Alexis Yang
Senior Health Informatics Analyst
Washington, District of Columbia, United States
Summary
🤩
Rockstar🎓
Top SchoolAlexis Yang is a Senior Health Informatics Analyst with nine years of cross-disciplinary experience applying data science, NLP, and generative AI to healthcare product and analytics problems. Currently at Prime Therapeutics, she blends clinical data insight with production-focused ML practices, having transitioned from product innovation and earlier data science roles to operationalize models for real-world impact. Her GitHub work on high-performance computer vision (FastMOT) shows hands-on expertise optimizing models and inference — including INT8 calibration and algorithmic improvements like DIoU NMS — highlighting a focus on efficiency and interpretability. Trained with an MS in Business Analytics from George Washington University and a BBA in Accounting, she pairs technical modeling skills with strong product and financial acumen. Based in Washington, D.C., Alexis is driven to make AI systems both performant and auditable in regulated healthcare settings.
9 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Business Analytics, Master of Science - MS, Business Analytics at The George Washington UniversityBachelor, Business Administration - Accounting, Bachelor, Business Administration - Accounting at Portland State University
English, Chinese
Github Skills (10)
tensorrt10
object-detection10
computer-vision10
machine-learning10
numpy10
cprogramming-language7
onnx7
deep-learning7
c-language7
tensorflow4
Programming languages (5)
Github contributions (5)
GeekAlexis/FastMOT
Jun 2020 - Jan 2022
High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
Role in this project:
ML Engineer
ML EngineerContributions:2 releases, 11 reviews, 215 commits in 1 year 7 months
Contributions summary:Alexis's commits focus on optimizing and refining object detection models within the fastmot repository. Their work involves modifying and adapting the models, including the addition of INT8 calibration for better performance. The user also contributed to integrating new algorithms, such as DIoU NMS, and addressed several performance issues by optimizing and vectorizing critical functions, such as those used in optical flow. Moreover, the user has demonstrated a strong grasp of computer vision techniques and experience with various deep learning models.
multiple-object-trackingreal-timeedge-computingcomputer-visionobject-detection
GeekAlexis/chess-robot
Mar 2019 - Dec 2020
Contributions:37 pushes, 1 branch in 1 year 8 months
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