Lambert Leong
Health AI portfolio for Lambert Leong, PhD
ALOHA
Lambert Leong, PhD
Senior AI/ML Applied Scientist
My expertise:
Healthcare Foundation Models, Medical Imaging Models, Clinical GenAI Evaluation, Structured EHR Modeling, AI Governance and Monitoring, Recommender Systems
Featured Post!
Agents Under the Curve (AUC)...
Agentic AI systems are on the rise in healthcare, but most only output binary decisions. This post provides options to turn those decisions into continuous scores so we can still use AUC for fair model comparison.
Area Under the Curve and...
Doctors and data scientist, here is an intro to IDI and NRI with a Python coded example. AUCs may not always be enough and this post allows you to go beyond the curve
Designing 3D Multip Processor Compute...
A Heuristic for Optimizing the Physical Layout and Network Topology of Integrated 3D Multi-chip Systems Under Temperature Constraints
Recent Posts
AI Predicts Your Insides From Your Outsides With Pseudo-DXA
A Quantitatively Accurate and Clinically Useful Generative Medical Imaging Model.
Optum AI: Healthcare Foundation Models and Clinical GenAI Evaluation
I work at Optum AI as a Senior AI/ML Applied Scientist, building health AI systems that span healthcare foundation models, structured EHR sequence modeling, clinical GenAI evaluation, model monitoring, and responsible AI review.
University of Washington OncoRad Research Core
I worked as a Postdoctoral Researcher at the University of Washington School of Medicine, building translational imaging, NLP, and computer vision tools for predictive biomarker research and clinical trial support.
Final DXA-nation
AI and deep learning predict all-cause mortality from single and sequential body composition imaging, showcasing the power of longitudinal models in healthcare.
Photo Mosaics with Nearest Neighbors: Machine Learning for Digital Art
We use machine learning to create digital art. K-nearest neighbors is used to build photo mosaics
Artificial Intelligence Can See Breast Cancer Before it Happens
Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms, A Case-Case-Control Study in 6369 Women
