Bing Xing Huo
About Bing Xing Huo
Bing Xing Huo is a Principal Investigator and Associate Director of Scientific Strategy and Partnership at the Broad Institute of MIT and Harvard. She has a diverse academic background and extensive research experience in neuroscience, data science, and neuroinformatics.
Current Role at Broad Institute
Bing Xing Huo serves as Principal Investigator and Associate Director of Scientific Strategy and Partnership at the Broad Institute of MIT and Harvard. She has held this position since 2022, contributing to the institute's mission of advancing biomedical research through innovative strategies and partnerships. Her role involves overseeing scientific initiatives and fostering collaborations that enhance research capabilities.
Previous Experience
Bing Xing Huo has an extensive background in research and consulting. She worked as a Postdoctoral Fellow at Penn State University for four months in 2015 and as a Visiting Research Fellow at the National Institute of Neurological Disorders and Stroke (NINDS) for four months in 2009. Additionally, she held various positions at Cold Spring Harbor Laboratory, including Consultant from 2022 to 2023, Computational Science Manager from 2019 to 2022, and Collaborative Scientist from 2016 to 2019. Her experience also includes roles at RIKEN as a Research Scientist from 2015 to 2019 and at Harvard Business School as a Research Associate from 2007 to 2008.
Educational Background
Bing Xing Huo has a diverse educational background. She earned a Bachelor of Science (BSc) in Mathematics and Physics from The University of Hong Kong. She also holds a Master of Science (MS) in Computational Neuroscience from New York University and a Master of Arts (M.A.) in Economics from Boston University. Furthermore, she completed her Doctor of Philosophy (Ph.D.) in Neural Engineering at Penn State University from 2010 to 2014. Additionally, she studied Software Engineering at Tsinghua University.
Research Focus and Interests
Bing Xing Huo's research extends beyond neuroscience to encompass data science applications in various biomedical fields. She focuses on developing scalable infrastructure for data mining and sustainable knowledge base development in neuroinformatics. Her work emphasizes the importance of FAIR data principles and open science, advocating for transparency and accessibility in research. She is also involved in cross-modal data integration, which is a key aspect of her research interests.