = {AIAA SciTech 2020 Forum},
  pages = {1853},
  year = {2020}
}

Probabilistic Aircraft Trajectory Prediction with Weather Uncertainties using Approximate Bayesian Variational Inference to Neural Networks

Pang, Y., Wang, Y., & Liu, Y.

Published in AIAA AVIATION 2020 Forum, 2020

Approximate Bayesian variational inference applied to neural networks for weather-aware probabilistic trajectory prediction.

BibTeX
@inproceedings{pang2020probabilisticb,
  title = {Probabilistic Aircraft Trajectory Prediction with Weather Uncertainties using Approximate Bayesian Variational Inference to Neural Networks},
  author = {Pang, Y. and Wang, Y. and Liu, Y.},
  booktitle = {AIAA AVIATION 2020 Forum},
  pages = {2897},
  year = {2020}
}

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.

Published in IEEE Transactions on Intelligent Transportation Systems, 2021

A simulation framework that injects realistic pilot-controller voice communication into aircraft trajectory modeling.

BibTeX
@article{wang2021voice,
  title = {A Voice Communication-Augmented Simulation Framework for Aircraft Trajectory Simulation},
  author = {Wang, Y. and Pang, Y. and Gorceski, S. and Kostiuk, P. and Mohen, M. T. and Menon, P. K. and Liu, Y.},
  journal = {IEEE Transactions on Intelligent Transportation Systems},
  year = {2021}
}

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.

Published in Reliability Engineering & System Safety, 2021

Fuses physics knowledge with data through Bayesian-entropy methods to tighten the uncertainty of trajectory predictions.

BibTeX
@article{wang2021uncertainty,
  title = {Uncertainty quantification and reduction in aircraft trajectory prediction using Bayesian-Entropy information fusion},
  author = {Wang, Y. and Pang, Y. and Chen, O. and Iyer, H. N. and Dutta, P. and Menon, P. K. and Liu, Y.},
  journal = {Reliability Engineering \& System Safety},
  pages = {107650},
  year = {2021}
}

Data-driven trajectory prediction with weather uncertainties: A Bayesian deep learning approach

Pang, Y., Zhao, X., Yan, H., & Liu, Y.

Published in Transportation Research Part C: Emerging Technologies, 2021

Among the first Bayesian deep-learning frameworks for aircraft trajectory prediction to treat convective weather as a first-class source of uncertainty; my most-cited work.

BibTeX
@article{pang2021data,
  title = {Data-driven trajectory prediction with weather uncertainties: A Bayesian deep learning approach},
  author = {Pang, Y. and Zhao, X. and Yan, H. and Liu, Y.},
  journal = {Transportation Research Part C: Emerging Technologies},
  volume = {130},
  pages = {103326},
  year = {2021}
}

Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks

Pang, Y., Cheng, S., Hu, J., & Liu, Y.

Published in CVPR 2021 Workshop on Adversarial Machine Learning in Real-World Computer Vision Systems, 2021

arXiv

Evaluates how Bayesian neural networks hold up against different classes of adversarial attacks.

BibTeX
@inproceedings{pang2021evaluating,
  title = {Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks},
  author = {Pang, Y. and Cheng, S. and Hu, J. and Liu, Y.},
  booktitle = {CVPR 2021 Workshop on Adversarial Machine Learning in Real-World Computer Vision Systems},
  year = {2021},
  eprint = {2106.09223},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url = {https://arxiv.org/abs/2106.09223}
}

Bayesian Spatio-Temporal Graph Transformer Network (B-STAR) for Multi-Aircraft Trajectory Prediction

Pang, Y., Zhao, X., Hu, J., Yan, H., & Liu, Y.

Published in Knowledge-Based Systems, 2022

A Bayesian spatio-temporal graph transformer that predicts the trajectories of many interacting aircraft jointly, with quantified uncertainty.

BibTeX
@article{pang2022bayesian,
  title = {Bayesian Spatio-Temporal Graph Transformer Network (B-STAR) for Multi-Aircraft Trajectory Prediction},
  author = {Pang, Y. and Zhao, X. and Hu, J. and Yan, H. and Liu, Y.},
  journal = {Knowledge-Based Systems},
  volume = {249},
  pages = {108998},
  year = {2022}
}

Fracture Pattern Prediction with Random Microstructure using Physics-Informed Deep Neural Networks

Wei, H., Yao, H., Pang, Y., & Liu, Y.

Published in Engineering Fracture Mechanics, 2022

Physics-informed deep networks that predict fracture patterns across random material microstructures.

