Mahyar Najibi
Co-Founder And Chief Scientific Officer at Apple
Maryland, United States
Summary
🤩
Rockstar🎓
Top SchoolMahyar Najibi is a research-driven AI leader and entrepreneur with 11 years of experience bridging cutting-edge computer vision research and production systems. Currently a Ph.D. student at the University of Maryland, he serves as Co-Founder and Chief Scientific Officer of ElastixAI while also leading AIML initiatives at Apple, bringing practical deployment experience from prior research roles at Waymo and Google. His work focuses on object detection, robust deep learning, and adversarial ML, and he has made concrete open-source contributions to influential detection tools like SNIPER and SSH that optimize multi-scale training and face detection on benchmarks such as WIDER. Comfortable moving between low-level training pipeline changes and high-level research questions, he pairs rigorous academic training (PhD, top grades) with a proven track record of shipping research into production.
11 years of coding experience
4 years of employment as a software developer
The University of Maryland, College ParkMaster of Science - MS, Artificial Intelligence, Master of Science - MS, Artificial Intelligence at Sharif University of Technology
Github Skills (7)
face-detection10
object-detection10
computer-vision10
mxnet10
machine-learning10
caffe10
python10
Programming languages (3)
Github contributions (5)
mahyarnajibi/SSH
Sep 2017 - Aug 2021
SSH: Single Stage Headless Face Detector
Role in this project:
ML Engineer
ML EngineerContributions:55 commits, 9 pushes, 21 comments in 3 years 11 months
Contributions summary:Mahyar primarily contributed to the development of a Single Stage Headless Face Detector (SSH) by initializing the necessary packages and modules. They added dataset loading capabilities for the WIDER dataset, a common benchmark for face detection tasks. Further contributions involved integrating evaluation tools specifically designed for the WIDER dataset.
face-detectorssh
mahyarnajibi/SNIPER
Jan 2018 - Aug 2021
SNIPER / AutoFocus is an efficient multi-scale object detection training / inference algorithm
Role in this project:Back-end Developer
Back-end DeveloperContributions:262 commits, 32 pushes, 31 comments in 3 years 7 months
Contributions summary:Mahyar updated the main module and the iterator within the `sniper` repository, a multi-scale object detection training and inference algorithm. These changes involved modifying the core logic of the training process, including updates to the main execution file, specifically related to the usage of a record iterator and modules. Furthermore, the user was involved in integrating new library files and updating the iterator to correctly handle the data and labels in the training pipeline.
autofocusinferenceobject-detection
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