Rina Ding
Machine Learning Scientist at Natera
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolRina Ding is a machine learning scientist with a strong academic-to-industry trajectory, blending a PhD-focused research background at UCLA in medical imaging informatics with applied roles at Merck, Microsoft, Johnson & Johnson, and Natera. She has led multimodal and self-supervised learning projects for cancer diagnosis and prognosis, published novel methods for pathology image classification, and built domain-driven graph transformers and GNNs informed by close collaboration with pathologists. Her work spans weakly supervised localization, multimodal fusion across radiomic-pathomic-genomic data, and image-captioning/VQA tasks for biomedical detection, with multiple conference acceptances and oral presentations. Based in Seattle and drawing on eight years of experience, she mentors students and consistently translates domain knowledge into model architecture innovations. Rina is motivated by using AI to solve high-impact clinical problems and aims to continue toward a PhD to push translational research further. An under-the-radar strength is her ability to pair information-theoretic principles with practical multimodal pipelines to choose fusion strategies that generalize across cohorts.
8 years of coding experience
7 years of employment as a software developer
Bachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at Case Western Reserve UniversityUniversity of California, Los Angeles
English, Chinese
Github Skills (1)
exchange1
Programming languages (1)
Github contributions (5)
rina-ding/ssl_luad_classification
Apr 2022 - Apr 2022
Contributions:21 commits, 46 pushes, 1 branch in 1 day
rina-ding/gat-mamba
May 2024 - Jun 2024
Contributions:30 pushes, 1 branch in 18 days
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