Adam Li

United States, China

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Summary

🤩

Rockstar

Adam Li is a Senior Applied Scientist at Amazon with 11 years of experience translating ambiguous customer and research problems into production-ready solutions. He holds a PhD in Biomedical Engineering from Johns Hopkins and completed a NSF-funded postdoc in Columbia’s Causal AI Lab, working at the intersection of causal inference, computational neuroscience, and dynamical systems. An active open-source core developer on scikit-learn (59k stars) and contributor to MNE-Python and networkx, he pairs deep algorithmic expertise with pragmatic software engineering in Python/Cython and working knowledge of C/C++ and R. As CTO and cofounder of a medtech startup he led regulatory (510(k)) and fundraising efforts, and in industry internships implemented causal ML at scale on 100M+ samples and low-level C++ template work for sparse tensors. He is equally comfortable shipping high-performance analytics on Spark/Hadoop and designing robust APIs, CI, and unit-tested libraries that bridge research and production.

11 years of coding experience

Stackoverflow

Stats

1,222reputation

145kreached

11answers

72questions

Github Skills (36)

data-visualizations10

python10

annotations10

scikit10

machine-learning10

data-visualisation10

file-io10

annotation10

fileio10

scikit-learn10

file-handling10

file-processing10

data-visualization10

file-access10

annotate10

Programming languages (13)

JavaC++CDHolyCMakefileTeXAGS Script

Github contributions (5)

scikit-learn/scikit-learn

Sep 2021 - Dec 2022

scikit-learn: machine learning in Python

Role in this project:

Data Scientist

Contributions:344 reviews, 4 commits, 73 PRs in 1 year 3 months

Contributions summary:Adam primarily contributed to the scikit-learn library by fixing bugs and maintaining the existing code base. Their work included removing unnecessary parameters from the `FeatureAgglomeration` fit function for a more concise error message, as well as separating unit tests in `test_tree.py` for pickling and handling `min_impurity_decrease`. The user also handled `Tree.sample_weight` using a memoryview, and removed Cython compilation warnings. Further contribution to documentation for tree structure and fixing the handling missing values in `MSE` and `Friedman-MSE` `children_impurity`.

data-analysispythonstatisticsdata-sciencelearn-machine-learning

mne-tools/mne-python

Nov 2019 - Jul 2022

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

Role in this project:

Back-end Developer

Contributions:177 reviews, 37 commits, 46 PRs in 2 years 8 months

Contributions summary:Adam contributed to the MNE-Python project by implementing and refining features related to data visualization and file I/O. They added and updated parameters, fixed formatting, and addressed bugs related to annotation handling, particularly with annotations in EDF files, and channel name handling. The user also worked on the integration of annotation features and the associated metadata into the Epochs object, enabling more robust data handling workflows.

visualizationneuroimagingneurosciencepythonstatistics

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