Vishwesh Nath
Sr. Applied Research Scientist at NVIDIA
Nashville Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolVishwesh Nath is a Sr. Applied Research Scientist with a decade of experience at the intersection of deep learning and medical imaging, currently building multi-modal models and healthcare LLMs at NVIDIA. He has a strong academic foundation (PhD, Vanderbilt) in diffusion MRI and tractography, and a track record of translating that domain expertise into practical tooling and training pipelines for medical image segmentation. An active contributor to the MONAI ecosystem, he has added self-supervised transformer components, contrastive losses, and label-quality metrics used by the broader medical imaging community. His work bridges uncertainty quantification, active learning, and optimizer/loss improvements to accelerate annotation and robust model training across scanners. Notably, he demonstrated zero-shot transfer from nonhuman primate to human brain data during his graduate research, highlighting a pragmatic focus on generalizable solutions.
10 years of coding experience
9 years of employment as a software developer
Bachelor of Engineering (B.E.), Electrical and Electronics Engineering, Bachelor of Engineering (B.E.), Electrical and Electronics Engineering at Manipal Institute of TechnologyDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Vanderbilt University
English, Hindi
Stackoverflow
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Github Skills (18)
pytorch10
python10
machine-learning10
image-segmentation10
deep-learning10
segmentation10
ai10
computer-vision10
jupyter-notebook10
medical-image-segmentation10
medical-image-processing9
testing9
unit-testing9
active-learning9
3d8
Programming languages (3)
Github contributions (5)
Project-MONAI/tutorials
Nov 2021 - Jan 2023
MONAI Tutorials
Role in this project:
ML Engineer
ML EngineerContributions:62 reviews, 8 commits, 14 PRs in 1 year 2 months
Contributions summary:Vishwesh's primary contribution involves developing and refining training scripts and notebooks for medical image segmentation using MONAI. The commits demonstrate the addition of an SSL training script, including integration of ViTAutoEnc and Contrastive Loss, and a finetuning script for model adaptation. Furthermore, the user contributed to creating, polishing and adapting training and inference notebooks for a NuClick tutorial. This includes fixes related to MetaTensor and updates to transform arguments for the AsDiscreted transform.
pytorchmonai-tutorialsdeep-learningjupyter-notebookmonai
Project-MONAI/MONAILabel
Jun 2021 - Sep 2022
MONAI Label is an intelligent open source image labeling and learning tool.
Role in this project:ML Engineer
ML EngineerContributions:26 reviews, 16 commits, 24 PRs in 1 year 3 months
Contributions summary:Vishwesh primarily contributes to the development and testing of machine learning components within the MONAI Label project, specifically focusing on the integration of epistemic scoring metrics and unit tests for deep learning infer tasks. They added changes to improve the training process for the Spleen App, including the use of Novograd optimizer and DiceCE loss. The user also worked on updating training pipelines for NuClick and performed code formatting and fixes to ensure functionality and compatibility of these components.
pytorchintelligentactive-learningdeep-learninglearning-tool
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