CV
Summary
Computational physicist (PhD, Michigan 2025) developing machine learning systems and production data pipelines for climate tech and science. Seven years designing physics-informed models, statistical algorithms, and HPC pipelines for multi-terabyte scientific datasets.
Education
- PhD, Physics — University of Michigan (GPA: 4.0), 2018–2025
- BS, Physics — University of California, Santa Barbara (GPA: 3.95), 2014–2018 (College of Creative Studies)
Technical Skills
- Machine Learning: PyTorch, CNNs, transformers, Gaussian process regression, probabilistic modeling, uncertainty quantification, scikit-learn, time-series analysis, signal processing
- Scientific Python: NumPy, SciPy, Xarray, Dask, Pandas, GeoPandas, OpenCV, Matplotlib, Jupyter
- Languages: Python, C++, SQL
- Infrastructure: AWS (S3, EC2), GCP, Docker, SLURM/PBS, NASA Pleiades HPC, NetCDF/HDF5/Zarr, GitHub
- Scientific Computing: MATLAB, Mathematica, LaTeX, FIJI
Selected Experience
Research Quality Analyst — Flypower.io (Jan–Mar 2026, Remote)
- Reviewed and validated AI-generated analysis for 15 high-impact renewable energy risk reports
- Developed prompt engineering and QA methodology supporting permitting for 20 GW of renewable projects
Climate Data Scientist & Algorithm Developer — Arbic Lab, U-M (2021–2025, Ann Arbor)
- Designed spatio-temporal spectral decomposition algorithm (NumPy, SciPy, Xarray, Dask) for NASA’s coupled Earth-system simulation (MITgcm/GEOS5)
- Built parallelized ETL pipelines on NASA Pleiades HPC processing ~1 TB of NetCDF climate simulation data
Biophysical Data Scientist & Simulation Engineer — Lubensky Lab, U-M (2018–2025, Ann Arbor)
- Built first semi-automated image analysis pipeline (Python, OpenCV, scikit-learn, FIJI) processing ~1 TB of microscopy data, achieving 10x throughput improvement
- Engineered C++ physics-based simulation of 100,000+ heterogeneous cells with stochastic dynamics
- Supervised 5 undergraduate researchers on simulation, statistical inference, and image analysis
Selected Publications
- Avik Mondal*, Andrew J. Morten*, Brian K. Arbic, Glenn R. Flierl, and Robert B. Scott. “Spatio-temporal spectral transfers in fluid dynamics.” Phys. Rev. Fluids 10, 064602 (2025). (*co-first authors)
- Avik Mondal, Chantal Nguyen, Xiao Ma, Ahmed E. Elbanna, and Jean M. Carlson. “Network models for characterization of trabecular bone.” Phys. Rev. E 99, 042406 (2019).
Talks & Presentations
- APS March Meeting 2025 (in-person talk)
- APS March Meeting 2024 (in-person talk)
- AGU Ocean Sciences Meeting 2024 (in-person talk)
- AGU Annual Meeting 2023 (in-person poster)
- APS March Meeting 2023 (in-person talk)
- APS March Meeting 2022 (virtual)
- AGU Ocean Sciences Meeting 2022 (virtual)
- APS March Meeting 2018 (in-person)
Teaching (University of Michigan, 2018–2025)
- Physics of Architecture (121), Computational Physics (411), Physical Oceanography (421, 3 semesters), Intro to Oceanography (222/223), Statistical Mechanics (406), Intro Statistics (PSTAT 412, 2 semesters), Intro Mechanics Labs (141)