Welcome to my website
I am a person of many interests, and that is what I intend this blog to be. I envision this blog to be a place for me to share my experiences with new technology or software with the world in a way that is helpful and entertaining.
I am a bioinformatician in graduate school who enjoys programming, analyzing biological data, and tinkering with technology.
Most of my programming experience is in Python and Bash, but I am also highly proficient in C++, Java, and R. I like Python because it’s simple to use and has a library for basically everything. With Python and Bash, I am able to have a quick turnaround for projects and assignments since I am able to write and run my code so quickly. Recently, I also started GPU programming and working in CUDA. I think GPU’s are incredible pieces of hardware and – since I don’t see many GPU accelerated programs in the field of bioinformatics, I really want to show them how incredibly fast parallel programs running on GPUs are.
I have a lot of hobbies, and in my free time, I like to see how many different parts of my life I can make easier with programming and automation. These fun DIY projects like making a stock trading bot or an automated home media server are just one of the many ways I like to spend my free time.
Jun. 2021 – present
- Led a team of three at the Lewis Lab at UC San Diego in designing and implementing a bioinformatics pipeline using RNA-seq samples of CHO cells – a popular production cell line – to detect viruses in pharmaceutical settings.
- Investigated endogenous retroviruses across several CHO lineages.
Mar. 2019 – Sep. 2021
- Developed a bioinformatics pipeline with a team of three at the Alexandrov Lab to process DNA samples from over 2000 precancer patients and thousands of cancer patients from TCGA and PCAWG.
- Extracted and analyzed mutational signatures to better understand the progression of precancer to cancer.
- Currently providing ongoing technical support.
Apr. 2021 – Sep. 2021
- Converted existing nonnegative matrix factorization code for mutational signature extraction from PyTorch-GPU to CUDA with a team of three at the Alexandrov Lab for the San Diego Supercomputer Center’s GPU Hackathon.
- Runtime was improved by 14x and GPU memory usage improved by 3x.