Programming & Data Analysis:
Proficient in R, Python, and Unix, utilizing tools like Pandas, JupyterLab, ggplot2, Bioconductor, and Conda for efficient and reproducible data analysis.
Multiomic Analysis:
Skilled in integrating proteomics, transcriptomics, metabolomics, and genomics data to identify key relationships, such as uncovering taxonomic and functional patterns across large-scale datasets.
Bioinformatics Tools:
Experienced with BLAST, QIIME, Benchling, CLC, and Bioconductor to perform taxonomic, functional, and community analyses of ‘omics data in diverse environments.
Data Engineering:
Designed and automated scalable workflows for next-generation sequencing data, leveraging pipeline managers like Snakemake and Nextflow to optimize performance, reproducibility, and efficiency.
High-Performance Computing:
Optimized bioinformatics pipelines using HPC resources (SLURM), enabling the analysis of large-scale microbial and viral datasets with enhanced speed and efficiency.
Cloud Platforms & Databases:
Skilled in managing and analyzing large datasets using SQL, AWS, Google Cloud Platform, and Docker to deliver scalable, cloud-based solutions.
Documentation & Version Control:
Ensured transparency and reproducibility through clear, versioned documentation in Jupyter Notebooks and HackMD, with centralized project management in Git repositories.
Data Visualization & Statistics:
Developed clear, interpretable visualizations and performed advanced statistical analyses, including Bayesian methods, to uncover actionable insights from complex biological data.
Project Management Skills
Multi-Project Coordination:
Successfully managed multiple projects, meeting tight deadlines while delivering scalable bioinformatics solutions tailored to organizational goals.
Cross-Functional Collaboration:
Acted as a liaison between lab scientists and software engineers to develop user-friendly tools for NGS data analysis, translating biological needs into computational workflows.
Documentation & Organization:
Maintained centralized project repositories and streamlined communication across teams, ensuring seamless collaboration and reproducibility of analyses.
Communication & Mentorship Skills
Scientific Communication:
Adept at translating complex scientific topics into accessible insights, supporting stakeholders across varying scientific and technical backgrounds.
Public Engagement:
Led sessions at national scientific conferences to provide field-specific learning opportunities, including workshops on integrating bioinformatics and data science tools.
Research Dissemination:
Synthesized research findings into actionable presentations and print materials, contributing to publicly available pandemic-related resources and high-impact publications.
Mentorship:
Fostered relationships with junior scientists, guiding them in skill development and career exploration, including strategies for achieving research and technical milestones.
Problem-Solving & Innovation
Scalable Solutions:
Designed and implemented automated pipelines to process multiomic datasets, improving workflow efficiency and enabling downstream analyses at scale.
Workflow Optimization:
Refactored NGS pipelines to reduce computational costs and processing time by 30%, increasing the speed of high-throughput data analyses.
Data Integration:
Troubleshot and optimized workflows to integrate proteomic and genomic datasets, enabling robust cross-platform analyses with actionable biological insights.
Innovative Tools:
Partnered with multidisciplinary teams to develop new bioinformatics tools tailored to user needs, improving analysis accuracy and accessibility for lab scientists.