Aza Tulepbergenov
Software Engineer at Google
California, United States
Summary
🤩
Rockstar🎓
Top SchoolAza Tulepbergenov is a machine learning software engineer at Google Health with a decade of experience applying foundational AI research to real-world products, especially in dialogue management, recommendation systems, and behavior change. He blends research pedigree—publications at NeurIPS and ICLR—with hands-on engineering across Google Assistant, Cloud, and Health, implementing Deep RL and transformer-based models and building ML infrastructure to scale experiments. His background includes developer tooling and open-source contributions, notably improving testing and reliability for the widely used Actions on Google Node.js client. Aza graduated summa cum laude in Computer Science and has a history of accelerating projects from prototype to production, from Golang CLIs that manage media resources to test-suite refactors that cut technical debt. Based in California, he combines scientific curiosity with pragmatic engineering to bridge research and product impact.
10 years of coding experience
3 years of employment as a software developer
Bachelor's degree, B.S. Computer Science, Honors College, Summa Cum Laude, Distinction Honors from Honors College, Bachelor's degree, B.S. Computer Science, Honors College, Summa Cum Laude, Distinction Honors from Honors College at Boise State University
English, Russian
Github Skills (6)
nodejs10
javascript10
testing9
chai7
nodejs-library7
mochajs7
Programming languages (10)
Github contributions (5)
actions-on-google/actions-on-google-nodejs
May 2017 - Apr 2019
Node.js client library for Actions on Google
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 commits in 1 year 11 months
Contributions summary:Aza primarily focused on refactoring and improving the existing test suite for the Node.js client library. This involved rewriting and restructuring tests for various components like `ApiAiApp`, including tests for version detection, SSML syntax validation, and constructor methods. The user consolidated repetitive code within the tests, introducing global variables and `beforeEach` blocks to enhance readability and maintainability. The commits indicate a focus on ensuring the reliability and accuracy of the library's functionality.
alexa-skills-kitjs-clientgoogleclient-librarynode-js
NTMatBoiseState/FiberFit
Nov 2015 - Nov 2016
FiberFit is a portable Python application for Mac and Windows. It uses computer vision to analyze ligament patterns in 2-D 8-bit images. A results summary table (.csv) and image summary documents (.pdf) may be exported by the user.
Contributions:112 commits, 12 pushes, 1 branch in 11 months
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