Ekta Khanna
Software Engineer at Broadcom
San Jose, California, United States
Summary
👤
Senior🎓
Top SchoolEkta Khanna is a software engineer with 10 years of experience building large-scale distributed databases and in-database machine learning, currently based in San Jose. She has deep expertise in query optimization engines and end-to-end delivery, having contributed to GPORCA and led feature development and stability improvements for Apache MADlib where she enhanced deep learning support and minibatch performance. At Pivotal and VMware she combined hands-on engineering with technical leadership—scoping requirements, breaking down complex problems, and coordinating cross-team integrations for Greenplum and release automation. Her background spans telecom OSS at Amdocs to big-data tooling at Walmart Labs, giving her practical strengths in performance tuning, debugging production escalations, and operationalizing ML. Notably, she improved MADlib’s madlib_keras_fit() to support one-hot dependent variables and added metrics controls that materially sped up training workflows in production-like environments.
10 years of coding experience
7 years of employment as a software developer
Master's Degree Computer Engineering, Master's Degree Computer Engineering at San José State UniversityBachelor's Degree Computer Science, Bachelor's Degree Computer Science at MIT College of Engineering, Pune
English, Hindi
Github Skills (15)
data-preprocessing10
keras10
machine-learning10
deep-learning10
postgresql10
python10
set-operations9
tensorflow9
sql9
operation9
array-merge9
array-iterator9
array-comparison9
performance-optimization8
unit-testing7
Programming languages (5)
Github contributions (5)
apache/madlib
Mar 2019 - Apr 2021
Mirror of Apache MADlib
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:85 reviews, 49 commits, 69 PRs in 2 years 1 month
Contributions summary:Ekta's contributions primarily focused on the deep learning aspects of the Apache MADlib project, a library for in-database machine learning. They removed and refactored code related to "dependent_offset" functionality, and added support for one-hot encoded dependent variables in the `madlib_keras_fit()` function for training deep learning models. They also improved the performance of minibatch processing by using array_cat and adding a new parameter "metrics_compute_frequency" to madlib_keras_fit(). Additionally, the user fixed several bugs related to disk space, data distribution and various other issues within the deep learning and utility modules, along with updating the testing code.
javaapachemadlib
khannaekta/gporca
Apr 2017 - Dec 2021
A modular query optimizer for big data
Contributions:32 releases, 65 pushes, 33 branches in 4 years 8 months
queryquery-optimizersqlbig-datadatabase
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