Sasun Hambardzumyan
Chief Technology Officer at Activeloop
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolSasun Hambardzumyan is a technology leader and CTO based in the San Francisco Bay Area who combines hands-on engineering with startup-scale product leadership. He has guided engineering and visualization teams at Activeloop through rapid AI data-product development and now leads the company’s technical strategy as Chief Technology Officer. With a background spanning R&D and senior engineering roles at Synopsys and Mentor Graphics and a prior CTO role at Qube, he brings deep systems and visualization expertise grounded in a mathematics and computer science education. Sasun contributes to open-source AI infrastructure—notably improving core query capabilities in the widely used DeepLake vector-database project—showing practical impact on real-world ML data workflows. Known for turning complex data and model challenges into production-ready features, he blends research-minded rigor with pragmatic shipping instincts.
2 years of coding experience
11 years of employment as a software developer
Master Mathematics Computer Science, Master Mathematics Computer Science at Yerevan State University
English, Russian, Armenian
Stackoverflow
285reputation
27kreached
0answers
5questions
Github Skills (18)
python10
machine-learning10
vector-database10
deeplearning-ai10
deep-learning10
ai10
data-science9
datasets9
large-language-models9
tensorflow8
pytorch8
mlops7
openmp6
editline6
readline6
Programming languages (1)
Github contributions (2)
activeloopai/deeplake
Nov 2023 - Mar 2025
Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
Role in this project:
ML Engineer
ML EngineerContributions:9 releases, 136 reviews, 64 PRs in 1 year 4 months
Contributions summary:Sasun's contributions primarily focused on refining the `deeplake.query` functionality, which seems to be a core feature for querying and processing data within the DeepLake ecosystem. They introduced the initial version of the `deeplake.query` and `universal_query` functions, implemented the ability to perform queries, and modified the DeepLakeQueryDataset and DeepLakeQueryTensor classes to accommodate query operations. Additionally, they addressed edge cases and made adjustments to the DeepLake dataset, indicating a focus on enabling more advanced queries and integrations.
pythonreal-time-datadata-streamjupyter-notebookvisualize
activeloopai/buH
Feb 2024 - Feb 2024
Hub backwards (buH) compatibility tests!
Contributions:1 push in 1 day
testingcompatibility
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