Will Thompson

CV

General Information

Full Name

William H. W. Thompson
Email

wthomps3@uvm.edu
Website

willhwthompson.com
GitHub

WillHWThompson

Education

  • 2022 – Present
    Ph.D. in Complex Systems & Data Science
    University of Vermont
  • 2016 – 2020
    B.A. in Liberal Arts
    St. John’s College, Santa Fe

Experience

  • Summer 2026
    Data Science for Social Good Summer Fellow
    Johns Hopkins University

    • Worked with the City of Baton Rouge on early warning models for blight and structural deterioration risk for city revitalization.

    • Selected from more than 400 applicants.

  • Aug 2022 – Present
    Graduate Research Assistant
    University of Vermont

    • Developed a Fourier-space representation for Bayesian Neural Networks (BNNs) to enable exact, sampling-free inference and learning, bypassing computational bottlenecks of MCMC.

    • Designed non-parametric Bayesian models for contagion processes, enabling the simultaneous joint reconstruction of network topology and spreading dynamics from time-series data.

    • Engineered a Graph Machine Learning (GML) pipeline to infer latent interaction kernels governing stochastic network dynamics.

  • Jun 2025 – Oct 2025
    Graduate Research Intern
    Los Alamos National Laboratory

    • Worked with Andrey Lokhov on inference in graphical models.

    • Developed BPTuning, a high-performance Julia framework for graphical model inference on loopy graphs.

    • Derived a proof of optimality for a method that optimizes effective parameters to cancel out Belief Propagation (BP) approximation errors on cyclic topologies.

    • Implemented a reconstruction-based objective function to serve as a tractable proxy for exact likelihood maximization.

  • Apr 2023 – Jan 2025
    Lead Data Scientist
    Zoodiker

    • Engineered an end-to-end NLP pipeline to process screenplay text, utilizing sentiment analysis and structural extraction algorithms to quantify narrative arcs.

    • Designed anomaly detection heuristics to correlate specific character dynamics with reader engagement deviations.

    • Implemented a full-stack analytics dashboard (SQL/React/Python) to visualize high-dimensional text features.

    • Patent Pending: Filed for novel techniques in automated narrative structural analysis.

  • Jan 2021 – Aug 2022
    Post-Baccalaureate Researcher
    Los Alamos National Laboratory

    • Developed a 200+ unit photon detection system for the Coherent Captain Mills (CCM) particle physics experiment.

    • Enhanced the data acquisition system (DAQ), doubling capacity and improving stability for 10-ton scale data capture.

    • Led the development of detector control software, integrating sensor arrays and automated emergency alerts.

  • Aug 2020 – Dec 2020
    Data Scientist
    MITRE Corporation

    • Applied complex systems science to analyze satellite network structures and mitigate failure cascades.

    • Co-developed Strategy Mining, an open-source genetic programming tool for evolving Agent-Based Models (ABMs).

    • Conducted semantic analysis of judicial documents to improve legal search accuracy.

  • Jun 2018 – Aug 2018
    Undergraduate Research Fellow
    Carnegie Mellon University

    • Executed a research project on the structural analysis of philosophical texts using NLP and Random Walk algorithms; findings published as first author.

  • Jun 2017 – Jun 2018
    Undergraduate Researcher
    Santa Fe Institute

    • Conducted independent research using NLP and machine learning to explore the narrative function of the chorus in Greek tragedies.

Grants & Awards

  • Data Science for Social Good Summer Fellowship, Johns Hopkins University (2026) — $14,000 award; selected from more than 400 applicants. Worked with the City of Baton Rouge on early warning models for blight and structural deterioration.

  • Complexity Award Grant, Vermont Complex Systems Institute, University of Vermont (2026) — $5,000 for modeling strategic lying in Kenyan pastoralists with Eleonora Zanetti (PhD candidate, Washington State University). Supported survey participant compensation and in-country travel for her June–July 2026 fieldwork in Kenya.

  • Second Place, University of Vermont Computing Student Research Day (2023) — Presented research on developing learnable asynchronous models of opinion dynamics.

  • Third Place, University of Vermont Computing Science Research Day (2022) — Awarded for research employing evolutionary algorithms to develop robust facility placements.

  • MITRE Best Paper Incentive Prize (2022) — For ‘The Structure and Dynamics of Modern United States Federal Case Law.’

  • Large Team Distinguished Performance Award, LANL (2022) — Recognized for construction and dark matter search leadership with Coherent Captain Mills Detector.

