Robert Pearce

Awards: Dean’s Honor List Medallion

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

Awards: Nominated for Outstanding Student in Mathematics

• Ported the kSZ zreion cosmological simulation code from CPU-based OpenMP to CUDA for GPU clusters, modernizing the Fortran codebase originally written for Bridges-2.
• Profiled the existing OpenMP routines to identify bottlenecks and prioritize the kernels with the most to gain from GPU acceleration.

• 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%.

• Selected from a competitive applicant pool to collaborate with a four-person team across three U.S. and South Korean universities, delivering a computational pathology study in four weeks (GitHub).
• Built an end-to-end pipeline in Python and PyTorch, covering WSI segmentation, patching, and frozen UNI v2 feature extraction through attention-based MIL and Cox survival modeling, over 875 TCGA glioma patients (1,700+ slides) on a SLURM GPU cluster.
• Benchmarked MC-Dropout, Deep Ensembles, and SNGP for uncertainty quantification under 5-fold patient-level cross-validation; all three tied on discrimination (C-index 0.76), while SNGP delivered the most informative uncertainty (selective-prediction area 0.809) and best calibration.
• Achieved SNGP's calibrated uncertainty in a single forward pass, versus 100 stochastic passes for MC-Dropout and 5 separately trained models for Deep Ensembles, cutting inference cost substantially at equal accuracy.
• Diagnosed and fixed a per-fold risk-scaling artifact that had made SNGP appear worst-in-class, then built confound diagnostics isolating how much of each method's uncertainty was independent of its own risk score.

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.

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, 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.

• Uncertainty benchmark (MC-Dropout vs. Deep Ensembles vs. SNGP) for an ABMIL + Cox survival model on TCGA glioma whole-slide images, built on frozen UNI v2 features with 5-fold patient-level cross-validation.
• SNGP matched the others on discrimination (C-index 0.76) while giving the most informative uncertainty and best calibration in a single forward pass.