BibTeX
@article{wei2022fracture,
  title = {Fracture Pattern Prediction with Random Microstructure using Physics-Informed Deep Neural Networks},
  author = {Wei, H. and Yao, H. and Pang, Y. and Liu, Y.},
  journal = {Engineering Fracture Mechanics},
  pages = {108497},
  year = {2022}
}

Optimal maintenance scheduling under uncertainties using Linear Programming-enhanced Reinforcement Learning

Hu, J., Wang, Y., Pang, Y., & Liu, Y.

Published in Engineering Applications of Artificial Intelligence, 2022

Couples linear programming with reinforcement learning to schedule maintenance actions under uncertainty.

BibTeX
@article{hu2022optimal,
  title = {Optimal maintenance scheduling under uncertainties using Linear Programming-enhanced Reinforcement Learning},
  author = {Hu, J. and Wang, Y. and Pang, Y. and Liu, Y.},
  journal = {Engineering Applications of Artificial Intelligence},
  volume = {109},
  pages = {104655},
  year = {2022}
}

Robust Satellite Image Classification with Bayesian Deep Learning

Pang, Y., Xu, N., & Liu, Y.

Published in 2022 Integrated Communication, Navigation and Surveillance Conference (ICNS), 2022

Bayesian deep learning for satellite image classification that stays robust under distribution shift.

BibTeX
@inproceedings{pang2022robust,
  title = {Robust Satellite Image Classification with Bayesian Deep Learning},
  author = {Pang, Y. and Xu, N. and Liu, Y.},
  booktitle = {2022 Integrated Communication, Navigation and Surveillance Conference (ICNS)},
  year = {2022}
}

Physics-Based Learning for Aircraft Waiting Time Prediction

Xu, Q., Pang, Y., Zhang, Z., & Liu, Y.

Published in AIAA AVIATION 2022 Forum, 2022

Physics-based learning that predicts aircraft waiting times in congested operations.

BibTeX
@inproceedings{xu2022physics,
  title = {Physics-Based Learning for Aircraft Waiting Time Prediction},
  author = {Xu, Q. and Pang, Y. and Zhang, Z. and Liu, Y.},
  booktitle = {AIAA AVIATION 2022 Forum},
  pages = {3826},
  year = {2022}
}

Posterior Regularized Bayesian Neural Network Incorporating Soft and Hard Knowledge Constraints

Huang, J., Pang, Y., Zhao, X., Liu, Y., & Yan, H.

Published in Knowledge-Based Systems, 2022

arXiv

Injects soft and hard domain-knowledge constraints into Bayesian neural networks through posterior regularization.

BibTeX
@article{huang2022posterior,
  title = {Posterior Regularized Bayesian Neural Network Incorporating Soft and Hard Knowledge Constraints},
  author = {Huang, J. and Pang, Y. and Zhao, X. and Liu, Y. and Yan, H.},
  journal = {Knowledge-Based Systems},
  volume = {249},
  pages = {109047},
  year = {2022},
  eprint = {2210.08608},
  archivePrefix = {arXiv},
  primaryClass = {cs.AI},
  url = {https://arxiv.org/abs/2210.08608}
}

Air Traffic Controller Cognitive Workload Level Prediction using Conformal Dynamical Graph Learning

Pang, Y., Hu, J., Lieber, C., Cooke, N., & Liu, Y.

Published in Advanced Engineering Informatics, 2023

arXiv

Predicts controller cognitive workload in real time on dynamical airspace graphs, with conformal-prediction guarantees on the estimates.

BibTeX
@article{pang2023air,
  title = {Air Traffic Controller Cognitive Workload Level Prediction using Conformal Dynamical Graph Learning},
  author = {Pang, Y. and Hu, J. and Lieber, C. and Cooke, N. and Liu, Y.},
  journal = {Advanced Engineering Informatics},
  volume = {57},
  pages = {102113},
  year = {2023},
  eprint = {2307.10559},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url = {https://arxiv.org/abs/2307.10559}
}

Air Traffic Density Prediction using Bayesian Ensemble Graph Attention Network (BEGAN)

Xu, Q., Pang, Y., & Liu, Y.

Published in Transportation Research Part C: Emerging Technologies, 2023

A Bayesian ensemble graph attention network that forecasts air-traffic density with calibrated confidence intervals.

