Ousseynou Ndiaye

● Generating data visualizations for scientific journals and reports with R ggplot2 and Python matplotlib ● Developing deep learning model (convolution neural network) to identify neural tube defects images ● Principal Component analysis (PCA) on gene expression profile using Python Scikit-learn library ● Statistical analysis of cellular organelle circular distributions with R CircStat package ● Statistical analysis of gene expression data from RNAseq with Python Scipy.stats and Scikit- learn libraries and R Bioconductor ● Next-generation sequencing (RNA-seq) analysis with Galaxy andUnix-script base STAR and Homer packages. ● Developing RNA-seq analysis pipeline form raw data (fastq files) to gene profile reports (in csv and Excel files). Data are stored in NIH NCBI GEO database https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112364 ● Image analysis with NIH ImageJ, Python Opencv, PIL and scikit-image libraries ● Imaging data collection with Zeiss LSM 880, Zeiss LSM 710 and CSU spinning-disk confocal microscopy ● qPCR for gene expression profiling with QuantStudio Real-Time PCRSYSTEM (Thermo-Fisher)

● Collaborated with cross-functional teams to compile and communicate findings of data analysis exercises ● Utilize modern molecular and cell biology methods/techniques such as CRISPR, qPCR, and RNA-seq to perform genetics study for cell differentiation induced by mechanical forces. ● Conducted circular statistics and image data analysis with Python and R scripts. Developed Python programs for visualizing experimental data with Matplotlib library.

• Demonstrated that the mechanical forces direct the polarity of atypical wnt pathway on epithelial cell differentiation with animal model Xenopus laevis, using qPCR, gene knockdown, in situ hybridization technique • Studied gene expression for cilia-related genes with lentivirus and mammalian cell culture system.