The pull requests and none-code contributions below were chosen to showcase the types of software work I've done. This list is not exhaustive.

Features

• adapting lightgbm and xgboost to scikit-learn 1.6:
  • https://github.com/microsoft/LightGBM/pull/6651
  • https://github.com/microsoft/LightGBM/pull/6733
  • https://github.com/microsoft/LightGBM/pull/6718
  • https://github.com/dmlc/xgboost/pull/11021
• setting up conda packages for legate-boost, legate-dataframe, and legate-raft: https://github.com/rapidsai/legate-boost/issues/115
• replacing LightGBM's setup.py with scikit-build-core for PEP 517/518 compatibility: https://github.com/microsoft/LightGBM/pull/5759
• upstreaming dask-lightgbm into LightGBM and guiding community discussion with Dask, XGBoost maintainers
  • https://github.com/dask/community/issues/104
  • https://github.com/microsoft/LightGBM/pull/3515
• adding Webhook storage to prefect: https://github.com/PrefectHQ/prefect/pull/3000
• adding autoconf-based builds of LightGBM's R package: https://github.com/microsoft/LightGBM/pull/3188
• making snowflake-connector-python compatible with pyjwt 1.x and 2.x: https://github.com/snowflakedb/snowflake-connector-python/pull/604
• allow tight control over ports in LightGBM distributed traiining with Dask: https://github.com/microsoft/LightGBM/pull/3994
• cut compiled size of {lightgbm} by ignoring CLI-only objects: https://github.com/microsoft/LightGBM/pull/3566
• allow use of multiple image pull secrets in prefect kubernetes agent: https://github.com/PrefectHQ/prefect/pull/3596
• replace single-shot HTTP requests with httr::RETRY() in various R packages
  • project I led at Chi R Collab 2020: https://github.com/chircollab/chircollab20/issues/1
  • {sergeant} (one example): https://github.com/hrbrmstr/sergeant/pull/42

Docs

• tutorial on distributed LightGBM training with Dask: https://github.com/microsoft/LightGBM/pull/4030
• early stopping example in XGBoost Dask docs: https://github.com/dmlc/xgboost/pull/6501
• detailed information on how LightGBM parameters affect training speed: https://github.com/microsoft/LightGBM/pull/3628
• guide on how to find valid memory and CPU combinations for ECS / Fargate clusters in dask-cloudprovider: https://github.com/dask/dask-cloudprovider/pull/156

Bug Fixes

• fixing dtype setting and tests across predict() calls in lightgbm, required deep investigation (https://github.com/microsoft/LightGBM/pull/7140#discussion_r2725023963)
• fixing OpenMP conflicts in lightgbm:
  • https://github.com/microsoft/LightGBM/issues/6595#issuecomment-2351398026
  • https://github.com/microsoft/LightGBM/pull/6629
  • https://github.com/microsoft/LightGBM/pull/6489
  • https://github.com/microsoft/LightGBM/pull/6391
• detecting debug symbols in pandas 2.0 wheels: https://github.com/pandas-dev/pandas/issues/51900
• prevent conda from "downgrading" Python from CPython to PyPy, while also reducing the risk of a subtle networking error made worse by unpredictability in when Dask garbage collects objects (https://github.com/microsoft/LightGBM/pull/5510)
• create a reproducible example for lightgbm loading failing with GLIBCXX compatibility errors: https://github.com/microsoft/LightGBM/issues/5106#issuecomment-1121925896
• fix jupyter_server conda-forge feedstock recipe to prevent broken environments: https://github.com/conda-forge/jupyter_server-feedstock/pull/84
• make multioutput behavior of dask-ml regression metrics consistent with scikit-learn: https://github.com/dask/dask-ml/pull/820
• fix saving Dask Random Forest models in cuml: https://github.com/rapidsai/cuml/pull/3388
• fix checks for availability of mm_malloc in {lightgbm} autoconf-based builds: https://github.com/microsoft/LightGBM/pull/3510
• fix broken plots in {lightgbm}'s docs site: https://github.com/microsoft/LightGBM/pull/3508
• factor out dependency on gendef.exe for compiling XGBoost and LightGBM R packages with Visual Studio compilers and R 4.0:
  • {xgboost}: https://github.com/dmlc/xgboost/pull/5764
  • {lightgbm}: https://github.com/microsoft/LightGBM/pull/3065

Infrastructure / CI

• helping with various migrations for all of the RAPIDS libraries:
  • updating to newer fmt / spdlog: https://github.com/rapidsai/build-planning/issues/56
  • Dropping Python 3.9: https://github.com/rapidsai/build-planning/issues/88
  • CUDA 12.5: https://github.com/rapidsai/build-planning/issues/73
  • Adding Python 3.12: https://github.com/rapidsai/build-planning/issues/40
  • Adding Python 3.11: https://github.com/rapidsai/build-planning/issues/3
• switching LightGBM's Python package jobs to manylinux_2_28: https://github.com/microsoft/LightGBM/pull/5580
• automatically publish prefect-saturn to PyPI when a new release is created: https://github.com/saturncloud/prefect-saturn/pull/7
• moving LightGBM CI jobs from Travis to GitHub Actions:
  • https://github.com/microsoft/LightGBM/pull/3745
  • https://github.com/microsoft/LightGBM/pull/3726
  • https://github.com/microsoft/LightGBM/pull/3119
• move {uptasticsearch} CI to GitHub Actions: https://github.com/uptake/uptasticsearch/pull/217
• add CI job testing {lightgbm} within ASAN and UBSAN sanitizers: https://github.com/microsoft/LightGBM/pull/3439
• reduce data loading work in LightGBM tests by caching data loading calls: https://github.com/microsoft/LightGBM/pull/3486
• add Dockerfile to build an image for testing the Apache Arrow R package: https://github.com/apache/arrow/pull/2770