Eli Lifland
Founding Researcher at AI Futures Project
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolEli Lifland is a founding researcher and AI entrepreneur in the San Francisco Bay Area with a decade of experience building ML-infused products and tools that support forecasting and responsible AI development. He co-founded Sage, now advising while leading projects that produce accessible AI explainers and forecasting tools, and currently writes detailed AI scenarios at the AI Futures Project in collaboration with former OpenAI staff. Eli blends hands-on software engineering (contributions to the TextAttack adversarial-NLP framework, including a genetic algorithm attack and weighted-saliency word ranking) with research-oriented product work such as Elicit at Ought. His background in computer science and economics informs a pragmatic approach to AI risk and usability, and he has a track record of moving research ideas into practical tools for forecasters and practitioners.
10 years of coding experience
2 years of employment as a software developer
Bachelor's degree Computer Science and Economics, Bachelor's degree Computer Science and Economics at University of Virginia
Github Skills (8)
adversarial-machine-learning10
machine-learning10
nlp10
python10
data-structure9
algorithm9
data-structures9
algorithms9
Programming languages (5)
Github contributions (5)
QData/TextAttack
Oct 2019 - Aug 2020
TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/
Role in this project:
ML Engineer
ML EngineerContributions:3 reviews, 174 commits, 17 PRs in 10 months
Contributions summary:Eli contributed new functionality to the textattack library, specifically a new genetic algorithm attack with associated file structure and supporting code. Further contributions include debugging and improvements to the genetic algorithm, alongside the refactoring and improvement of other supporting files like `tokenized_text.py` and `attack.py`. The user's commits also show the implementation of a weighted-saliency method for word importance ranking.
pythonadversarial-machine-learningadversarialmodel-trainingtraining
npfoss/team008
Jan 2017 - Jan 2017
Contributions:172 commits in 21 days
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