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Welcome to the Maizie Zhou Lab!

The overarching goal of my lab is to understand how we generate intelligent behavior through normal brain development and learning-induced plasticity, and the consequences of defects in these processes. We investigate multiple dimensions of these questions, spanning computational genomics, bioinformatics, computational neuroscience, and machine learning. Our approach tackles a range of data science problems, including designing new algorithms for high-throughput sequencing with applications to personal and cancer genomes; data mining of large cohort studies in neurological diseases to link risky genes and phenotypes; and understanding dynamical behavior of neural circuits, in natural and artificial neural networks.

Our approach involves several interconnected areas:

Develop algorithms to fully reconstruct personal or cancer genomes and detect genome-wide large structural variants including copy number variants (CNVs) based on high-throughput sequencing data.
Use Machine Learning techniques to take advantage of large genomic data and text mining of published research to investigate clinical relevant variants and phenotypes in neurodevelopmental disorders such as autism.
Use Whole Genome Sequencing technologies and Hominid Ancestral Population analysis to understand the genetic basis of cognitive functions in animal models, particularly based on monkeys.
Use Artificial Intelligence to understand the development of the human brain and how cognitive functions improve through training and development.