Shubham Chandel
Quantitative Researcher
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolShubham Chandel is a quantitative researcher with 11 years of experience at the nexus of language modeling and reinforcement learning, currently driving research at Vatic Labs after training and evaluating LLMs for code generation on GitHub Copilot at Microsoft. He combines industry-scale model development with academic rigor from NYU—where he worked with Yann LeCun—plus internships at IBM and Amazon focused on RL-enhanced NLP. Shubham has a practical engineering bent evidenced by contributions like a refined PyTorch model-summary tool that improves model inspection and size estimation for practitioners. Comfortable moving between research and production, he has applied RL to GPT-2 fine-tuning, text-based gaming, and code-generation benchmarks. Based in New York, he brings a track record of shipping reliable ML tooling and model improvements that bridge prototyping and deployed systems.
11 years of coding experience
5 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at New York UniversityBachelor of Technology (B.Tech.) Computer Science Engineering, Bachelor of Technology (B.Tech.) Computer Science Engineering at Indian Institute of Technology, Mandi
English, Hindi, German
Stackoverflow
3,073reputation
422kreached
2answers
3questions
pytorch
top-1%
python
top-5%
Github Skills (16)
pytorch10
machine-learning10
deep-learning10
trainings10
python10
modeling10
keras9
numpy7
quicksort6
infinite-loop6
range-query6
algorithm6
parse-platform6
android-service6
android6
Programming languages (6)
Github contributions (5)
sksq96/pytorch-summary
Apr 2018 - May 2021
Model summary in PyTorch similar to `model.summary()` in Keras
Role in this project:
ML Engineer
ML EngineerContributions:38 commits, 6 PRs, 28 pushes in 3 years 1 month
Contributions summary:Shubham primarily contributed to the development of a PyTorch model summary tool. They refactored the core `summary` function, packaged the content, and added setup files. The user also addressed bugs related to bias and weight handling within the model summary and added model size estimation features. These changes indicate an active role in enhancing the tool's functionality and usability.
pytorchloss-functionssimilardeep-learningtorchvision
sksq96/dl-papernotes
Apr 2018 - May 2018
Contributions:18 commits, 1 push in 1 month
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