Mohammad Adil
Founding Engineer at Obvio
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMohammad Adil is a Founding Engineer and senior deep learning practitioner with 12 years of experience building computer vision and healthcare AI platforms from research prototypes to production. Based in Sunnyvale, he has led transfer learning and DL platform efforts at NVIDIA and delivered 3D mapping and change-detection algorithms for autonomous checkout at Standard AI before co-founding Obvio to tackle traffic safety. He contributes to notable open-source projects like MONAI (3D medical image segmentation improvements and sliding-window inference) and NVIDIA NVFlare (federated learning examples and CI reliability), reflecting a strong focus on scalable ML workflows and inference optimization. His background spans hands-on research in human motion and tutoring agents to shipping industry-grade deep learning systems, giving him a rare mix of academic rigor and product-oriented engineering. Colleagues rely on him to bridge model development, data pipelines, and robust CI/testing so research can reliably move into real-world applications.
12 years of coding experience
10 years of employment as a software developer
Master’s Degree Computer Science, Master’s Degree Computer Science at University of California, DavisBachelor’s Degree Computer Sc, Bachelor’s Degree Computer Sc at Lahore University of Management Sciences
English, Hindi
Github Skills (21)
pytorch10
python10
medical-image-processing10
testing10
machine-learning10
imaging10
numpy10
deeplearning-ai10
deep-learning10
federated-learning10
ai10
medical-imaging10
computer-vision10
tensorflow9
rotation9
Programming languages (1)
Github contributions (5)
Project-MONAI/MONAI
Feb 2020 - Aug 2021
AI Toolkit for Healthcare Imaging
Role in this project:
ML Engineer
ML EngineerContributions:8 reviews, 18 commits, 42 PRs in 1 year 6 months
Contributions summary:Mohammad primarily contributed to the implementation and improvement of machine learning models within the MONAI framework, specifically focusing on 3D image segmentation tasks. Their contributions included adding new transforms, such as spatial flipping and rotation, and integrating them into the image processing pipeline. They also worked on incorporating and optimizing the sliding window inference technique, indicating a focus on improving the efficiency and performance of model inference on large medical images.
pytorchpythondeep-learningmedical-image-processingcomputer-vision
NVIDIA/NVFlare
Nov 2021 - Jan 2022
NVIDIA Federated Learning Application Runtime Environment
Role in this project:ML Engineer
ML EngineerContributions:13 reviews, 7 commits, 14 PRs in 1 month
Contributions summary:Mohammad primarily focused on updating and testing application examples within the NVIDIA Federated Learning Application Runtime Environment. Their contributions involved modifying and enhancing testing applications, particularly in the context of federated learning with NumPy, likely focusing on data handling, model training, and persistence. They also updated various examples to enhance the performance and usability of the project, involving file changes within the example directory and cleaning up project files. Additionally, the user also added and updated the CI configuration and test files, focusing on reliability.
pythonnvidiadeep-learningruntimemachine-learning
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