Stanford University
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Tairan Qiu
Assistant Professor of Education
BioI, 邱泰然, Tairan Qiu (she/her/她), am an Assistant Professor of English Language Arts and Literacy Education in the Graduate School of Education. Prior to my doctoral studies, I was an ELA and ESOL teacher. As a transnational migrant and East Asian woman, my research is at the intersection of language, literacy, culture, race, gender, and im/migration. My research agenda is oriented around critically unpacking the dynamic language and literacy practices of transnational youth and families, centering their stories and experiences to shape research-informed change in their schools, communities, and homes, and sustaining their whole cultural, linguistic, and literate repertoire.
I am committed to working in and with historically marginalized communities through community-centered research, teaching, and service. For example, funded by the Community Literacies Collaboratory, I am the co-founder of the University of Houston-Project Row Houses Community Literacies Center in Third Ward, Houston, TX. This arts-based Community Literacies Center offers weekly multiliteracies events for predominantly Black, Asian, Latiné, and translingual children, youth, and caregivers, where they celebrate their vibrant literacy practices in community with one another. Currently, I am working with a collective of nine transnational Girls of Color to narrate their transnational and translingual literacies and advocacy in and across their respective communities. -
Xiaojie Qiu
Assistant Professor of Genetics and, by courtesy, of Computer Science
Current Research and Scholarly InterestsAt the Qiu Lab, our mission is to unravel and predict the intricacies of gene regulatory networks and cell-cell interactions pivotal in mammalian cell fate transitions over time and space, with a special emphasis on heart evolution, development, and disease. We are a dynamic and interdisciplinary team, harnessing the latest advancements in machine learning as well as single-cell and spatial genomics by integrating the predictive power of systems biology with the scalability of machine learning,