BibTeX
@article{xu2023air,
  title = {Air Traffic Density Prediction using Bayesian Ensemble Graph Attention Network (BEGAN)},
  author = {Xu, Q. and Pang, Y. and Liu, Y.},
  journal = {Transportation Research Part C: Emerging Technologies},
  year = {2023}
}

Decentralized graph-based multi-agent reinforcement learning using reward machines

Hu, J., Xu, Z., Wang, W., Qu, G., Pang, Y., & Liu, Y.

Published in Neurocomputing, 2023

arXiv

Decentralized multi-agent reinforcement learning that exploits reward-machine structure over graphs for scalable coordination.

BibTeX
@article{hu2023decentralized,
  title = {Decentralized graph-based multi-agent reinforcement learning using reward machines},
  author = {Hu, J. and Xu, Z. and Wang, W. and Qu, G. and Pang, Y. and Liu, Y.},
  journal = {Neurocomputing},
  pages = {126974},
  year = {2023},
  eprint = {2110.00096},
  archivePrefix = {arXiv},
  primaryClass = {cs.MA},
  url = {https://arxiv.org/abs/2110.00096}
}

Epistemic and aleatoric uncertainty quantification for crack detection using a Bayesian Boundary Aware Convolutional Network

Rathnakumar, R., Pang, Y., & Liu, Y.

Published in Reliability Engineering & System Safety, 2023

arXiv

Separates epistemic from aleatoric uncertainty in neural-network crack detection, enabling risk-aware structural inspection.

BibTeX
@article{rathnakumar2023epistemic,
  title = {Epistemic and aleatoric uncertainty quantification for crack detection using a Bayesian Boundary Aware Convolutional Network},
  author = {Rathnakumar, R. and Pang, Y. and Liu, Y.},
  journal = {Reliability Engineering \& System Safety},
  pages = {109547},
  year = {2023},
  eprint = {2302.06827},
  archivePrefix = {arXiv},
  primaryClass = {cs.CV},
  url = {https://arxiv.org/abs/2302.06827}
}

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.

Published in Drones, 2023

A semantic-segmentation network for UAV imagery that sharpens forest-fire boundary detection through boundary enhancement and context awareness.

BibTeX
@article{zhang2023fbc,
  title = {FBC-ANet: A Semantic Segmentation Model for UAV Forest Fire Images Combining Boundary Enhancement and Context Awareness},
  author = {Zhang, L. and Wang, M. and Ding, Y. and Wan, T. and Qi, B. and Pang, Y.},
  journal = {Drones},
  volume = {7},
  number = {7},
  pages = {456},
  year = {2023}
}

Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties

Pang, Y., Zhao, P., Hu, J., & Liu, Y.

Published in Transportation Research Part C: Emerging Technologies, 2024

arXiv

Blends machine-learned arrival-time distributions with optimization to schedule aircraft landings that stay efficient under uncertainty.

BibTeX
@article{pang2024machine,
  title = {Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties},
  author = {Pang, Y. and Zhao, P. and Hu, J. and Liu, Y.},
  journal = {Transportation Research Part C: Emerging Technologies},
  volume = {158},
  pages = {104444},
  year = {2024},
  eprint = {2311.16030},
  archivePrefix = {arXiv},
  primaryClass = {cs.AI},
  url = {https://arxiv.org/abs/2311.16030}
}

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

Patents dynamic airspace sectorization driven by learned workload prediction and clustering.

BibTeX
@misc{liu2024systems,
  title = {Systems and Methods for Dynamic Airspace Sectorization with Machine Learning Enhanced Workload Prediction and Clustering},
  author = {Liu, Y. and Xu, Q. and Pang, Y.},
  howpublished = {U.S. Patent},
  year = {2024}
}

PIGAT: Physics-Informed Graph Attention Transformer for Air Traffic State Prediction

Xu, Q., Pang, Y., Zhou, X., & Liu, Y.

Published in IEEE Transactions on Intelligent Transportation Systems, 2024

A physics-informed graph attention transformer that forecasts air traffic state while respecting the underlying flow dynamics.

BibTeX
@article{xu2024pigat,
  title = {PIGAT: Physics-Informed Graph Attention Transformer for Air Traffic State Prediction},
  author = {Xu, Q. and Pang, Y. and Zhou, X. and Liu, Y.},
  journal = {IEEE Transactions on Intelligent Transportation Systems},
  year = {2024}
}

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

Patents the conformalized dynamical graph learning approach to real-time controller workload prediction.

BibTeX
@misc{liu2024systemsb,
  title = {Systems and Methods for Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning},
  author = {Liu, Y. and Pang, Y. and Hu, J.},
  howpublished = {U.S. Patent},
  year = {2024}
}

Dynamic airspace sectorization with machine learning enhanced workload prediction and clustering

Xu, Q., Pang, Y., & Liu, Y.

