CV

For a downloadable pdf version, see here.

Education¶

University of California, Berkeley
Statistics (PhD Candidate), Begin August 2021, Expected May 2026
Advisors: Jon McAuliffe (Department of Statistics, UC Berkeley; The Voleon Group),
Fernando Pérez (Department of Statistics, UC Berkeley)

The College, University of Chicago
Physics, Statistics (BA; double major), Begin September 2017, Graduated June 2021

Research Projects¶

*ongoing
CASCADE Group, Lawrence Berkeley National Laboratory (LBNL)
Quantifying uncertainty in the rarity of extreme multivariate weather and climate events*
People: Jon McAuliffe, Michael Wehner

• Develop nonparametric, data-driven method to quantify uncertainty in estimates of rare bivariate compound events in random systems, with application 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 as efficiently as possible on NERSC supercomputer

Pérez Group, University of California, Berkeley
Associating storm characteristics with extreme impacts of Antarctic atmospheric rivers*****
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 strategy to catalog storm events, and develop both 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 modern open science software tools (example site; example GitHub repo)

  Simulating impact of extreme temperature swings on ice melt*****
  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 with varying extremal properties, informing how global warming-induced climate change may exacerbate polar melt

  *Exploring limitations of current geoscience software tools for sparse datasets*  
  	*People: Fernando Pérez;* (1/2023 – 5/2023)

• Testing xArray’s compatibility with sparse geoscience datasets and communicate with developers of xArray on improving the package’s sparse data API

Data and Analytics (DAS) Group, NERSC, LBNL
Software pipelines for AI-ready data at NERSC
People: Wahid Bhimji, Chris Harris

• Develop software to catalog AI-ready datasets at NERSC for public use, provide distributed data loaders, and test new job monitoring software, all with the goal of making it easier to find data for AI jobs at NERSC and lower technical barriers to running jobs on such data

Weare Group, University of Chicago/NYU Collaboration
Analysis of a stochastic atmospheric blocking model using transition path theory
People: Justin Finkel, Dorian Abbot, Jonathan Weare; (6/2020 – 8/2021)

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

• Predict and characterize dynamics of atmospheric blocks, a phenomenon that can hold weather systems in place and cause heat waves, droughts, or floods

Cosmological Physics and Advanced Computing Group, Argonne National Laboratory
Validating physicality of synthetic galaxy catalogs
People: Joe Hollowed, Salman Habib; (6/2018 – 9/2018)

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

  *Quantifying uncertainty in deep-learning estimates of galaxy-galaxy strong lensing parameters*  
  	*People: Nesar Ramachandra, Salman Habib;* (9/2018 – 9/2019, 10/2019 – 8/2021)

• Quantify 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, Evanston, IL
Fabricating nanoscale devices to measure signatures of superconductivity
People: Patrick Krantz, Venkat Chandrasekhar; (6/2019 – 8/2019)

Leadership and Service¶

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

• Direct student-led committees to undertake initiatives to improve graduate student life in the department (social events, speaker engagements, etc.), organize orientation and admit visit days

• Communicate and respond to concerns raised in department graduate student body to staff/faculty and vice versa

Statistics Graduate Student Association (SGSA), UC Berkeley, Berkeley, CA
Social Committee Chair, August 2022 - May 2023; August 2024 - May 2025

• Direct committee members to organize weekly wind-down events (game nights, restaurants in Berkeley, etc.) for graduate students in the statistics department (MA + PhD students)

• Organize yearly SGSA t-shirt design contest

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

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

• Helped strategize lesson plans for students needing extra assistance in building their math skills

• Lead small-group tutoring sessions after school for 8th grade students taking Chicago’s Selective Enrollment test (used for admission into the city’s top-rated public high schools)

Teaching¶

University of California, Berkeley

• Collaborative and Reproducible Data Science (Stat 159/259); TA/GSI; Fall 2025, course site

• Berkeley Statistics Computational Skills Workshop; Tutor; August 18-22, 2025, course site

• Introduction to Time Series (Stat 153/248); TA/GSI; Spring 2025, course site

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

• Berkeley Statistics Computational Skills Workshop; Tutor; August 19-23, 2024, course site

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

• Principles & Techniques of Data Science (Data 100); TA/GSI; Fall 2022, course site

University of Chicago

• Statistical Theory and Methods I (STAT 24400); Grader; Winter 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., Pérez, F., McAuliffe, J. 2025. Cloud-based Workflows for Antarctic Atmospheric Rivers: Successes and Challenges. (Oral) AGU Fall Meeting 2025. (Zenodo)

• Butler J., Maclennan, M., Pérez, F., McAuliffe, J. 2025. Linking Antarctic Atmospheric River Characteristics with Their Landfalling Impacts. (Oral) AGU Fall Meeting 2025. (Zenodo)

• 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. (Zenodo)

• 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. (Site link).

• 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. (Zenodo)

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

• Butler, J., Finkel, J., Weare, J. 2020. Analysis of a Stochastic Atmospheric Blocking Model using Transition Path Theory. (Poster). 2020 Midstates Undergraduate Research Symposium in the Physical Sciences, Math, and Computer Science.

• 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.

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