Portfolio item number 2
Short description of portfolio item number 2 
Short description of portfolio item number 2 
Published in Applied Soft Computing, 164, 111934, 2024
A hybrid large-neighbourhood-search and evolutionary method that established new lower bounds on ten hard benchmark instances.
Recommended citation: Jitao Xu, H. Li, M. Yin. (2024). "Finding and Exploring Promising Search Space for the 0-1 Multidimensional Knapsack Problem." Applied Soft Computing, 164, 111934.
Download Paper
Published in Proceedings of the AAAI Conference on Artificial Intelligence, 39(11), 2025
A learning-based framework that predicts which variable ordering heuristic to apply at each decision point in a CSP solver. AAAI 2025.
Recommended citation: Jitao Xu, Y. Wu, H. Li, M. Yin. (2025). "Prediction-Based Adaptive Variable Ordering Heuristics for Constraint Satisfaction Problems." Proceedings of the AAAI Conference on Artificial Intelligence, 39(11), 11390.
Published in 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI), 2025
Generative surrogate models that make event-level analysis tractable in hadronic physics.
Recommended citation: T. Alghamdi, Jitao Xu, N. Ramachandra, N. Sato, Y. Li. (2025). "Towards an Event-Level Analysis in Hadronic Physics Using Generative AI-Based Surrogates." 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI).
Published in Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2026), Main Track, 2026
Diffusion-based active inference for inverse problems where the prior information available is incomplete. Accepted to the main track of IJCAI-ECAI 2026.
Recommended citation: Jitao Xu, N. Sato, Y. Li. (2026). "Active Diffusion-Based Inference for Ill-Posed Inverse Problems Under Incomplete Priors." Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2026), Main Track.
Published in Conference on Physics and AI (PAI26), 2026
A conditional diffusion model that learns transverse momentum distributions directly from raw scattering events, without an intermediate binning or unfolding step.
Recommended citation: Jitao Xu, C. Cocuzza, K. Braga, D. Lersch, N. Sato, Y. Li. (2026). "Learning Transverse Momentum Distributions from Raw Scattering Events via Conditional Diffusion." Conference on Physics and AI (PAI26).
Published in arXiv preprint arXiv:2605.06994, 2026
A neural network parameterisation of generalized parton distributions. arXiv preprint.
Recommended citation: Jitao Xu, H. Jang, Z. Panjsheeri, G. W. Chern, Y. Li, S. Liuti, D. Adams, et al. (2026). "Neural Network Representation of Generalized Parton Distributions (NNGPD)." arXiv preprint arXiv:2605.06994.
Download Paper
Undergraduate course, Old Dominion University, Department of Computer Science, 2024
Teaching assistant, Fall 2024. Supported 40+ undergraduates through process management, memory allocation, and concurrency.
Undergraduate course, Old Dominion University, Department of Computer Science, 2025
Teaching assistant, Spring 2025. Supported instruction in HTML5, CSS3, JavaScript, and responsive web design.
Undergraduate course, Old Dominion University, Department of Computer Science, 2025
Invited by the course instructor to deliver a full lecture to an undergraduate section, Spring 2025.