Matthew Danielson
Postdoctoral Fellow In Geomorphological Analyses Of Submarine Landforms at Stockholm University
United States
Summary
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Rockstar🎓
Top SchoolMatthew Danielson is a postdoctoral geomorphologist specializing in high-latitude submarine landforms, using seismic, sediment core and multibeam bathymetry to reconstruct past ice-sheet retreat in places like the Ross Sea. With a PhD in Geology and four years of research experience, he combines field-derived geoscience with quantitative skills from a Data Science diploma to extract signal from complex marine datasets. His work bridges academia and industry—he’s held internships at Chevron and contributed to applied exploration projects—while currently advancing geomorphological analyses at Stockholm University. Matthew also brings practical software and ML experience, having improved performance and reliability in the widely used hmmlearn HMM library through NumPy optimizations and variational inference contributions. Colleagues describe him as equally comfortable interpreting seismic facies and refactoring code for better model selection and testing. He aims to translate seafloor form into robust paleoenvironmental narratives that inform both science and resource decision-making.
4 years of coding experience
3 years of employment as a software developer
Data Science, Data Science at Flatiron SchoolDoctor of Philosophy - PhD, Geology/Earth Science, Doctor of Philosophy - PhD, Geology/Earth Science at Louisiana State UniversityBachelor of Science (BS), Geology/Earth Science, General, Bachelor of Science (BS), Geology/Earth Science, General at Texas A&M University
Github Skills (8)
testing10
scikit10
machine-learning10
variational-inference10
pytest10
python10
numpy10
scikit-learn10
Programming languages (1)
Github contributions (2)
hmmlearn/hmmlearn
Nov 2021 - Jan 2023
Hidden Markov Models in Python, with scikit-learn like API
Role in this project:
ML Engineer / Data Scientist
ML Engineer / Data ScientistContributions:62 reviews, 11 commits, 48 PRs in 1 year 2 months
Contributions summary:Matthew focused on improving the performance and reliability of the Hidden Markov Model library. Their contributions include optimizing Multinomial emission statistics using NumPy array operations, which significantly improved performance. They also refactored test code to use parameterized tests for better error reporting and covered various aspects of the Gaussian HMM, including spherical, diagonal, and tied covariance types, along with model selection criteria. In addition, the user contributed to the introduction of variational inference models with Gaussian and Categorical emissions.
apipythonlogistic-regressiondata-sciencestatistical-inference
blckmaxima/hmmlearn
Nov 2021 - Feb 2025
Hidden Markov Models in Python, with scikit-learn like API
Contributions:187 pushes, 36 branches in 3 years 3 months
dtwapipythonlogistic-regressiondata-science
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