Head of Causal and Predictive Modeling in immunology, Oncology, and Neuroscience.Lead a team to develop, implement, and improve advanced analysis workflow for modelingintegration and visualization of large-scale multi-Omics datasetsConduct Bayesian causal modeling using state-of-the art machine learning technologies (such asReverse Engineering and Forward Simulation, REFS) and statistical analysis methods forBiomarkers and drug targets identification in Immunology and Oncology -- autoimmune diseases: Inflammatory Bowel Disease (IBD), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), atopic dermatitis (AD) -- Oncology datasets: Multiple Myeloma, AML, DepMap datasets -- Neuroscience: AMP-AD, ADNI, Digital Twin projectsConduct study design for causal and predictive modeling to study disease Mechanism of Action(MoA) and drug targets identification for Immunology, Oncology, and Neuroscience.

• Develop scRNASeq analysis workflow and conduct statistical analysis methods for CD40L activation marker project for Systemic Lupus Erythematosus (SLE).• Conduct study design and data analysis of SLE RNA-Seq big data project.• Conduct Bayesian Causal Network to study disease MOA for SLE.• Develop scRNASeq analysis workflow and conduct statistical analysis methods for SLE andParkinson’s disease (GBA-PD iPSC Project)• Provide statistical support for RORgt inhibition project for psoriasis

Conduct statistical analyses on RNA sequencing data for Huntington’s disease.• Lead client projects in biology, pharmaceutical and healthcare area.• Apply intensive computational and statistical techniques to build predictive and causal models to address clients’ questions using GNS’ proprietary Bayesian inference platform.

• Conduct statistical analyses on Next Generation Sequencing (NGS) data for Alzheimer’s disease.

• Perform statistical analyses on RNA sequencing data for Autism Spectrum Disorders (ASD).• Implement project pipelines on Amazon cloud for RNA sequencing data QC, integration, and analyses.

• Develop integrative analytical approaches to analyze The Cancer Genomic Atlas (TCGA) data.• Develop novel gene-set enrichment analysis methods for network inference.

Probability and Statistics