Arash Fard
Senior Software Engineer at Roku
Greater Boston United States
Summary
👤
Senior🎓
Top SchoolArash Fard is a Senior Software Engineer with 12 years of experience specializing in distributed systems and in-database machine learning, currently building Roku’s ad platform. He has been a key architect and implementer of Vertica’s distributed ML subsystem, contributing production-grade algorithms (Random Forest, linear/logistic regression, PLS) and MLOps features like model versioning and tracking. Arash combines deep research roots—a PhD in computer science and early work on large-graph pattern matching—with hands-on engineering, adding CI/CD automation and Python SDK enhancements to open-source projects like VerticaPy. He excels at turning algorithmic ideas into scalable, production systems that run across clusters, and has repeatedly delivered first-of-a-kind distributed ML capabilities. Based in Greater Boston, he pairs academic rigor with practical product focus and a track record of shipping data-intensive infrastructure at scale. An often-overlooked strength is his long history of systems administration and cluster management, which grounds his distributed design decisions in operational reality.
12 years of coding experience
16 years of employment as a software developer
Amirkabir University of TechnologyUniversity of GeorgiaBachelor of Science Electronic Engineering, Bachelor of Science Electronic Engineering at Iran University of Science and Technology
English, Persian
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (35)
python10
machine-learning10
forge10
vertica10
data-visualization10
science10
data-science9
analytics9
conda9
big-data9
data-analysis9
conda-forge8
smithy8
api7
continuous-integration6
Programming languages (3)
Github contributions (5)
afard/GraphCaching
Mar 2014 - Jul 2014
Contributions:17 commits, 2 PRs, 1 push in 4 months
base-codegraphql
afard/VerticaPy
Aug 2020 - Jul 2024
VerticaPy is a Python library that exposes sci-kit like functionality to conduct data science projects on data stored in Vertica, thus taking advantage Vertica’s speed and built-in analytics and machine learning capabilities. It supports the entire data science life cycle, uses a ‘pipeline’ mechanism to sequentialize data transformation operation (called Virtual Dataframe), and offers multiple graphical rendering possibilities.
Contributions:2 PRs, 165 pushes, 103 branches in 3 years 11 months
storedoperationpythoncyclevertica
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