This project introduces Unique+, a descriptive metric designed to quantify stylistic differentiation in MLB pitch profiles using k-nearest neighbors in standardized pitch shape space. The model constructs high-dimensional pitcher season feature vectors from Statcast derived pitch characteristics and measures structural distance relative to same handedness peers. While Unique+ only adds modest explanatory power for run prevention beyond Stuff+ and velocity, the framework generates interpretable similarity relationships that can inform player comps, development strategy, and bullpen or rotation construction.

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(Inprogress) This project studies uncertainty-aware forecasting of MLB pitcher value by modeling next-season fWAR as a distributional prediction problem using quantile regression and conformal calibration. It compares nested feature sets, moving from season-level statistics to basic pitch-level characteristics, to evaluate how added pitch information affects the sharpness and quality of prediction intervals. Preliminary results indicate that incorporating basic pitch-level features leads to narrower prediction intervals and lower Winkler scores, with modest improvements in median error, particularly for starting pitchers.

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This project evaluates the effectiveness of a traditional neural network versus a graph neural network for predicting the bioactivity of PFAS molecules, which are environmentally persistent chemicals linked to serious health risks. Using a large PFAS bioassay dataset, the models are compared under different precision–recall tradeoffs, showing that neural networks are better at minimizing false positives while graph neural networks excel at identifying active molecules when recall is prioritized. The results highlight how model choice should depend on the specific scientific or regulatory goal of PFAS screening.

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This project developed and optimized a YOLO-based multi-object tracking pipeline for analyzing NIF target capsules during chemical vapor deposition, consolidating previously separate processing stages into a single streamlined workflow. Final results show significant performance gains, with up to 2.3× speedup, alongside improved logging, error detection, and metric extraction. These improvements enabled reliable analysis of longer, higher-quality videos and supported more consistent evaluation of capsule behavior across test runs.