Duy Tran
Clinical Specialist at MEMORIAL REGIONAL HOSPITAL
Pompano Beach, Florida, United States
Summary
👤
Senior🎓
Top SchoolDuy Tran is a clinical specialist and pharmacist with 11 years of healthcare experience, currently based in Pompano Beach and serving at Memorial Regional Hospital after completing PGY-1 and PGY-2 residencies with a focus on cardiology and immunology. He combines frontline clinical expertise in acute and ambulatory settings with a technical background in computer vision and machine learning, having contributed video-data handling improvements to the well-known Caffe2 deep learning framework. Duy’s training spans high-acuity rotations—including transplant and emergency medicine—and community pharmacy practice, giving him a practical, systems-oriented view of patient care. Comfortable bridging clinical and technical domains, he brings a rare mix of hands-on pharmacy practice and ML engineering experience to clinical informatics and cardiology-focused projects.
11 years of coding experience
2 years of employment as a software developer
Doctor of Pharmacy, Pharmacy, 3.28 GPA, Doctor of Pharmacy, Pharmacy, 3.28 GPA at Florida Agricultural and Mechanical UniversityAssociate of Arts, General Studies, 3.5 GPA, Associate of Arts, General Studies, 3.5 GPA at Broward CollegeHigh School Diploma, Medical Magnet, 4.5 GPA, High School Diploma, Medical Magnet, 4.5 GPA at Blanche Ely High School
English, Vietnamese, Spanish
Github Skills (12)
video-processing10
computer-vision10
machine-learning10
artificial-intelligence10
caffe10
deep-learning10
ai10
ffmpeg9
python9
c-language8
cprogramming-language8
tensorflow3
Programming languages (5)
Github contributions (5)
facebookarchive/caffe2
May 2017 - Jun 2017
Caffe2 is a lightweight, modular, and scalable deep learning framework.
Role in this project:
ML Engineer
ML EngineerContributions:12 commits in 1 month
Contributions summary:Duy primarily contributed to the development of a video data layer for the Caffe2 deep learning framework. Their work included adding functionality to read video data from frames and videos, outputting a 5D tensor and supporting multiple labels. The contributions also encompassed improvements to video decoding, including handling different pixel formats, memory management, and frame access strategies. Furthermore, the user modified the video input operator to output labels as int32 and incorporated options for temporal jittering.
pytorchscalablecaffe2deep-learningml
facebookresearch/VMZ
May 2018 - Nov 2020
VMZ: Model Zoo for Video Modeling
Contributions:24 commits, 10 PRs, 21 pushes in 2 years 7 months
zoomodel-zoomodelingvideo
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