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Researcher profile

Jiayi Huang

Jiayi Huang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Biological PhysicsComputational Engineering, Finance, and Scienceeess.SPMachine Learningmath.NANumerical Analysisphysics.flu-dynphysics.med-ph

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Published work

3 published item(s)

preprint2026arXiv

A primer on treatment planning aspects for temporally modulated pulsed radiation therapy

Temporally modulated pulsed radiotherapy (TMPRT) delivers conventional fraction doses of radiation using temporally separated pulses of low doses (<30 cGy) yielding fraction-effective dose rates of around 6.7 cGy/min with the goal to exploit tumor radiation hypersensitivity, which was observed in both, preclinical models and in human clinical trials. To facilitate TMPRT, volumetric modulated arc therapy (VMAT) and 3D-CRT planning techniques were developed following the guidelines of the proposed NRG CC-017 trial. Plans were evaluated with respect to homogeneity, conformality, and adherence to dose constraints. Deliverability of plans was assessed using in-phantom measurements for absorbed dose accuracy at low dose rates and using EPID for isodose verification. For VMAT only single arc plans were found to be acceptable due to otherwise unacceptably heterogeneous field doses, while for dynamic conformal arcs machine limtations on the number of monitor units per degree require the use of partial arcs for each pulse. Delivery of plans at low dose rates (< 100 MU/min) was accurate with high Gamma pass rates on modern LINACs and moderate pass rates on legacy LINACs, in line with their general performance. Generally, VMAT is preferred to achieve optimal homogeneity, conformality, and organ-at-risk sparing, while the use of 3D-CRT can increase the availability of TMPRT for more patients and clinics.

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preprint2026arXiv

Foundations of Reliable Inference: Reliability-Efficiency Co-Design

Reliable inference requires that artificial intelligence (AI) models provide trustworthy uncertainty estimates, not merely accurate predictions. Recent advances in Bayesian learning have made significant progress toward this goal, and growing concerns about computational overhead have jointly shifted the design criterion from reliability alone to the co-design of reliability and efficiency, i.e., reducing computational overhead while preserving trustworthy uncertainty quantification. This thesis develops a unified framework from two perspectives to address the central question: can we efficiently perform reliable inference?

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preprint2022arXiv

Vascular fluid-structure interaction: unified continuum formulation, image-based mesh generation pipeline, and scalable fully implicit solver technology

We propose a computational framework for vascular fluid-structure interaction (FSI), focusing on biomechanical modeling, geometric modeling, and solver technology. The biomechanical model is constructed based on the unified continuum formulation. We highlight that the chosen time integration scheme differs from existing implicit FSI integration methods in that it is indeed second-order accurate, does not suffer from the overshoot phenomenon, and optimally dissipates high-frequency modes in both subproblems. We propose a pipeline for generating subject-specific meshes for FSI analysis for anatomically realistic geometric modeling. Unlike most existing methodologies that operate directly on the wall surface mesh, our pipeline starts from the image segmentation stage. With high-quality surface meshes obtained, the volumetric meshes are then generated, guaranteeing a boundary-layered mesh in the fluid subdomain and a matching mesh across the fluid-solid interface. In the last, we propose a combined suite of nonlinear and linear solver technologies. Invoking a segregated algorithm within the Newton-Raphson iteration, the problem reduces to solving two linear systems in the multi-corrector stage. The first linear system can be addressed by the algebraic multigrid (AMG) method. The matrix related to the balance equations presents a two-by-two block structure in both subproblems. Using the Schur complement reduction (SCR) technique reduces the problem to solving matrices of smaller sizes of the elliptic type, and the AMG method again becomes a natural candidate. The benefit of the unified formulation is demonstrated in parallelizing the solution algorithms as the number of unknowns matches in both subdomains. We use the Greenshields-Weller benchmark as well as a patient-specific vascular model to demonstrate the robustness, efficiency, and scalability of the overall FSI solver technology.

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