Zhuofu Li

Ph.D. Candidate · University of Washington

Zhuofu (Chester) Li

Dual Ph.D. Candidate in Astrophysics & Astrobiology

I develop 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 am a dual Ph.D. candidate in Astrophysics and Astrobiology at the University of Washington, working at the intersection of astronomy, statistics, and artificial intelligence.

My research turns massive astronomical datasets into physical insight — applying machine learning, Bayesian inference, and large-scale simulation to questions ranging 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 am 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 have helped lead student scientific communities — co-founding the Data Science Society at UW and serving as President of UCLA's Astronomical Society — and I am an active astrophotographer drawn to the same skies I study.

15k+
Asteroids in the orbit pipeline
1,000
Orbital clones per object
4–4.8 h
Trojan spin-barrier limit
150k+
Particle-spray simulations

Research

Selected research

Three threads of 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

A high-performance pipeline that integrates asteroid orbits to identify objects temporarily captured into co-orbital resonance with Jupiter. For each of 15,000+ asteroids, it generates 1,000 statistical clones to propagate observational uncertainty, then runs N-body simulations across millions of years to trace long-term dynamical evolution. Classification algorithms flag anomalous trajectories, enabling robust detection of these short-lived co-orbital states.

15k+
Asteroids integrated
1,000
Clones per object
Myr-scale
N-body integration
Python N-body simulation HPC clusters Anomaly detection 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

Rubin Observatory & LSST

Co-author on the first Rubin Observatory paper to characterise interstellar comet 3I/ATLAS, recovering pre-discovery detections in commissioning data — with continued involvement in the LSST Solar System Science Collaboration.

Chandler et al., ApJL 2026

Astronomy AI at Scale

Machine learning purpose-built for astronomy — simulation-based inference, deep learning, and anomaly detection that turn survey-scale datasets into physical understanding.

Cataclysmic Variables & Compact Binaries

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.

Margot et al., AJ 2023

Publications

Peer-reviewed and preprint work

From first-author leadership to large survey collaborations — every paper is openly available on arXiv.

  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. First author

    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 ·

  3. 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 ·

  4. 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 ·

  5. 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 ·

  6. 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 ·

  7. 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

Background

Education & honors

  1. 2022 – Present

    Dual Ph.D., Astrophysics & Astrobiology

    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

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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