Published in Journal of Air Transport Management, 2024

Repartitions airspace sectors dynamically using learned controller-workload predictions, balancing load across the system.

BibTeX
@article{xu2024dynamic,
  title = {Dynamic airspace sectorization with machine learning enhanced workload prediction and clustering},
  author = {Xu, Q. and Pang, Y. and Liu, Y.},
  journal = {Journal of Air Transport Management},
  year = {2024}
}

Systems and Methods for Machine Learning-Enhanced Aircraft Landing Scheduling Under Uncertainties

Liu, Y., Pang, Y., & Hu, J.

U.S. Patent, 2024

Patents the machine-learning-enhanced landing scheduling framework developed in my Transportation Research Part C work.

BibTeX
@misc{liu2024systemsc,
  title = {Systems and Methods for Machine Learning-Enhanced Aircraft Landing Scheduling Under Uncertainties},
  author = {Liu, Y. and Pang, Y. and Hu, J.},
  howpublished = {U.S. Patent},
  year = {2024}
}

Bayesian Approach for Uncertainty Quantification of Neural Networks-Based Crack Diagnostics

Rathnakumar, R., Liu, Y., & Pang, Y.

Published in AIAA SCITECH 2025 Forum, 2025

Bayesian uncertainty quantification for neural-network-based structural crack diagnostics.

BibTeX
@inproceedings{rathnakumar2025bayesian,
  title = {Bayesian Approach for Uncertainty Quantification of Neural Networks-Based Crack Diagnostics},
  author = {Rathnakumar, R. and Liu, Y. and Pang, Y.},
  booktitle = {AIAA SCITECH 2025 Forum},
  pages = {1959},
  year = {2025}
}

Data-driven governing equation identification of near terminal air traffic flow dynamics

Xu, Q., Pang, Y., & Liu, Y.

Published in Journal of Air Transport Management, 2025

Recovers interpretable governing equations of near-terminal air traffic flow dynamics directly from operational data.

BibTeX
@article{xu2025data,
  title = {Data-driven governing equation identification of near terminal air traffic flow dynamics},
  author = {Xu, Q. and Pang, Y. and Liu, Y.},
  journal = {Journal of Air Transport Management},
  year = {2025}
}

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

An optimization framework for same-day delivery routing that honors vehicle capacity limits and delivery-time guarantees.

BibTeX
@unpublished{liang2026optimizing,
  title = {Optimizing Same-Day Delivery: A Framework Incorporating Vehicle Capacity and Time Guarantees},
  author = {Liang, J. and Pang, Y. and Clarke, J.},
  note = {Under review at International Journal of Systems Science: Operations & Logistics},
  year = {2026}
}

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

arXiv

Turns terminal-area vectoring practice into computable, conflict-free arrival trajectories through trajectory-based optimization.

BibTeX
@unpublished{pang2026trajectory,
  title = {Trajectory-Based Optimization for Air Traffic Control in the Terminal Maneuvering Area},
  author = {Pang, Y. and Delahaye, D. and Clarke, J.},
  note = {Under review at Transportation Research Part C},
  year = {2026},
  eprint = {2604.17776},
  archivePrefix = {arXiv},
  primaryClass = {eess.SY},
  url = {https://arxiv.org/abs/2604.17776}
}

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

Real-time runway-incursion alerting that fuses ATC speech understanding with ADS-B surveillance into probabilistic runway-occupancy conflicts.

BibTeX
@unpublished{dong2026probabilistic,
  title = {A Probabilistic Runway Occupancy Conflict Alerting Framework for Real-Time Runway Incursion Detection from ATC Speech and ADS-B},
  author = {Dong, Z. and Pang, Y. and Clarke, J.},
  note = {Under review at AIAA Journal of Aerospace Information Systems},
  year = {2026}
}

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.

Published in Transportation Research Part C: Emerging Technologies, 2026

arXiv

Language AI that interprets pilot-ATC radio communication and fuses it with surveillance data to assess collision risk during airport surface movement.

