Faisal Ladhak
Researcher at NVIDIA
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolFaisal Ladhak is a researcher and applied machine learning engineer with six years of experience building and evaluating language and vision models across industry and academia. Currently at NVIDIA after recent roles at Answer.ai and Amazon, he blends hands-on model evaluation with production-focused engineering. His PhD-level research background from Columbia underpins work on robust, transparent model assessment—highlighted by contributions to the influential HELM framework where he implemented summarization scenarios and added faithfulness metrics like SummaC. Faisal’s track record includes dataset integration, prompt engineering, and practical fixes for caching and formatting that bridge research and deployable tooling. Based in the San Francisco Bay Area, he combines deep academic training with product-minded delivery across large-scale ML stacks. An unusual detail: he pairs a CS PhD with an undergraduate degree in biology, informing a cross-disciplinary approach to ML problems.
6 years of coding experience
11 years of employment as a software developer
Master of Science (MS) Computer Science, Master of Science (MS) Computer Science at The University of Texas at ArlingtonDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Columbia University
English, Spanish, Urdu, Gujarati
Github Skills (10)
eval10
summarization10
machine-learning10
nlp10
python10
evaluation10
natural-language-processing10
data-science9
rouge9
transformer-models9
Programming languages (2)
Github contributions (5)
stanford-crfm/helm
Mar 2022 - Nov 2022
Holistic Evaluation of Language Models (HELM), a framework to increase the transparency of language models (https://arxiv.org/abs/2211.09110). This framework is also used to evaluate text-to-image models in HEIM (https://arxiv.org/abs/2311.04287) and vision-language models in VHELM (https://arxiv.org/abs/2410.07112).
Role in this project:
ML Engineer
ML EngineerContributions:34 commits in 7 months
Contributions summary:Faisal implemented summarization scenarios for the Holistic Evaluation of Language Models (HELM) framework, specifically integrating datasets like XSum and CNN/DailyMail. Their contributions included defining the summarization task, integrating the datasets, and setting up the adapter specifications, including prompt formats, temperature, and stop sequences, for model evaluation. They also added the SummaC faithfulness metric to the project. Furthermore, they made code adjustments, including fixes related to caching, prompt formatting and dataset integration.
nlparxivabsberthelm
eturcan/scripts
Mar 2020 - Feb 2021
SCRIPTS summarization system for IARPA MATERIAL project.
Contributions:2 PRs, 11 pushes, 1 branch in 11 months
pythonsummarization
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