Johns Hopkins University
M.S.E. Computer Science (Thesis Track)
Robert Pearce
M.S.E. Computer Science
Johns Hopkins University
B.S. Computer Science, University of Nevada, Las Vegas
Abstract
Computer science student and machine learning researcher focused on scientific ML, uncertainty quantification, and high-performance computing.
I build end-to-end ML systems, from simulation data pipelines on HPC systems to Python packages for Bayesian uncertainty quantification.
Seeking Summer 2027 ML engineering internships.
1.Education
University of Nevada, Las Vegas
B.S. Computer Science, Minor in Mathematics (GPA: 3.77/4.0)
Relevant coursework: Machine Learning, Computational Linear Algebra, Statistics, Operating Systems, Compilers, Databases, Cloud Computing.
Awards: Dean’s Honor List Medallion
College of Southern Nevada
Associate of Business (GPA: 3.77/4.0, High Honors)
Awards: Nominated for Outstanding Student in Mathematics
2.Publications
In preparation, 2026.
3.Experience
Machine Learning Researcher
LEADS Lab (Dr. Paul La Plante) — University of Nevada, Las Vegas
- 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 (Cℓ) 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%.
Open-Source Contributor
The Quorum Programming Language
Identified and resolved 8 accessibility issues in iOS components, addressing issues related to WCAG 2.1 AA accessibility guidelines and enhancing usability for visually impaired users.
AWS Cloud Club — Treasurer
University of Nevada, Las Vegas
Led financial operations and built the club’s website. Earned AWS Academy Cloud Foundations certification covering EC2, VPC, S3, IAM, and RDS.
Teaching & Mentoring
UNLV, College of Southern Nevada, The Coder School
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.
4.Projects
reionemu — ML Emulation Package
Python | PyTorch | Ray Tune | HDF5 | PyTest | GitHub Actions
- 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, 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.
5.Technical Skills
Languages:
Python, C/C++, Rust, SQL, Java, HTML/CSS/JavaScript
ML & Data:
PyTorch, TensorFlow, Ray Tune, NumPy, HDF5, Bayesian Neural Networks
Infrastructure:
CUDA, OpenMP, SLURM, Git, GitHub Actions, PyPI, AWS (EC2, S3, IAM), Docker
Methods:
Uncertainty Quantification, Hyperparameter Optimization, Latin Hypercube Sampling, Fourier Analysis
6.Certificates & Training
Certificates
- OASiS Foundations Program (April 2026) — University of Cincinnati semiconductor micro-credential covering IC fabrication, metrology, cleanroom & chemical safety.
- AWS Academy Cloud Foundations (November 2025) — AWS architecture, security, compute, storage, networking, and managed services.
- Harvard CS50 Python (July 2024) — Functions, OOP, unit tests, file I/O, regular expressions.
- IBM Data Structures & Algorithms (C++) (December 2023) — Algorithm design, data structures, and problem solving.
Workshops & Lectures
- Fine-Tuning LLMs with Domain-Specific Datasets (April 2026) — LoRA-based adaptation and Hugging Face tools.
- SDSC Data Storage and File Systems (April 2026) — Distributed systems (NFS, Lustre, Ceph), I/O performance.
- ACES: GPU Programming (March 2026) — CUDA C/C++, GPU architecture, memory management, parallel execution.
- Architecting Reproducible Science (March 2026) — Python packaging, testing, automation for HPC workflows.