• Co-authored paper accepted at AISTATS 2025, “Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space”

• Implemented variational sequential Monte Carlo (VCSMC) methods in Python/PyTorch for Bayesian phylogenetic

inference in hyperbolic space, and markedly improved inference accuracy compared with prior Euclidean approaches

• Designed and executed large-scale training experiments (100+ runs) on cloud VMs, improving model convergence and empirical performance on high-dimensional inference tasks

• Visualized metrics using Weights & Biases to track training hyperparameter allocations with respect to model performance

• Spearheaded the development of Deep Neural Networks by leveraging Python, Scikit-Learn, & TensorFlow

• Achieved an impressive 92.63% validation accuracy for a biomechanics project

• Presented research findings at the Michigan Space Grant Consortium (Oct 2021)

• Presented research findings at the Midwest American Society Of Biomechanics Conference (Sep 2021)ference (Sep 2021)

• Managed comprehensive test management for apps and websites, ensuring high-quality software products

• Conducted validation of software across diverse platforms, resulting in a notable 13% increase in user satisfaction

• Facilitated effective communication between developers and end-users, enhancing overall user experience

• Produced a 13% increase in user satisfaction