Alekh Agarwal
Staff Research Scientist at Google
Redmond, Washington, United States
Summary
👤
Senior🎓
Top SchoolAlekh Agarwal is a Staff Research Scientist at Google with 12 years of experience bridging machine learning theory and practical interactive learning systems, specializing in contextual bandits, reinforcement learning, and active learning. A Berkeley PhD with a strong statistics background, he has held research and leadership roles at Microsoft where he advanced enterprise ML research from postdoc to principal manager. He contributes to prominent open-source ML tooling—evidenced by commits to Vowpal Wabbit that improved parallel allreduce logic and feature-interaction code—highlighting his focus on scalable, production-ready algorithms. Known for pairing rigorous theoretical guarantees with engineering pragmatism, he builds systems that perform under real-world constraints while publishing the ideas behind them. Based in Redmond, WA, he combines deep academic credentials with hands-on backend and ML engineering experience.
12 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at University of California, BerkeleyIndian Institute of Technology Bombay
Github Skills (8)
machine-learning10
c-language10
cprogramming-language10
active-learning10
elearning10
cpp9
bandit9
reinforcement-learning8
Programming languages (3)
Github contributions (5)
VowpalWabbit/vowpal_wabbit
May 2014 - Aug 2018
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:61 commits, 12 PRs, 46 comments in 4 years 2 months
Contributions summary:Alekh made several commits focused on modifying the `stagewise_poly.cc` file, which appears to be related to the Vowpal Wabbit machine learning library. Their contributions involved modifications related to parallel processing, specifically allreduce operations, and included changes to the algorithm's core logic with respect to depth and parent nodes. They also added code related to interacting namespaces within the machine learning framework, indicating a focus on feature engineering and model structure.
machine-learningvowpal-wabbitc-plus-plusonline-learningcontextual-bandits
alekh/alekh.github.io
Sep 2016 - Nov 2022
Contributions:71 pushes, 1 branch in 6 years 3 months
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