Adam Li
Senior Applied Scientist at Amazon
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolAdam Li is a Senior Applied Scientist at Amazon with 11 years of experience applying causal inference, machine learning, and dynamical-systems expertise to ambiguous, high-impact problems across industry and academia. He brings deep research credentials—a PhD in Biomedical Engineering from Johns Hopkins and a postdoc in Columbia’s Causal AI Lab—combined with production-grade software engineering as a scikit-learn core developer and contributor to MNE and other high-profile open-source projects. His work spans large-scale data engineering (Spark/Hadoop), optimized Python/Cython/C++ code, and validated medical-device pipelines, including leading an NSF-funded startup CTO role that progressed toward FDA pathways. At Uber he scaled causal models to 100M+ samples, and his open-source contributions to scikit-learn and MNE reflect a rare mix of API design, numerical rigor, and testing discipline. Based in New York, he blends theoretical depth with product-minded execution and a track record of turning neuroscience and biomedical research into reproducible, deployable software.
11 years of coding experience
11 years of employment as a software developer
University of California, San DiegoJohns Hopkins UniversityHigh School Diploma, High School Diploma at Westlake High SchoolGlobal Pre-MBA Leadership Program, Global Pre-MBA Leadership Program at Yale School of Management
English, Chinese, French
Stackoverflow
1,222reputation
145kreached
11answers
72questions
Github Skills (31)
data-visualizations10
python10
annotations10
scikit10
machine-learning10
data-visualisation10
fileio10
scikit-learn10
file-handling10
file-processing10
data-visualization10
file-access10
annotate10
data-analysis10
statistics9
Programming languages (13)
Github contributions (5)
scikit-learn/scikit-learn
Sep 2021 - Dec 2022
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions: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
Back-end DeveloperContributions: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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