Research

My work sits at the intersection of computational imaging, physical-layer and distributed systems, and applied ML for the sciences. Below is a partial record of past projects, publications, and coursework.

Research Projects

  • Deep Learning for PANOSETI
    Computer Vision · PyTorch · Real-time Systems · UC Berkeley SSL
    Designed 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.
    [Conference Poster] [Technical Summary]
  • High-speed Data Acquisition System
    Systems · C++ · HASHPIPE · gRPC · UC Berkeley SSL
    Developed 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.
    [System Overview] [gRPC API]
  • Terabyte-scale Data Pipeline
    Distributed Systems · BeeGFS · Dask · HPC · UC Berkeley SSL
    Co-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.
    [Cluster Setup]
  • Unsupervised Anomaly Detection
    Computer Vision · PyTorch · β-VAE · UC Berkeley SSL
    Proposed 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.
    [Write-up]
  • Characterizing Polar Express
    Optimization · Muon Optimizer · Transformer Architectures
    Empirically characterized the Polar Express Muon variant, evaluating its sensitivity to key hyperparameters and the extent to which it stabilizes the attention mechanism in Transformer architectures.
    [View Paper]

Publications & Presentations

  • Nanosecond differential timing using inexpensive differential GNSS receivers
    B. Godfrey, W. Liu, N. Rault-Wang, J. Kocz, D. Werthimer.
    USNC-URSI National Radio Science Meeting (NRSM), Jan. 2025.
    [DOI 10.23919]
  • Machine learning applications for anomaly and interference detection on PANOSETI data
    N. Rault-Wang, Y. Dong, W. Liu, D. Werthimer, J. Maire, S. Wright.
    PANOSETI Collaboration Meeting (Plenary Talk), San Diego, Jan. 2025.
    [DOI 10.5281]
  • Identifying clouds in panoramic SETI data with machine learning
    N. Rault-Wang, et al.
    Assembly of the Order of the Octopus (Poster), Green Bank, WV, Aug. 2024.
    [DOI 10.5281]

Education

University of California, Berkeley Aug 2021 – Dec 2025

Bachelor of Arts in Applied Mathematics and Computer Science (double major) · GPA: 3.88

Deep Neural Networks (CS 182) Machine Learning (CS 189) Computer Vision (CS 180) Optimization Models (EECS 127) Digital Signal Processing (EE 123) Probability Theory (DATA 140) Real Analysis (MATH 104) Complex Analysis (MATH H185) Operating Systems (CS 162) Computer Architecture (CS 152) Digital Design & ICs (EECS 151) Quantum Mechanics (PHYSICS 137A)

Class projects and independent work (CS180, EECS151 CPU design) are on the Projects page.