Robert Pearce

Relevant coursework: Machine Learning, Computational Linear Algebra, Statistics, Operating Systems, Compilers, Databases, Cloud Computing.

• Engineered a neural network emulator for the kSZ angular power spectrum, achieving <5% prediction error while reducing inference time from hours of simulation to near real-time.
• Executed 1,000+ cosmological simulations on the Bridges-2 supercomputer via SLURM, automating 4D parameter sweeps across distributed compute nodes.
• Built a data pipeline to generate kSZ temperature maps and compute angular power spectra (C_ℓ) via Fourier transforms, extracting statistical observables of the Epoch of Reionization.
• Applied Latin Hypercube Sampling to efficiently explore multi-dimensional parameter spaces, reducing computational cost versus exhaustive grid search.
• Processed raw simulation outputs into ML-ready datasets, compressing storage requirements by 75%.

Identified and resolved 8 accessibility issues in iOS components, improving compliance with WCAG 2.1 AA standards and enhancing usability for visually impaired users.

Led financial operations and built the club’s website. Earned AWS Academy Cloud Foundations certification covering EC2, VPC, S3, IAM, and RDS.

3+ years of teaching across multiple roles: SI Math Leader, Math Peer Mentor, Code Coach, and STEM Mentor. Supported 80+ students per semester in Calculus, Linear Algebra, and Statistics. Coached game development in Unity/Godot (C#, GDScript) and led robotics workshops for 200+ students using Sphero and Arduino platforms.

• Architected and published a pip-installable Python package for end-to-end scientific ML workflows: data ingestion, feature engineering, model training, and evaluation.
• Developed reusable pipelines for HDF5 data reduction and power spectrum computation, compressing intermediate data by 75% for large-scale experiments.
• Implemented a neural network emulator with Bayesian uncertainty quantification (in development), achieving <5% error on held-out simulations with near real-time inference.
• Integrated hyperparameter optimization via Ray Tune, automated testing with PyTest, and CI/CD through GitHub Actions for reproducible experimentation.