Pradeep Dasigi
Lead Research Scientist at Ai2
United States
Summary
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Rockstar🎓
Top SchoolPradeep Dasigi is a Lead Research Scientist at AI2 with 11 years of experience in NLP, specializing in post-training for large language models, fine-tuning, data curation, and evaluation. He has played key roles in developing open models such as Olmo and Tulu and has progressed through multiple research ranks at AI2, reflecting deep operational and scientific ownership. His background spans both classic and deep learning approaches across semantic parsing, QA, and representation learning, with early work on weak supervision and cross-lingual retrieval. An active contributor to allennlp, he has implemented core encoder-decoder functionality, testing improvements, and practical tooling for model data generation. He holds a PhD from Carnegie Mellon and combines rigorous academic training with hands-on engineering that bridges research prototypes to reusable open-source artifacts.
11 years of coding experience
15 years of employment as a software developer
B. Tech, Computer Science and Engineering, B. Tech, Computer Science and Engineering at Vellore Institute of TechnologyMaster of Science, Computer Science, Master of Science, Computer Science at Columbia EngineeringDoctor of Philosophy (PhD), Language and Information Technologies, Doctor of Philosophy (PhD), Language and Information Technologies at Carnegie Mellon University
English, Telugu, Hindi
Stackoverflow
1reputation
0reached
0answers
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Github Skills (7)
pytorch10
nlp10
encoder-decoder10
python10
natural-language-processing9
deep-learning9
data-science8
Programming languages (5)
Github contributions (5)
allenai/allennlp
Oct 2017 - Aug 2019
An open-source NLP research library, built on PyTorch.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:32 commits, 116 PRs, 50 pushes in 1 year 10 months
Contributions summary:Pradeep implemented a basic encoder-decoder model, making changes to core model files and adding tests. They addressed mypy errors and commented on code. Additional work included making the number of requested GPUs configurable, reading vocab parameters in model tests, adding a script to generate data from an ERM trained model, and fixing issues related to empty decoder outputs. The user also contributed to an ERM parser and made checklist updates more efficient.
nlppytorchtransformerspythonbert
pdasigi/pnp
Jan 2017 - Aug 2017
Contributions:45 pushes, 4 branches in 7 months
neural-networkneural-networksmachine-learningprobabilistic
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