Zhuofu Li

Ph.D. Candidate · University of Washington

Zhuofu (Chester) Li

Ph.D. Candidate in Astrophysics

I build data-driven and machine-learning methods to study the small bodies of our Solar System, dark matter, and the next generation of large astronomical surveys.

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About

Bridging astrophysics, statistics, and machine learning

I'm a Ph.D. candidate in Astrophysics at the University of Washington, working at the intersection of astronomy, statistics, and artificial intelligence.

My research turns large astronomical datasets into physical understanding, using machine learning, Bayesian inference, and large-scale simulation on questions that range from the dynamics of small Solar System bodies to the nature of dark matter. Alongside the science, I build the high-performance computing pipelines that make this analysis possible at survey scale.

I earned my dual B.S. in Astrophysics and Geophysics from UCLA with Departmental Highest Honors, and I'm completing a dual M.S. in Astrophysics and Statistics alongside my doctoral work. At UW I also teach ASTR 324 — Astrostatistics & Machine Learning in Astronomy and ASTR 302 — Python for Astronomy. Beyond research, I've helped lead student scientific communities — co-founding the Data Science Society at UW and serving as President of UCLA's Astronomical Society — and I'm an active astrophotographer, drawn to the same skies I study.

Background

Education & honors

  1. 2022 – Present

    Ph.D., Astrophysics

    University of Washington, Seattle

    Advanced Data Science Track

  2. 2022 – Present

    Dual M.S., Astrophysics & Statistics

    University of Washington, Seattle

    Machine Learning & Big Data Track

  3. 2018 – 2022

    Dual B.S., Astrophysics & Geophysics

    University of California, Los Angeles

    Departmental Highest Honors

Salutatorian — Dept. of Earth, Planetary & Space Sciences

UCLA · 2022

Chancellor's Service Award

UCLA · 2022

Astronomy Summer Undergraduate Research Fellowship

Caltech · 2021

Research

Selected research

Three threads run through my doctoral work — small bodies of the Solar System, time-domain astronomy, and the nature of dark matter — each built on large datasets and scientific computing.

01

Temporary Jovian Co-orbitals

Active

2025 – Present  ·  Advisor: Prof. Sarah Greenstreet

An automated, scalable pipeline that analyzes every qualifying near-Jupiter object in the JPL Small-Body Database. For each of 14,801 objects it generates 1,000 covariance clones to propagate orbit uncertainty, runs 0.5-Myr N-body integrations, and classifies each object as a long-term stable Trojan, a transient co-orbital, non-resonant, or insecure. A fine-cadence libration stage then measures the libration period, amplitude and center of every resonant object and assigns it to the L4, L5, horseshoe or quasi-satellite state — yielding the largest homogeneous catalog of Jupiter Trojan libration elements to date.

14,801
Objects analyzed
1,000
Clones per object
14,541
Trojan libration elements
Python N-body simulation HPC clusters Resonant dynamics Orbital dynamics
02

Rotation Periods of Jupiter Trojans

First author · Icarus 2025

2022 – 2024  ·  Advisor: Prof. Željko Ivezić

First-author time-domain study of Jupiter Trojan asteroids using ZTF light curves. Statistical filtering and noise-correction pipelines, paired with period-finding algorithms and cross-validation against external catalogs, recover reliable rotation periods for 216 Trojans — 80 of them previously uncharacterized. Periods span 4.6–447.8 hours and reveal a 4–4.8 h spin barrier above ~10 km, implying a mean rubble-pile density of ~0.52 g/cm³ and a higher ice fraction than main-belt asteroids.

216
Trojans characterized
80
New rotation periods
4–4.8 h
Spin-barrier limit
Time-series analysis Period-finding ZTF Statistical modeling Light curves
Read paper · Icarus 438, 116609 (2025)
03

Inferring Dark Matter Subhalos

Published 2025

2024 – 2025  ·  Advisor: Prof. Nora Shipp  ·  LSST‑DESC

Forecasting dark matter subhalo constraints from stellar streams via implicit likelihood inference. Neural Posterior Estimation recovers a perturber's mass, scale radius, velocity, and encounter geometry directly from stream data. Tested on the ATLAS–Aliqa Uma stream with 150,000+ Lagrange-Cloud particle-spray simulations and conditional normalizing flows, the approach achieves well-calibrated posteriors and 15–20% subhalo-mass uncertainty under ideal LSST coverage.

