CV

Education

California Institute of Technology, 2023-Present

• Ph.D. Student in Environmental Science and Engineering, GPA: 4.0 – Advisor: Tapio Schneider
• Thesis Advisory Committee: Tapio Schneider, Andrew Stuart, Tom Beucler, Andrew Thompson (chair), Aditi Sheshadri
• Relevant Coursework: Inverse Problems, Data Assimilation, Machine Learning, Geophysical Fluid Dynamics

Harvard University, 2023

• A.B. Magna Cum Laude in Applied Mathematics, GPA: 3.94
• Focus Field in Earth and Planetary Sciences, Secondary in Statistics

Middlebury Union High School, 2018

• Valedictorian, Vermont Scholar

Current Research Projects

• Improving precipitation forecasts using graph-transformers and satellite data with Google collaborators. link
• Subseasonal Forecasting in AIWeatherQuest with Ronak Patel.
• Optimizing loss functions to accelerate calibration of earth system models. PI: Tapio Schneider.
• Designing online data-driven closures for cloud fraction. PI: Tapio Schneider in collaboration with Tom Beucler.

Publications

1. Schmitt, J.F., Delorme, B., King, R.C., Patodia, Y., Schneider, T., Sheshadri, A., Jain, R. (2026 arXiv preprint). Improving precipitation forecasts in an AI weather model using observational data. arXiv. https://doi.org/10.48550/arXiv.2609.03210. Submitted.

2. Grayson W. White, Josh K. Yamamoto, Dinan H. Elsyad, Julian F. Schmitt, Niels H. Korsgaard, Jie Kate Hu, George C. Gaines III, Tracey S. Frescino, and Kelly S. McConville. 2025. Small area estimation of forest biomass via a two-stage model for continuous zero-inflated data. Canadian Journal of Forest Research. 55: 1-19. https://doi.org/10.1139/cjfr-2024-0149

3. Schmitt, J.F., Tseng, K.-C., Hughes, M., & Johnson, N. C. (2024). Illuminating snow droughts: The future of western United States snowpack in the SPEAR large ensemble. Journal of Geophysical Research: Atmospheres, 129, e2023JD039754. https://doi.org/10.1029/2023JD039754

Conference Presentations

• Schmitt, J., Beucler, T., Christoploulos, C., Schneider, T., “Online learning of a machine learning cloud fraction closure in a hybrid earth system model”, American Geophysical Union, 2025.
• Schmitt, J., Baptista, R., Reddy, T., Christopoulos, C., Dunbar, O., Sridhar, A., Schneider, T., “A framework to assess parameter identifiability and observation utility in earth system model calibration”, GC41A-05, American Geophysical Union, 2025.
• Schmitt, J., Abel, M., Johnson, N., Tseng, K.-C., “Illuminating Snow Droughts: The Future of Snowpack in the Western United States”, 14A-3. 36th Conference on Hydrology. American Meteorological Society, 2022.
• Denolle, M., Schmitt, J., Ermert, L., Clements, T., Wang, N., and Olsen, K. B., “Illuminating seismic waveguides using noise cross correlations and numerical simulations”, Art. no. S005-03, 2020. American Geophysical Union, 2020.
• Schmitt, J., Clements, T., Retailleau, L., Mordret, A., Beroza, G. C., and Denolle, M., “Comprehensive California-wide large-scale noise cross correlations”, vol. 2020, Art. no. S022-08, 2020.
• Schmitt, J., Clements, T., Wang, N., Olsen, K., Denolle, M., “Ground Motion Prediction Using Ambient Seismic Noise on a Large-N Array in the LA Basin”, Poster #212. Southern California Earthquake Center Annual Meeting, 2020.
• Clements, T., Schmitt, J., Denolle, M., “Cloud-Native Analysis of Southern California Waveform Data”, Poster #193. Southern California Earthquake Center Annual Meeting, 2020.

