Gerald Nwanne

• Identify and develop software tools for efficient data integration and for mining diverse types of biological data
• Share modern computational methods and bioinformatics solutions within the company and outside if not proprietary
• Developed Docker containers to simplify single-cell RNA and ATAC-seq data analysis, enhancing research collaboration using R programming .
• Conducted comprehensive bulk RNA-seq analysis on kidney-specific data, advancing nephrology research.
• Evaluated and selected single-cell genomics analysis tools, optimizing research methodologies.
• Conducted detailed bulk kidney RNA data analysis using advanced techniques like Limma Voom and DESeq2, ensuring data reliability and contributing to nephrology knowledge.

• Worked with staff to optimize an R package that would assess students’ performance in a particular class
• Assisted professors in accessing and using the web-based learning management system Canvas.
• Diagnose, troubleshoot, and resolve various issues on the software Canvas.

• Data management querying, filtering, extraction, visualization, merging unstructured datasets with missing values using R
• Next Gen sequencing technology, tool usage, and data analysis
• Proficiency in analyzing genes that are differentially expressed using DESEq2 R package
• Understanding & usage of biostatistics and bioinformatics algorithms
• Proficiency in programming in Python and R
• Working knowledge of microarray genotyping and sequencing pipelines
• Proficiency in analyzing bulk RNA-seq and single cell RNA-seq
• Mastery of analyzing large genomic data using High performance Computing.
• Multivariate analysis: PCA/LDA, model selection with regularization
• Data mining and Machine learning algorithms for ‘Big data;: cluster, decision tree, artificial neural with multilayer, perceptron, kernel machine, hidden Markov model
• Regression and classification: linear/non-linear regression, logistic regressions, mixed effect models, robust analysis

• Analyzed various datasets, performing analysis and data manipulation using Python, SQL, R as well as data visualization to aid students’ performance as well as targeted decision making.
• Conducted qualitative and quantitative research using various machine learning algorithms such as linear, logistic regression, decision trees etc. to identify students’ habits, patterns, and trends.
• Building statistical models to analyze trends, create predictive models for actionable intelligence and measuring student’s performance using python libraries such as pandas, NumPy, sklearn.
• Actively trained and mentored students in software skills as well as troubleshooting computers and software.
• Encouraging students to participate in science fairs, competitions, and expos, and preparing students for these events.

· Using CLI to do shell scripting