Owen Melia
Flatiron Research Fellow
New York, New York, United States
Summary
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Senior🎓
Top SchoolOwen Melia is an incoming Flatiron Research Fellow at the Simons Foundation’s Center for Computational Mathematics with eight years of experience bridging scientific computing and machine learning for the physical sciences. He completed a Ph.D. in Computer Science at the University of Chicago in 2025 and focuses on fast direct PDE solvers to accelerate data-driven scientific imaging and biological modeling. His background includes data science and collaborative research across institutions (including Argonne National Laboratory) and teaching advanced math and ML courses, giving him a strong blend of theoretical rigor and practical implementation. Known for translating sophisticated numerical methods into tools for real-world scientific problems, he’s equally comfortable developing lab infrastructure and simulating adaptive statistical queries. Based in New York, he brings an unusual combination of mathematical depth, hands-on engineering, and cross-disciplinary collaboration to AI-for-science projects.
8 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of ChicagoBachelor of Science (BS), Mathematics and Economics, Bachelor of Science (BS), Mathematics and Economics at The University of Chicago
Github Skills (19)
imputation10
gwas9
data-access8
reproducible-research7
statistics7
pipeline7
python6
latex5
slurm3
hpc-cluster3
gradient2
hpc2
pytorch2
flexibility2
factor-analysis1
Programming languages (6)
Github contributions (5)
hakyimlab/summary-gwas-imputation
Feb 2020 - Aug 2020
harmonization, liftover, and imputation of summary statistics from GWAS
Contributions:135 commits, 2 PRs, 4 pushes in 6 months
statisticspythonimputationharmonizationgwas
meliao/fourier_neural_operator
Mar 2021 - Feb 2022
Use Fourier transform to learn operators in differential equations.
Contributions:268 pushes in 11 months
equationstransformneural-networksmachine-learningfourier
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