Roger Iyengar - Research Scientist at Meta

Roger Iyengar

Research Scientist at Meta

Seattle, Washington, United States

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Summary

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Senior

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Top School

Roger Iyengar is a research scientist at Meta’s XStack Efficiency team with 11 years of software engineering experience and a PhD in Computer Science from Carnegie Mellon focused on scaling wearable cognitive assistance for assembly tasks. He brings a blend of research rigor and production-first engineering from internships at Google and Facebook, and practical systems work touching distributed messaging and documentation in notable open-source projects like PyZMQ and TensorFlow docs. His contributions to ZeroMQ Python bindings and robustness improvements in ØMQ examples show a focus on reliable backend systems and clearer developer APIs, while documentation edits reflect attention to usability. Based in Seattle, he pairs academic depth with hands-on system design, often improving fault handling and clarity in complex codebases.

11 years of coding experience

1 year of employment as a software developer

Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Washington University in St. Louis

Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Carnegie Mellon University

Github Skills (9)

error-handling10

zeromq10

tensorflow10

python10

documentation10

concurrency9

machine-learning9

deep-learning8

deeplearning-ai8

Programming languages (11)

JavaC++CSCSSJavaScriptGoPHPHTML

Github contributions (5)

tensorflow/docs

Apr 2019 - Sep 2019

TensorFlow documentation

Role in this project:

Technical Writer

Contributions:9 commits, 4 PRs, 1 comment in 5 months

Contributions summary:Roger primarily contributed to the TensorFlow documentation repository by making updates to the `images.ipynb` tutorial. Their work involved correcting wording, removing outdated statements, and addressing minor formatting issues within the tutorial. The edits suggest a focus on improving clarity and accuracy of the tutorial content for users. Specifically, the user edited the introduction to the tutorial and removed redundant information.

deep-learningmachine-learningdeep-neural-networkstensorflow-tutorialstensorflow

zeromq/pyzmq

Jun 2020 - Jun 2020

PyZMQ: Python bindings for zeromq

Role in this project:

Back-end Developer

Contributions:6 commits, 1 PR, 2 comments in 3 days

Contributions summary:Roger primarily focused on updating the `socket.py` and `poll.py` files, which are likely core components of the PyZMQ library. Their commits involved modifying method descriptions, event handling, and documentation within these modules. The changes suggest a focus on refining and clarifying the API for interacting with ZeroMQ sockets and polling mechanisms in Python. These edits aim to improve the usability and understanding of the PyZMQ library for developers.

pythonzeromqpython-bindingspyzmqcython

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