BibTeX
@article{pang2026from,
  title = {From Voice to Safety: Language AI Powered Pilot-ATC Communication Understanding for Airport Surface Movement Collision Risk Assessment},
  author = {Pang, Y. and Kendall, A. P. and Porcayo, A. and Barsotti, M. and Jain, A. and Clarke, J.},
  journal = {Transportation Research Part C: Emerging Technologies},
  volume = {184},
  pages = {105540},
  year = {2026},
  eprint = {2503.04974},
  archivePrefix = {arXiv},
  primaryClass = {eess.AS},
  url = {https://arxiv.org/abs/2503.04974}
}

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

Augments spatio-temporal attention with vectorized procedure maps so that trajectory predictions respect published terminal-area procedures.

BibTeX
@unpublished{pang2026vstar,
  title = {V-STAR: VectorNet-Augmented Spatio-Temporal Attention and Reasoning for Procedure-Aware Aircraft Trajectory Prediction},
  author = {Pang, Y. and Hu, J. and Iyer, H. and Zhao, X. and Clarke, J.},
  note = {Under review at Transportation Research Part C},
  year = {2026}
}

Communication and Autonomy Level Tradeoff for Autonomous Systems Resource Allocation

Pang, Y.*, Kendall, A., & Clarke, J.

Working paper, in preparation

Characterizes the tradeoff between communication load and autonomy level when allocating resources across autonomous systems.

BibTeX
@unpublished{pang2026communication,
  title = {Communication and Autonomy Level Tradeoff for Autonomous Systems Resource Allocation},
  author = {Pang, Y. and Kendall, A. and Clarke, J.},
  note = {Working paper},
  year = {2026}
}

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.

Published in AIAA SCITECH 2026 Forum, 2026

Deep Q-learning with Lagrangian relaxation for safe autonomous aircraft landing decisions.

BibTeX
@inproceedings{momit2026deep,
  title = {Deep Q-Network With Lagrangian Relaxation for Autonomous Aircraft Landing},
  author = {Momit, M. A. and Jiang, W. and Hussain, B. Z. and Ammar, M. and Bhujel, S. and Pang, Y. and Hu, J. and others},
  booktitle = {AIAA SCITECH 2026 Forum},
  pages = {1983},
  year = {2026}
}

Optimal TRACON Descent Procedures under Wind Uncertainty and Fuel Savings Factors

Pang, Y.* & Clarke, J.

Under review at AIAA Journal of Aircraft

arXiv

Selects the flap-deployment trigger speeds and the glideslope-capture distance that minimize expected descent fuel under wind uncertainty, from six-degree-of-freedom aircraft and flight management system simulations.

BibTeX
@unpublished{pang2026fuel,
  title = {Optimal TRACON Descent Procedures under Wind Uncertainty and Fuel Savings Factors},
  author = {Pang, Y. and Clarke, J.},
  note = {Under review at AIAA Journal of Aircraft},
  year = {2026},
  eprint = {2608.22480},
  archivePrefix = {arXiv},
  primaryClass = {eess.SY},
  url = {https://arxiv.org/abs/2608.22480}
}

The Reliability of Remotely Piloted Aircraft System Performance under Aeronautical Communication Uncertainties

Pang, Y.*, Kendall, A., & Clarke, J.

Published in Reliability Engineering & System Safety, 2026

arXiv

A reliability framework showing how uncertainties in aeronautical communication links bound the safe performance of remotely piloted aircraft systems.

BibTeX
@article{pang2026reliability,
  title = {The Reliability of Remotely Piloted Aircraft System Performance under Aeronautical Communication Uncertainties},
  author = {Pang, Y. and Kendall, A. and Clarke, J.},
  journal = {Reliability Engineering \& System Safety},
  year = {2026},
  eprint = {2501.07743},
  archivePrefix = {arXiv},
  primaryClass = {eess.SY},
  url = {https://arxiv.org/abs/2501.07743}
}

A Reactive Control Law for Aircraft Collision Avoidance and Its Capacity Limits

Pang, Y.*, Kendall, A., & Clarke, J.

Working paper, in preparation

A reactive control law for aircraft collision avoidance, with analytical bounds on the traffic capacity it can safely sustain.

BibTeX
@unpublished{pang2026reactive,
  title = {A Reactive Control Law for Aircraft Collision Avoidance and Its Capacity Limits},
  author = {Pang, Y. and Kendall, A. and Clarke, J.},
  note = {Working paper},
  year = {2026}
}

Geometric Trajectory Optimization for TRACON Arrivals: An NLP Approach with ATC Vectoring Maneuver Modeling

Pang, Y., Delahaye, D., & Clarke, J.

Published in Annual Modeling and Simulation Conference 2026, 2026

arXiv

A nonlinear-programming formulation of TRACON arrival optimization that models controller vectoring maneuvers geometrically.

