CV

For a downloadable pdf version, see here.

Education¶

University of California, Berkeley PhD Candidate, Statistics | August 2021 – Expected May 2026

• Advisors: Jon McAuliffe (Dept. of Statistics, UC Berkeley; The Voleon Group), Fernando Pérez (Dept. of Statistics, UC Berkeley)

The College, University of Chicago BA, Physics & Statistics (Double Major) | September 2017 – June 2021

• Graduated Magna Cum Laude

Research Projects¶

CASCADE Group, Lawrence Berkeley National Laboratory (LBNL)¶

Project: Quantifying uncertainty in the rarity of extreme multivariate weather and climate events (Ongoing) People: Jon McAuliffe, Michael Wehner

• Develop nonparametric, data-driven methods to quantify uncertainty in estimates of rare bivariate compound events in random systems.

• Applied to extreme weather/climate events with bivariate drivers (e.g., wind speed and dryness in wildfires).

• Utilize high-performance computing workflows and code optimization strategies to run simulations efficiently on NERSC supercomputers.

Pérez Group, University of California, Berkeley¶

Project: Associating storm characteristics with extreme impacts of Antarctic atmospheric rivers (Ongoing) People: Michelle Maclennan, Fernando Pérez, Jon McAuliffe

• Apply extreme value statistical methodology to quantitatively explore drivers of the most extreme atmospheric river-induced precipitation and temperature events in Antarctica.

• Develop specialized clustering strategies to catalog storm events.

• Create cloud-based and local workflows to extract weather/climate conditions from terabytes of MERRA-2 reanalysis data.

• Promote GitHub-based workflows to present and document data products using cutting-edge open science software tools.

Project: Simulating impact of extreme temperature swings on ice melt (Ongoing) People: Fernando Pérez

• Integrate physical and statistical models to explore the time-to-melt distribution of an idealized block of ice subjected to stochastically generated temperatures.

• Analyze varying extremal properties to inform how global warming-induced climate change may exacerbate polar melt.

Project: Exploring limitations of current geoscience software tools for sparse datasets People: Fernando Pérez | Jan 2023 – May 2023

• Tested xArray’s compatibility with sparse geoscience datasets.

• Communicated with xArray developers on improving the package’s sparse data API.

Data and Analytics (DAS) Group, NERSC, LBNL¶

Project: Software pipelines for AI-ready data at NERSC People: Wahid Bhimji, Chris Harris

• Developed software to catalog AI-ready datasets at NERSC for public use.

• Provided distributed data loaders and tested new job monitoring software.

• Goal: Lower technical barriers to running AI jobs and make data easier to find at NERSC.

Weare Group, University of Chicago/NYU Collaboration¶

Project: Analysis of a stochastic atmospheric blocking model using transition path theory People: Justin Finkel, Dorian Abbot, Jonathan Weare | June 2020 – Aug 2021

• Applied transition path theory, a theoretical math framework for analyzing dynamical systems, to stochastic atmospheric models.

• Predicted and characterized the dynamics of atmospheric blocks (phenomena causing heat waves, droughts, or floods).

Cosmological Physics and Advanced Computing Group, Argonne National Laboratory¶

Project: Validating physicality of synthetic galaxy catalogs People: Joe Hollowed, Salman Habib | June 2018 – Sept 2018

• Programmed tests to evaluate the realism of the collaboration’s synthetic galaxy catalogs, ensuring simulated galaxies followed physical laws.

Project: Quantifying uncertainty in deep-learning estimates of galaxy-galaxy strong lensing parameters People: Nesar Ramachandra, Salman Habib | Sept 2018 – Aug 2021

• Quantified uncertainty in deep learning estimates of physical parameters of galaxy-galaxy strong lenses using strategies such as dropout and Bayesian neural networks.

