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Biography

Yadi Cao develops field-ready machine learning for science and engineering: models and agents that hold up not just on clean benchmarks, but under the constraints real research and real hardware impose. His research centers on two directions: generalizable surrogate models that span different geometry, physics, and working conditions without retraining, addressing the data efficiency gap between scientific computing and fields like computer vision or NLP; and cost-aware scientific agents that operate under realistic resource constraints, with benchmarks and post-training methods that account for deployment cost.

Before joining UCF, Cao was a postdoctoral researcher at University of California, San Diego. He received his doctorate in computer science from the University of California, Los Angeles. His work has seen direct adoption in practice: BSMS-GNN enables learning turbomachinery simulations on meshes with over one million nodes and has been adopted by Rolls-Royce, while TGLF-WINN delivers a 48x speedup for fusion transport simulations and is integrated into General Atomics’ tokamak modeling pipeline.