Workshops and Summer Schools

• NASA Summer School on Satellite Observations and Climate Models. August 20-29, 2025.
• Les Houches School of Physics: Parameterizations for Global Dynamical Models in Climatology, Astrophysics, and Planetology. Presentation: Toward Reducing Climate Model Cloud Biases: Calibration of a Unified EDMF Parameterization with Observational Data. March 16-21, 2025. Poster.
• Schmidt Science 2024 Cross-VESRI Convening and Summer School. Cambridge, UK. Presentation: Fine-Tuning Model Parameterizations with Observational Data: A Case Study with CliMA’s EDMF Model. July 2024. Poster.

Teaching

• TA for Caltech ESE 101: Earth’s Atmosphere, Fall 2026. Prof: Tapio Schneider.
• TA for Caltech ESE 101: Earth’s Atmosphere, Fall 2025. Prof: Tapio Schneider.
• TA for Caltech Ge/Ay 117: Bayesian Statistics, Winter 2025. Prof: Heather Knutson.

Technical Skills

Programming Languages: Julia, Python, Git

• Julia: CliMA suite (package development, CI), Seismology Applications, and Packages
• Python: PyTorch, Hydra

Cloud Computing: Google Cloud Platform, Amazon Web Services, SLURM, Containerized Application Development

• Developing and training state-of-the-art AI weather models on 100+ GPU systems.

Archived Software and Data Products

• Schmitt, J., Clements, T., & Denolle, M. (2025). Julians42/C4-Project.jl: C4 Project v0.1.0 (v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.15276644
• Schmitt, J., Ni, Y., Clements, T., & Denolle, M. (2025). Julians42/SeisCore.jl: SeisCore v0.1.0 (v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.15276502
• Schmitt, J., Tseng, K.-C., Hughes, M., & Johnson, N. (2022). Dataset – Illuminating snow droughts (0.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7121528
• Julian Schmitt. (2022). Julians42/Snow_Droughts: 0.1.0 (0.1.0). Zenodo. https://doi.org/10.5281/zenodo.7130303

Selected Awards

National Science Foundation Graduate Research Fellowship (GRFP) - 2023

• Five year fellowship with three years of financial support

NOAA Hollings Scholarship - 2020

• Academic assistance (up to $9,500 per year) for two years and 10-week paid internship at NOAA facility

Detur Book Prize - 2019

• Given to the top 5% of Harvard Freshman Class

John Harvard Scholar - 2019

MathWorks Math Modeling Challenge Finalist - 2018 link

Activities

Caltech Alpine Club - Run Coordinator, 2025-present

• Design routes, coordinate carpool, and lead weekly trail runs for about 20 people.

Harvard Varsity Nordic Skiing - 2019-2023

• Walk-on member of the D1 Harvard Nordic Ski team. Student-Athlete Advisory Committee Representative 2020-22.

Undergraduate Research Experience

Software Engineer, Coolant, Summer 2023

• Remote sensing and machine learning. Developed pipeline for carbon stock estimation using Google Earth Engine and CNNs. Under the direction of Michael Wu, CEO.

Harvard Statistics Group / Senior Thesis, 2022

• Studied bias-variance trade-offs between machine learning methods for carbon stock estimation for U.S. Forest Service. Developed interactive website for NCASI. Prof. Kelly McConville.

NOAA Hollings Scholar, Summer 2021

• Statistical methods for snow drought forecast using the SPEAR large ensemble climate model at Boulder PSL and Princeton GFDL. Dr. Nathaniel Johnson (GFDL), Dr. Mimi Abel (PSL), Dr. Kai-Chih Tseng (NTU). Paper here.

Harvard Seismology Group, 2020-2021

• Built Julia workflow for high-performance seismic data analysis on AWS cloud using 100+ TBs of data to estimate waveguide effects in the Los Angeles basin. Prof. Marine Denolle (now at University of Washington).