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+---
+name: "Meichen Huang"
+role: "PhD Student"
+title: ["PhD Student"]
+avatar: "../../assets/meichen-huang.jpeg"
+bio: "Graph neural networks and computational genomics for autism spectrum disorder risk prediction"
+email: "meichen.huang@bcm.edu"
+linkedin: "https://www.linkedin.com/in/meichen-huang/"
+github: "https://github.com/meichen-huang"
+googleScholar: "https://scholar.google.com/citations?user=CMblpKEAAAAJ"
+weight: 50
+---
+
+Meichen is a PhD student in the Quantitative & Computational Biosciences program at Baylor College of Medicine, co-mentored by Dr. Anthony Zoghbi and Dr. Zhandong Liu. Her doctoral research develops graph-based deep learning methods to predict autism spectrum disorder risk from whole-genome sequencing data. By integrating rare and common genetic variants within multi-omic biological networks, her work aims to capture the complex genetic architecture underlying ASD and contribute to earlier identification of at-risk individuals.
+
+Prior to her PhD, Meichen worked as a Bioinformatics Programmer at BCM (2022-2024) under Dr. Joshua Shulman and Dr. Zhandong Liu, where she led single-cell RNA-seq analyses across *Drosophila* models of Alzheimer's, Parkinson's, and Huntington's diseases, developed interactive visualization tools, and contributed to genomic variant prioritization algorithms. She holds a B.S. in Mathematics and M.S. in Statistics from UT Dallas, where she worked with Dr. Yulia Gel on applying machine learning and topological data analysis to problems in crop yield forecasting and environmental health, resulting in publications at AAAI and the American Meteorological Society.