Brandon Pardi | Searcharxiv

arXiv2026

Improving Capstone Team Outcomes through Dynamic Skill Matching and Preference Alignment

Team-based projects are a cornerstone of engineering and computing courses, but unstructured team formation often leads to poor project outcomes due to misaligned student interests and inadequate skill coverage. This paper introduces a novel, three-stage methodology for creating effective student teams by integrating student preferences with project skill requirements. In the first stage, students complete a survey to report their project interests and self-assessed skills. Next, a Large Language Model (LLM) analyzes project descriptions to extract the necessary skills for each project's success. Finally, a dynamic assignment algorithm matches students to projects, simultaneously maximizing skill coverage and preference alignment. The algorithm iteratively prioritizes projects with unfulfilled skill needs to optimize team balance. Preliminary evaluations show our approach produces teams with higher skill coverage and better preference satisfaction compared to random or manual assignment approaches. Our approach also overcomes limitations of widely-used tools like CATME Team-Maker, which do not explicitly account for project skill fulfillment. Our findings point toward an effective and customizable strategy for improving student motivation and learning outcomes in project-based courses.

cs.CY↗

arXiv2026

OptiGAN for Crystal Arrays: Physics-Informed Generative Modeling of Optical Photon Transport in PET Detector Arrays

Monte Carlo simulations of optical photon transport are computationally prohibitive for large-scale optical systems including detector arrays and PET systems, restricting their practical use to single-crystal studies. This work presents an enhanced conditional generative adversarial network capable of replacing optical simulations at the crystal array level, extending our previous single-crystal approach to a 3x3 BGO detector array. We introduce Fourier feature encoding and a learnable latent mapping network as the modifications enabling stable training on the array geometry, together with a physics-informed loss term enforcing the unit-sphere state space $S^2$ of the generated propagation directions as a soft constraint. Training data requirements are reduced eight-fold by exploiting the array's symmetry. Performance is benchmarked against GATE10/Geant4 ground truth, using the fluctuations between independent Monte Carlo runs. The enhanced optiGAN achieves similarity values within 3$\sigma$ agreement of the Monte Carlo baseline across all evaluation conditions. An ablation and attribution analysis shows that the physics-informed loss term reduces low-SSIM bin fractions by a factor of 3.6 on the outer crystals, with a localized trade-off at the central crystal, yielding a net 48% reduction over the full array. The model transitions from electron-emission training data to realistic gamma-photon interactions, producing flood maps that reproduce experimental patterns including photopeak clusters and inter-crystal scatter lines. This proof-of-concept demonstrates that a physics-informed generative model can simulate optical photon transport in segmented scintillator arrays at a training and inference cost accessible on a single workstation GPU.

physics.ins-det↗