Nicolas Rault-Wang
UC Berkeley Applied Math & CS undergraduate researching computational imaging, machine learning for scientific instrumentation, and large-scale data systems at the Space Sciences Laboratory.
Research Focus
I develop machine learning systems and computational methods for scientific instrumentation, with expertise spanning computer vision, distributed systems, and hardware-software co-design. My research bridges theoretical mathematics with practical implementation to solve challenging problems in astronomical data analysis and real-time transient detection.
Publications & Presentations
Nanosecond differential timing using inexpensive differential GNSS receivers (DRAFT)
B. Godfrey, W. Liu, N. Rault-Wang, J. Kocz, D. Werthimer
2025 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Jan. 2025, 431–431.
DOI: 10.23919/USNC-URSINRSM66067.2025.10906985Machine learning applications for anomaly and interference detection on PANOSETI data
N. Rault-Wang, Y. Dong, W. Liu, D. Werthimer, J. Maire, and S. Wright
PANOSETI Collaboration Meeting (Plenary Talk), San Diego, CA, United States, Jan. 2025.
DOI: 10.5281/zenodo.17388495Identifying clouds in panoramic SETI data with machine learning
N. Rault-Wang, et al.
2024 Assembly of the Order of the Octopus (Poster Presentation), Green Bank, WV, United States, Aug. 2024.
DOI 10.5281/zenodo.14590904Research Projects
Deep Learning for PANOSETI
Computer VisionDesigned 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 the project’s daily terabyte-scale datasets of wide-field, 20μs-integration optical/near-IR images.
High-speed Data Acquisition System
SystemsDeveloped 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 full GitHub Actions CI pipeline to validate fault-tolerance and >99.99% data integrity.
Terabyte-scale Data Pipeline
Distributed SystemsCo-designed and implemented a scalable data reduction pipeline (TensorStore, Zarr, Dask) for terabyte-scale datasets, leading technical prototyping from a self-administered 12TiB BeeGFS cluster to San Diego Supercomputer Center HPC facilities.
Unsupervised Anomaly Detection
Computer VisionProposed and prototyped an unsupervised anomaly detector using a β-Variational Autoencoder, capable of clustering Cherenkov events, noise, and stellar signals based on low-dimensional latent embeddings.
Education
University of California, Berkeley
Aug 2021 – Dec 2025Bachelor of Arts, double major in Applied Mathematics and Computer Science
GPA: 3.88
Relevant Coursework:
Advanced Coursework and Projects
CS 180: Computer Vision & Computational Photography
The following are write-ups from my CS 180 projects, each of which reproduces results from key papers in computer vision.
EECS 151: Introduction to Digital Design and Integrated Circuits
Awarded 1st Place in a Spring 2025 Apple-sponsored RISC-V CPU design contest in a team with Neel Gajare, out of 18 two-person teams.
We achieved the highest figure-of-merit in the FPGA category over the past 2 years with a compact design featuring a 1.06 CPI and 125 MHz clock frequency on a Xilinx 7 Series FPGA.
I led the design of advanced optimizations and Verilog implementation, resulting in a fully bypassed, 5-stage RISC-V pipeline with speculative execution provided by a branch target buffer. See our write-up at the link below!
Get In Touch
I'm currently exploring PhD opportunities in computational imaging and machine learning systems. Feel free to reach out if you're interested in collaboration or discussion.