Activities

PROFESSIONAL EXPERIENCE

02/2023 – Present
Lead Bioinformatics Research Scientist
St. Jude Children’s Research Hospital

12/2022 – 02/2023
Senior Bioinformatics Research Scientist
St. Jude Children’s Research Hospital

10/2022 – 12/2022
Assistant Professor of Bioinformatics Research in Pediatrics
Weill Cornell Medicine

08/2021 – 10/2022
Senior Bioinformatics Analyst
Weill Cornell Medicine

03/2019 – 08/2021
Postdoctoral scholar
University of Chicago

08/2015 – 08/2018
Postdoctoral scholar
Missssippi State University

04/2013 - 06/2014
Visiting scholar
Miami University

RESEARCH INTERESTS

  • Computational methods of single cell sequencing data
  • Single cell sequencing data interpretation and visualization
  • Genetics and Evolution methods and their application
  • Biological/medical data interpretation and visualization
  • Bio-statistical methods and their application

EDUCATION

Xiamen University
Xiamen, China, 2011 - 2015
Ph.D. System Engineering/Bioinformatics

Xi’an Jiaotong University
Xi’an, China, 2009 - 2011
M.S. Control Engineering/Natural Language Processing

Xi’an Jiaotong University
Xi’an, China, 2005 - 2009
B.S. Automation

CONTACT

Advanced Research Center - St. Jude Children's Research Hospital
262 Danny Thomas Place
Memphis, TN, 38105
USA

Email: lei.li[at]stjude.org or leilioxford[at]gmail.com

PROJECTS

RSVrecon: An efficient workflow to screen clinically relevant genomic variations of RSV from NGS data
   
   
Improving Data Interpretation for High Resolution Spatial Omics using computational approaches
   
   
HTOreader: hybrid single cell demultiplexing strategy that increases both cell recovery rate and calling accuracy.
   
   
VGenes: an integrated graphical tool for efficient, comprehensive and multimodal analyses of massive B-cell repertoire sequences.
   
   
Librator: a platform for optimized sequence editing, design, and expression of influenza virus proteins.
   
   
LinQ-View is a joint single cell analysis strategy that could integrate information from both transcriptome and surface protein markers for cell heterogeneity identification.
   
Cookie: Selecting representative samples from single cell atlas using k-medoids clustering.
   
   

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