  • Science Undergraduate Laboratory Internship (SULI) Grant, DOE (2020) — Selective DOE grant for post-baccalaureate research at LANL.

  • Dean’s Award for College Service, St. John’s College (2020) — Recognized for exceptional leadership and dedication.

  • Robert Neidorf Memorial Scholarship (2018)

  • ARIEL Grant — St. John’s College (2017)

Publications (Preprints)

Publications (Peer-Reviewed Journals)

Coherent CAPTAIN-Mills Collaboration

Conference Proceedings

Teaching & Mentorship

  • 2023 – Present
    Graduate Research Mentor
    University of Vermont

    • Directed a research cohort of 5 undergraduate interns over two semesters; managed the development of a Machine Learning pipeline to detect student-authored news content across hundreds of U.S. publications.

    • Mentored summer interns on individual projects in network science and complex systems.

    • Guest Lecturer: Delivered lectures on ‘Introduction to Network Science,’ ‘Stochastic Cellular Automata,’ and ‘Chaos & Fractals’ for graduate and undergraduate courses.

  • Summers 2021, 2022
    Post-Baccalaureate Mentor
    Los Alamos National Laboratory

    • Supervised undergraduate interns during two consecutive summer sessions, providing technical guidance on C++ development and detector hardware calibration.

    • Facilitated onboarding for new researchers, training them on laboratory safety protocols and data acquisition software.

  • May 2019 – May 2021
    Head Senior Lab Assistant
    St. John's College

    • Directed the setup and instructional design of a tabletop quantum optics lab and trained junior assistants in experimental protocols.

Selected Talks

  • Invited Seminars

    • The Emergence of Inequality with Two Models of Social Dynamics — Dartmouth College Applied Math Seminar Series, Hanover, NH (Oct 2023)

    • Understanding Polarization with the Non-Linear Voter Model on Higher Order Networks — Vermont-KIAS Workshop on Higher Order Interactions, Burlington, VT (Sept 2023)

    • Searching for Light Dark Matter with Coherent Captain Mills — University of New Mexico NUPAC Colloquia Series, Albuquerque, NM (May 2021)

  • Conference Presentations

    • The Parliament of Thoughts in Language Models — parallel talk with Aviral Chawla and Jean-Gabriel Young, IC2S2 2026, Burlington, VT (Jul 2026)

    • Multiple latent preference orderings in language models — scheduled oral presentation, contributed session, Conference on Complex Systems (CCS) 2026

    • Inferring Interaction Kernels In Stochastic Opinion Dynamics Models — AMS Spring Eastern Sectional Meeting, Boston, MA (Mar 28, 2026; special session on Mathematical Modeling of Ecological and Evolutionary Dynamics)

    • Sensitivity of epidemic forecasts with statistical condition estimation — Dynamics Days 2025, Denver, CO (Poster)

    • Inferring Interaction Kernels In Stochastic Opinion Dynamics Models — NetSci 2024, Québec City (Jun 2024)

    • Understanding Polarization In the Higher Order Non-Linear Voter Model — Dynamics Days 2024, Davis, CA (Jan 2024)

    • The Emergence of Polarization in the Non-Linear Voter Model on Higher Order Networks — Joint Mathematics Meeting 2024, San Francisco, CA (Jan 2024)

    • Evolving Robust Facility Placements — GECCO 2023, Lisbon, Portugal (Jul 2023)

Service & Outreach

  • Peer Reviewer: Physical Review E (2024), NPJ Complexity (2024)

  • Public Outreach: Delivered talks on ‘The Mathematics of Making Coffee’ and Percolation theory at Vermont State University (2024), Vermont Science Olympiad (2024), and UVM MATHCOUNTS (2024)

Technical Skills

  • Languages: Julia, Python, C++, SQL, Bash, LaTeX

  • Machine Learning & Data: PyTorch, Flux.jl, NetworkX, SciPy, Pandas, Scikit-learn

  • Methods: Bayesian Inference, Graph Neural Networks, MCMC, Agent-Based Modeling, NLP

  • Tools: Git, Docker, UNIX/Linux, High-Performance Computing (HPC)

References

Peter Dodds

Professor, Complex Systems Center, University of Vermont — pdodds@uvm.edu
Richard Van De Water

Staff Scientist, P-2 Pure and Applied Physics, Los Alamos National Labs — vdwater@lanl.gov
Simon DeDeo

Assistant Professor, Carnegie Mellon University — sdedeo@andrew.cmu.edu
Matthew Koehler

Applied Complexity Scientist, MITRE Corporation — mkoehler@mitre.org