BibTeX
@inproceedings{pang2026geometric,
  title = {Geometric Trajectory Optimization for TRACON Arrivals: An NLP Approach with ATC Vectoring Maneuver Modeling},
  author = {Pang, Y. and Delahaye, D. and Clarke, J.},
  booktitle = {Annual Modeling and Simulation Conference 2026},
  year = {2026},
  eprint = {2604.18454},
  archivePrefix = {arXiv},
  primaryClass = {math.OC},
  url = {https://arxiv.org/abs/2604.18454}
}

Modeling the Impact of Communication and Human Uncertainties on Runway Capacity in Terminal Airspace

Pang, Y.*, Kendall, A., & Clarke, J.

Published in Journal of Air Transport Management, 2026

arXiv

Quantifies how pilot-controller communication delays and human response variability erode the runway throughput actually achievable in terminal airspace.

BibTeX
@article{pang2026modeling,
  title = {Modeling the Impact of Communication and Human Uncertainties on Runway Capacity in Terminal Airspace},
  author = {Pang, Y. and Kendall, A. and Clarke, J.},
  journal = {Journal of Air Transport Management},
  year = {2026},
  eprint = {2510.09943},
  archivePrefix = {arXiv},
  primaryClass = {eess.SY},
  url = {https://arxiv.org/abs/2510.09943}
}

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

arXiv

Jointly optimizes arrival sequencing and descent-path geometry in the terminal maneuvering area from recorded trajectory data.

BibTeX
@unpublished{pang2026trajectoryb,
  title = {Trajectory-Based Co-Optimization of Arrival Scheduling and Descent Path Design in the Terminal Maneuvering Area},
  author = {Pang, Y. and Clarke, J.},
  note = {Under review at Aerospace Science and Technology},
  year = {2026},
  eprint = {2609.03234},
  archivePrefix = {arXiv},
  primaryClass = {eess.SY},
  url = {https://arxiv.org/abs/2609.03234}
}

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

Extracts causal chains from aviation safety reports and grounds them with physics-based failure quantification and counterfactual analysis, producing risk explanations an operator can check.

BibTeX
@unpublished{pang2026hybrid,
  title = {A Hybrid Framework for Explainable Aviation Risk: Causal Chain Extraction from Safety Reports with Physics-Based Failure Quantification and Counterfactual Analysis},
  author = {Pang, Y. and Tian, Y. and Xie, J. and Zhao, X. and Hu, J. and Jacobsen, H. and Clarke, J.},
  note = {To be submitted to Advanced Engineering Informatics},
  year = {2026}
}

Aerodynamic Effects of Fuselage-Package Separation Distance on UAV Performance

Bhujel, S., Pang, Y., Snyder, P., Tang, C., & Hu, J.

Published in AIAA AVIATION 2026 Forum, 2026

Shows how the separation distance between fuselage and carried package changes UAV aerodynamic performance in delivery configurations.

BibTeX
@inproceedings{bhujel2026aerodynamic,
  title = {Aerodynamic Effects of Fuselage-Package Separation Distance on UAV Performance},
  author = {Bhujel, S. and Pang, Y. and Snyder, P. and Tang, C. and Hu, J.},
  booktitle = {AIAA AVIATION 2026 Forum},
  doi = {10.2514/6.2026-4760},
  year = {2026}
}

An Optimization Formulation for Last-Mile Delivery Using Ring Widget Structure

Liang, J., Pang, Y.*, & Clarke, J.

Working paper, in preparation

Formulates last-mile delivery over a ring-widget network structure for provably efficient routing.

BibTeX
@unpublished{liang2026optimization,
  title = {An Optimization Formulation for Last-Mile Delivery Using Ring Widget Structure},
  author = {Liang, J. and Pang, Y. and Clarke, J.},
  note = {Working paper},
  year = {2026}
}

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.

Published in Journal of Air Transport Management, 2026

arXiv

A machine-learning model that predicts which runway and taxiway exit a landing aircraft will take, giving surface-traffic planners earlier and more reliable information at the world’s busiest airport.

BibTeX
@article{porcayo2026data,
  title = {Data-Driven Runway and Taxiway Exits Prediction of Landing Aircraft: A Case Study at Hartsfield-Jackson Atlanta International Airport},
  author = {Porcayo, A. and Pang, Y. and Thomas, M. and Clarke, J.},
  journal = {Journal of Air Transport Management},
  year = {2026},
  eprint = {2606.11017},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url = {https://arxiv.org/abs/2606.11017}
}

talks

teaching