Alexis Olson
Director at Jasper Ridge Partners, L.P.
Fort Worth, Texas, United States
Summary
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Rockstar🎓
Top SchoolAlexis Olson is a data-focused analytics leader with 11 years of experience designing and architecting scalable data models and Common Data Models for investment analytics at Jasper Ridge Partners. As Director, she blends deep expertise in Power BI, DAX, M, mathematics, and SQL to translate complex data into actionable insights and reliable reporting frameworks. Her hands-on background ranges from building core analytics pipelines to optimizing model performance and governance, while mentoring teams through evolving analytics needs. Notably, she contributes to high-performance open-source projects like Leela Chess Zero, applying algorithm optimization and search heuristics expertise—an uncommon bridge between quantitative analytics and ML-driven algorithm work. Based in Fort Worth, Texas, Alexis combines academic rigor from Texas A&M with a practical tutoring pedigree, reflecting strong communication skills for both technical and non-technical stakeholders. She’s known for improving analytical backbone stability and squeezing performance out of models where it matters most.
11 years of coding experience
1 year of employment as a software developer
Bachelors, Bachelors at The University of Texas at ArlingtonMaster's degree, Master's degree at Texas A&M University
Stackoverflow
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excel-formula
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Github Skills (31)
algorithm10
algorithms10
c-language10
data-structure10
game-ai10
powerbi10
chess10
excel10
ai10
data-structures10
cprogramming-language10
chess-engine10
game-theory9
if-statement9
vba9
Programming languages (8)
Github contributions (5)
LeelaChessZero/lc0
May 2019 - Jun 2020
Open source neural network chess engine with GPU acceleration and broad hardware support.
Role in this project:
Back-end Developer & Algorithm Optimization
Back-end Developer & Algorithm OptimizationContributions:7 commits, 30 PRs, 5 branches in 1 year 1 month
Contributions summary:Alexis primarily contributed to the Leela Chess Zero engine by implementing and optimizing core game logic related to the evaluation and search algorithms. Their work involved modifying the centipawn conversion formula and adjusting parameters within the Monte Carlo Tree Search (MCTS) algorithm, focusing on aspects such as the calculation of Q+U values and move selection. Significant code changes included incorporating a logit function to the Q value calculation and adjusting MLH settings which would require further optimization and experimentation. In addition, the user addressed issues related to repeated positions and the fifty-move rule.
chess-enginegpu-accelerationhardwareneural-networkalphazero-inspired
AlexisOlson/lc0
May 2019 - Apr 2020
The rewritten engine, originally for tensorflow. Now all other backends have been ported here.
Contributions:26 PRs, 169 pushes, 13 branches in 11 months
pythonbackendsrewrittentensorflow
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