150k+
Training simulations
6
Parameters inferred
15–20%
Mass uncertainty (LSST)
PyTorch Normalizing flows Transformers Bayesian inference Stellar streams
Read paper · arXiv:2512.07960

Research breadth

Solar System Sciences

Small-body science across the Solar System — first-author rotation periods and the 4–4.8 h spin barrier for Jupiter Trojans in ZTF data, plus co-authorship on Rubin Observatory's first asteroid discoveries and its characterization of interstellar comet 3I/ATLAS — with ongoing work in the LSST Solar System Science Collaboration.

Astronomy AI at Scale

Machine learning built for survey-scale astronomy — simulation-based inference, deep learning, and anomaly detection that turn raw data into physical understanding, including neural posterior estimation with normalizing flows to constrain dark-matter subhalos along stellar streams.

Compact Binary Stars

Systematic search for short-period cataclysmic variables — close interacting binaries with a white-dwarf accretor — in Zwicky Transient Facility time-series data. Presented at the KITP UCSB White Dwarfs program.

SETI & Technosignatures

Co-authored the UCLA SETI group's L-band Green Bank Telescope search for technosignatures around 11,680 nearby stars — large-scale signal processing for candidate identification — and a community review surveying the full breadth of possible technosignatures.

Publications

Peer-reviewed and preprint work

From first-author work to large survey collaborations — each linked to its published version or preprint.

  1. NSF–DOE Vera C. Rubin Observatory Observations of Interstellar Comet 3I/ATLAS (C/2025 N1)

    C. O. Chandler, P. H. Bernardinelli, M. Jurić, et al., incl. Z. (Chester) Li

    The Astrophysical Journal Letters 1001, L35 ·

  2. Lightcurves, Rotation Periods, and Colors for Vera C. Rubin Observatory's First Asteroid Discoveries

    S. Greenstreet, Z. (Chester) Li, D. E. Vavilov, D. Singh, et al.

    The Astrophysical Journal Letters 996, L33 ·

  3. The Search for Technosignatures: a Review of Possibilities

    C. Vidal, B. L. Fields, D. R. Sowinski, et al., incl. Z. (Chester) Li

    arXiv preprint 2605.21093 ·

  4. Estimates of Rotation Periods for Jupiter Trojans with the Zwicky Transient Facility Photometric Lightcurves

    Z. (Chester) Li, Y. A. Chowdhury, Ž. Ivezić, A. Mahabal, A. Heinze, et al.

    Icarus 438, 116609 ·

  5. Forecasting Dark Matter Subhalo Constraints from Stellar Streams using Implicit Likelihood Inference

    T. Nguyen, R. Pei, Z. (Chester) Li, N. Shipp, et al. — LSST Dark Energy Science Collaboration

    arXiv preprint 2512.07960 ·

  6. Deep view of the intracluster light in the Coma cluster of galaxies

    Y. Jiménez-Teja, J. Román, K. HyeongHan, et al., incl. Chester Li

    Astronomy & Astrophysics 694, A216 ·

  7. Radio Jet Feedback on the Inner Disk of Virgo Spiral Galaxy Messier 58

    P. M. Ogle, I. E. López, V. Reynaldi, A. Togi, R. M. Rich, et al., incl. Z. (Chester) Li

    The Astrophysical Journal 962, 196 ·

  8. A Search for Technosignatures Around 11,680 Stars with the Green Bank Telescope at 1.15–1.73 GHz

    J.-L. Margot, M. G. Li, P. Pinchuk, et al., incl. Z. (Chester) Li

    The Astronomical Journal 166, 206 ·

  9. A giant thin stellar stream in the Coma Galaxy Cluster

    J. Román, R. M. Rich, N. Ahvazi, L. V. Sales, Chester Li, G. Golini, et al.

    Astronomy & Astrophysics 679, A157 ·

Toolkit

Technical capabilities

The methods and tools I use to move from raw survey data to calibrated scientific results.

Programming

Python PyTorch TensorFlow NumPy Pandas scikit-learn C++ R SQL Java

Machine Learning

Deep Learning CNNs Transformers Simulation-Based Inference Normalizing Flows Anomaly Detection NLP Reinforcement Learning

Statistical & Quantitative Methods

Bayesian Inference Time-Series Modeling Monte Carlo Methods Stochastic Simulation Optimization Risk Modeling

High-Performance Computing

Parallel Computing GPU Acceleration N-body Simulation Workflow Automation Large-Scale Pipelines

Contact

Let's connect

I'm always glad to talk research, collaborations, and opportunities across astronomy, AI, and data science — whether you're a fellow researcher, a recruiter, or simply curious about the cosmos.

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