Parmida Atighehchian
Sr. Data Scientist
Austin, Texas, United States
Summary
👤
Senior🎓
Top SchoolParmida Atighehchian is a senior data scientist with eight years of experience turning research-grade AI into production systems, currently working at AWS after recent senior research roles at Runway and Twitter. She specializes in active learning and uncertainty estimation—contributing to the baal Bayesian active learning library by fixing dataset labeling bugs and experimenting with DropConnect to improve model uncertainty. Her background spans applied research, productionizing SOTA models, and practical dataset curation, reflecting a rare mix of academic rigor from a Concordia CS master's and hands-on engineering. Based in Austin, she is driven by democratizing safe, responsible AI and has repeatedly built tooling and workflows that bridge research prototypes to scalable production deployments.
8 years of coding experience
13 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at Concordia UniversityBS Communication & Systems, BS Communication & Systems at Isfahan University of Technology
English, Persian, French
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
pytorch10
machine-learning10
deeplearning-ai10
deep-learning10
python10
active-learning10
testing8
Programming languages (2)
Github contributions (5)
baal-org/baal
Oct 2019 - Apr 2022
Bayesian active learning library for research and industrial usecases.
Role in this project:
ML Engineer
ML EngineerContributions:93 reviews, 26 commits, 54 PRs in 2 years 7 months
Contributions summary:Parmida primarily contributes to the `baal-org/baal` repository, a Bayesian active learning library. Their work focuses on addressing a labeling bug within the file dataset implementation, impacting data handling within the active learning framework. Furthermore, the user incorporates and tests the implementation of dropconnect, indicating a focus on research and enhancement of uncertainty estimation methods for active learning and model training. The user's contributions are focused on improving the library's core functionality for handling data and model training within the context of active learning.
pytorchpythonactive-learningbayesian-active-learningdeep-learning
parmidaatg/Parmida_notebook
Jan 2018 - Feb 2018
Contributions:4 PRs, 9 pushes, 2 branches in 1 month
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