Turning theoretical breakthroughs into deployed systems.
I'm Adam Lin, PhD. Previously focused on high-stakes clinical ML, I now specialize in the end-to-end engineering of Multimodal Generative AI. My work spans Large Language Models and Vision architectures, covering everything from fine-tuning to scalable deployment.
Let's connect.
Open to Research Scientist and Machine Learning Engineer roles focused on Multimodal Generative AI, Agents, and LLM architecture.
Selected Publications
Differential Predictability of Preterm Birth Types: Strong Signals for Indicated Cases versus Limited Success in Spontaneous Preterm Birth
medRxiv • First Author
A LUPI distillation-based approach: Application to predicting Proximal Junctional Kyphosis
Machine Learning for Healthcare (MLHC) • First Author
A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort
BMC Pregnancy and Childbirth • First Author
Interpretable prediction of necrotizing enterocolitis from machine learning analysis of premature infant stool microbiota
BMC Bioinformatics • First Author
Experience
Adjunct Professor
Columbia University
Primary instructor for Artificial Intelligence, developing curriculum and teaching foundational AI concepts and modern machine learning paradigms.
Graduate Research Assistant
Columbia University
Developing novel machine learning algorithms for healthcare applications, including predictive modeling for preeclampsia and preterm birth using large-scale clinical datasets.
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Invited Talks
Ethical and Effective Use of AI
Jan 2026NAVBO Virtual Roundtable • Panelist
Predicting Preterm Birth with Machine Learning
Nov 2025Columbia MFM Research Meeting • Guest Speaker