International Institute of Nanotechnology REU Program, Northwestern University¶

Project: Fabricating nanoscale devices to measure signatures of superconductivity People: Patrick Krantz, Venkat Chandrasekhar | June 2019 – Aug 2019

Leadership Activities¶

Statistics Graduate Student Association (SGSA), UC Berkeley Co-President | May 2023 – May 2024

• Directed student-led committees to undertake initiatives to improve graduate student life (social events, speaker engagements, etc.).

• Organized orientation and admit visit days.

• Communicated and responded to concerns raised by the department graduate student body to staff/faculty.

Emmett Till Math and Science Academy, Chicago Public Schools Teaching Aide | October 2017 – June 2021

• Worked with the same middle school math teacher for four years of undergrad.

• Helped strategize lesson plans for students needing extra assistance.

• Led small-group tutoring sessions after school for 8th-grade students taking Chicago’s Selective Enrollment test.

Teaching¶

University of California, Berkeley¶

• Collaborative and Reproducible Data Science (Stat 159/259): TA/GSI (Fall 2025)

• Berkeley Statistics Computational Skills Workshop: Tutor (August 18-22, 2025; August 19-23, 2024)

• Introduction to Time Series (Stat 153/248): TA/GSI (Spring 2025)

• Statistical Models: Theory and Application (Stat 215B): TA/GSI (Spring 2025)

• Concepts of Statistics (Stat 135): TA/GSI (Spring 2022; Fall 2023)

• Principles & Techniques of Data Science (Data 100): TA/GSI (Fall 2022)

University of Chicago¶

• Statistical Theory and Methods I (STAT 24400): Grader (Winter 2021)

Honors¶

• H2H8 Research Grant Awardee (2025)

• Two Sigma PhD Fellowship Recipient (2024)

• Magna Cum Laude, The College, University of Chicago (2021)

• Dean’s List, The College, University of Chicago (2018, 2019, 2020, 2021)

Papers¶

• Kovacs, E., et al. (2022). Validating Synthetic Galaxy Catalogs for Dark Energy Science in the LSST Era. The Open Journal of Astrophysics, 5. Kovacs et al. (2022)

• Madireddy, S., Li, N., Ramachandra, N., Butler, J., Balaprakash, P., Habib, S., Heitmann, K. (2019). A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling. arXiv preprint. Madireddy et al. (2019)

Presentations¶

• Butler J., Maclennan, M., McAuliffe, J., Pérez, F. (2024). Quantifying the association between Antarctic atmospheric river characteristics and their impacts using extreme-value statistics. (Talk) 3rd Antarctic Atmospheric Rivers Group workshop.

• Butler, J., Pérez, F. (2025). Using GitHub for community workflows, from code to publication: Using ARTMIP data as a test case. (Website demo) ARTMIP Future Directions Telecon.

• Butler J., Maclennan, M., McAuliffe, J., Pérez, F. (2024). Quantifying the association between Antarctic atmospheric river characteristics and their impacts using extreme-value statistics. (Poster) AGU Fall Meeting 2024.

• Butler J., McAuliffe, J., Wehner, M. (2023). Quantifying Uncertainty in the Rarity of Extreme Multivariate Weather and Climate Events. (Poster) AGU Fall Meeting 2023.

• Butler, J., Finkel, J., Weare, J. (2020). Analysis of a Stochastic Atmospheric Blocking Model using Transition Path Theory. (Poster) 2020 Midstates Undergraduate Research Symposium.

• Butler, J. (2019). Probing the Nature of Superconductivity in Mechanically-Exfoliated Thin Film MoS2. (Poster) University of Chicago’s 6th Annual Undergraduate Research Symposium.

• Butler, J. (2019). Probing the Nature of Superconductivity in Mechanically-Exfoliated Thin Film MoS2. (Talk) International Institute of Nanotechnology REU Closing Symposium.

Programming Skills¶

• Languages: Python, Julia, R

• R Packages: tidyverse, data.table, optim

• Python Packages: numpy, pandas, xArray, matplotlib, seaborn

• Other Software/Skills: Jupyter, Parallelization, Debugging, Class construction