Ming Shen
Machine Learning Engineer
Tempe, Arizona, United States
Summary
🤩
Rockstar🎓
Top SchoolMing Shen is a machine learning engineer with six years of experience bridging academic research and applied ML, currently pursuing a PhD in Computer Science at Arizona State University after earning an MS from USC and a BS from WPI. He has multiple applied scientist internships at AWS where he worked on production-relevant ML problems, and contributes to open-source healthcare ML tooling such as PyHealth, implementing and evaluating models like tLSTM and ResNet18 on MIMIC for mortality prediction. Ming combines deep learning model development, experimental workflow engineering, and rigorous dataset handling—skills honed through iterative notebook-driven experiments and real-world deployments. Based in Tempe, he brings both strong research foundations and practical engineering discipline to healthcare and production ML systems.
5 years of coding experience
1 year of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at University of Southern CaliforniaDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Arizona State UniversityBachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at Worcester Polytechnic Institute
Github Skills (15)
pytorch10
machine-learning10
deeplearning-ai10
lstm10
deep-learning10
trainings10
health10
ehealth10
python10
medical10
modeling10
data-analysis10
data-mining9
preprocess8
preprocessing8
Programming languages (1)
Github contributions (5)
sunlabuiuc/PyHealth
Aug 2020 - Jan 2021
A Deep Learning Python Toolkit for Healthcare Applications.
Role in this project:
ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:39 commits, 36 pushes, 3 branches in 5 months
Contributions summary:Ming's commits focus on updating and testing model workflows, specifically for mortality prediction using the MIMIC dataset within the pyhealth toolkit. These changes involve modifying existing notebooks, which include setting up the environment, loading experimental data, and training models. The user also incorporates and utilizes different models, specifically tLSTM and resnet18, indicating their involvement in model selection and evaluation within a deep learning framework applied to healthcare data.
python-libraryclinical-datapythondeep-learningdata-mining
qxiaobu/MHM
Nov 2020 - May 2022
Contributions:7 commits, 5 pushes, 1 branch in 1 year 6 months
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