Hi, there! I’m Gibraan Rahman, but everyone calls me Gibs. I’m a bioinformatics PhD from the Knight Lab at UCSD. My primary research interest is computational microbiology - focusing on statistical methods for differential abundance and diversity analysis. In my spare time, I like to collect vinyl, sing karaoke, and watch basketball (go Rockets!).
Contact me at: gibsramen (at) gmail.com
(†) denotes entry held online due to COVID-19
Education
Bioinformatics & Systems Biology, PhD
University of California, San Diego (Aug 2018 - June 2023)
Biomedical Engineering, BS
Concentration in computational biology
University of Texas at Austin (August 2014 - May 2018)
Teaching
Graduate Teaching Assistant - Network Biology and Biomedicine
University of California, San Diego (March 2023 - June 2023)
- Assisted with planning and organizing lectures
- Designed and graded homework assignments, exams, and final project
- Developed both mathematical & computational problems dealing with network biology
- Integrated published datasets into computational problems
Introduction to Statistics Co-Host†
Bioinformatics & Systems Biology Incoming Student Bootcamp
University of California, San Diego (September 2021)
- Presented a slide deck of statistical methods for bioinformatics
- Linear regression
- PCA
- tSNE/UMAP
“Compositional microbiome analysis using QIIME2 and related tools” Workshop Instructor†
Center for Microbiome Innovation/University of California, San Diego (March 2021)
- Helped answer questions relating to compositional data analysis
- Hosted workshop on hyperparameter tuning for differential abundance analysis using Songbird - Repo
- Hosted workshop introducing differential abundance analysis using BIRDMAn - Repo
“Software Engineering on a Team” Workshop Co-Host†
Bioinformatics & Systems Biology Incoming Student Bootcamp
University of California, San Diego (September 2020, 2021)
- Presented slide deck of best practices for collaborative software development
- Unit testing
- Style
- Git/GitHub
- Continuous integration
- Co-organized interactive demo of above skills involving student participation
- Helped >20 students modify code and submit pull requests to GitHub
- Answered student questions on collaborative code writing
Graduate Teaching Assistant - Network Biology and Biomedicine†
University of California, San Diego (March 2020 - June 2020)
- Assisted with planning and organizing lectures
- Established infrastructure for remote teaching, distribution of materials, & grading
- Designed and graded homework assignments, exams, and final project
- Developed both mathematical & computational problems dealing with network biology
- Integrated published datasets into computational problems
- Held virtual office hours for students to clarify material and receive assistance
Skills
Programming
I am most comfortable with Python for analysis, tool development, and general purpose scripting.
Occasionally I utilize tools written in R for bioinformatic analysis although I prefer Python.
For command line scripting I often develop bash scripts though I am still learning to properly use cut, awk, etc.
I have also recently begun implementing Stan into my workflows as I have been increasingly interested in Bayesian statistics.
On occasion I dabble in JavaScript and TypeScript for front-end development and interactive data visualizations.
Software Development
I am very comfortable on the UNIX/Linux command line for both development and analysis. When writing code, I make use of Git & GitHub for version control and open source. I integrate these tools with unit testing and continuous integration through GitHub Actions. I am a strong proponent of test-driven development and robust software testing.
Technologies
In my PhD work I have developed my skills working on high-performance clusters through job schedulers such as TORQUE and SLURM. I am especially fond of using Snakemake for modular and reproducible bioinformatics workflows. I frequently make use of AWS products including EC2, S3, and Lambda. In my spare time I have also started learning React.js and how to integrate with HTML and CSS.
Data Analysis
I have strengths in pandas and Numpy analysis for general data analysis. When tasked with machine learning problems or modelling I typically use sklearn and/or statsmodels. Additionally, I am very passionate and competent in data visualization through Matplotlib and Seaborn. For interactive visualizations I typically implement Bokeh. Recently I have also been learning Dask and xarray for parallelization and organization of multidimensional data. For microbiome specific analysis I use and develop for the QIIME 2 ecosystem of tools. More recently I have worked with PyTorch and Lightning for neural networks.
Presentations
“Acceleration of Bioinformatics Workloads” - CRISP 2020†
Gave presentation about computational improvements to metagenomics processing workflows.
Awards
Cockrell School of Engineering Honors Scholarship
University of Texas at Austin (2014 - 2018)
This scholarship is awarded to incoming first-years to the Cockrell School of Engineering at UT Austin and renewed each year contingent upon exemplary academic performance.