Xiaoming Lu

Principal Scientist @Stylus Medicine

Boston, MA, US
MOBILE NUMBERS
+91 *********19

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WORK HISTORY

Sep 2024 — Present

Principal Scientist @Stylus Medicine

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Cambridge, MA, US

EDUCATION

N/A

University of Cincinnati Carl H. Lindner College of Business

Master of Science (MS), Business Analytics

2007 — 2011

Sun Yat-sen University

Bachelor of Science (BS), Bioscience

N/A

University of Cincinnati Carl H. Lindner College of Business

Graduate Certificate, Data Analytics

N/A

University of Cincinnati College of Medicine

Doctor of Philosophy (Ph.D.), Immunobiology

SKILLS

RPrestoResearchPlinkSap ProductsSqlPolymerase Chain Reaction (Pcr)Molecular BiologyMicrosoft ExcelSas ProgrammingApache SparkPythonHiveMicrosoft OfficeData AnalysisArena Simulation SoftwareTableauHadoopScienceWestern BlottingApache PigCell Culture

ABOUT XIAOMING LU

Translational scientist with 10+ years of experience in gene editing, off-target safety assessment, and autoimmune genetics. Expertise in vector biology, CRISPR-based therapeutic assessment, and high-throughput screening using human, mouse, and NHP cell models. Proven ability to lead cross-functionalteams and manage discovery-stage programs in collaboration with CROs and internal partners. Deep experience with data-driven therapeutic optimization and in vivo/in vitro assay developmentExperience highlights- Expert in gene editing off-target safety assessment, building platforms including off-target nomination, confirmation and functional validation- Lead a cross-functional effort to build a rapid-turnaround ddPCR screening system with CRO, automation, eLN (Benchling), app integration, and advanced data visualization- Lead projects to develop and optimize cell models derived from human, mouse, and non-human primates (NHP) to facilitate mutant screening and analysis- Deep understanding in the development of high-throughput screening assays, with a particular focus on massively parallel reporter assays- Deep understanding of human genetics with a focus on autoimmune diseases utilizing on genome-wide association studies (GWAS), fine mapping, statistical analysis, and MPRA etc to identify and validate causal genetic variants- Proficient in bioinformatics pipeline development, leveraging R, Python, and Linux tools for comprehensive data analysis.

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