Vadim Khotilovich
Director And Data Scientist
Kansas City Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolVadim Khotilovich is a Director and Data Scientist with 13+ years of experience transforming physics-grade analytics into production healthcare predictive models, currently leading data science efforts at Oracle after a long tenure at Cerner. He blends deep quantitative expertise—Monte Carlo, robust estimation, numerical methods—with engineering skills across R, Python, C++ and distributed systems to ship scalable ML solutions and optimize data pipelines. His background driving real-time, low-latency trigger algorithms for LHC experiments informs a rare strength in performance-sensitive model design and algorithm debugging. An active contributor to the xgboost R interface, he has improved stratified CV, SHAP handling and feature-importance workflows, showing practical influence on a widely used open-source ML library. Based in the Kansas City area, he is known for turning complex, noisy data into reliable clinical decision tools and for accelerating systems with pragmatic, measurable optimizations.
12 years of coding experience
21 years of employment as a software developer
Bachelor of Science (B.S.) Physics (Throretical Physics) Computer Science, Bachelor of Science (B.S.) Physics (Throretical Physics) Computer Science at Belarusian State UniversityPh.D Physics, Ph.D Physics at Texas A&M University
Stackoverflow
773reputation
52kreached
12answers
1question
Github Skills (15)
r10
xgboost10
machine-learning10
cross-validation10
gradient-boosting10
data-analysis10
documentation8
linear-algebra7
statistics6
scipy6
awk6
boost6
python6
numpy6
classification6
Programming languages (4)
Github contributions (5)
dmlc/xgboost
Apr 2015 - Sep 2018
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:107 commits, 87 PRs, 22 pushes in 3 years 5 months
Contributions summary:Vadim primarily contributed to the R package of the xgboost library. Their work involved adding functionality to support stratified cross-validation, fixing documentation, and improving the handling of feature importances. They also made several code improvements related to SHAP feature contributions and handling of model attributes. The changes suggest a focus on enhancing the usability and functionality of the R interface, including performance optimizations and addressing edge cases.
xgboostpythonflinkdaskdataflow
khotilov/xgboost
Jan 2015 - Sep 2018
eXtreme Gradient Boosting (Tree) Library
Contributions:168 pushes, 49 branches in 3 years 9 months
extremegradientsmachine-learningextreme-gradient-boostinggradient
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