Nicolas Rault-Wang
BA double major in Applied Math & CS, UC Berkeley ('25) • Post-Bac Researcher, Caltech (Jan 2026 - present) • Incoming PhD Student, MIT EECS (Fall 2026)
I engineer machine-learning systems and computational methods for scientific instruments. Drawing on work at the Berkeley Space Sciences Laboratory and Caltech, my research focuses on the high-speed distributed acquisition systems, computational imaging algorithms, and physics-informed models that transform raw sensor streams into discovery. Whether optimizing distributed arrays for gamma-ray astronomy or isolating transient signals for optical SETI, I build the data systems that bridge bare metal to deep learning.
Selected Work
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Deep Learning for PANOSETIDesigned and implemented the first deep-learning pipeline for the PANOSETI collaboration, achieving 95% classification accuracy and 0.97 average precision in automated interference detection for daily terabyte-scale datasets of wide-field, 20 μs-integration optical/near-IR images.
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High-speed Data Acquisition SystemDeveloped and maintained an ultra-high data-rate (100k frames/sec) C++ acquisition pipeline, integrating an asynchronous gRPC API, leading several extensibility-focused refactors, and establishing a self-hosted hardware-software GitHub Actions CI pipeline to validate fault-tolerance and >99.99% data integrity.
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Terabyte-scale Data PipelineCo-designed and implemented a scalable data-reduction pipeline (Zarr, Ray, Nextflow) for terabyte-scale datasets and real-time classification, leading technical prototyping from a self-administered 4-GPU cluster to San Diego Supercomputer Center facilities.
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Apple-Sponsored RISC-V CPU Design Contest (1st Place)Awarded 1st place (of 18 teams) in a Spring 2025 Apple-sponsored contest with the best PPA metrics over two years: lowest resource utilization, 1.06 CPI, 125 MHz clock. Led microarchitecture of a fully bypassed, 5-stage RISC-V pipeline with speculative execution.