What is your role at MRCG at LSHTM?

I currently serve as a Bioinformatics Support Officer at MRCG at LSHTM.

Could you share insights into your current project?

I'm currently developing a variant calling pipeline tailored for Illumina and Nanopore amplicon reads, specifically for the Plasmodium falciparum genome. Integrating Nextflow, Bash, R, and Python ensures operational smoothness and adaptability. This project aligns with our commitment to deliver accurate genomic analysis results.

How did you begin your journey in Data Science?

My interest in science, math, and technology led me to pursue computer science. Inspired by my dad's involvement in medical research, I ventured into bioinformatics. Along the way, I developed a strong fascination for data science, especially its application in genomics research.

Can you describe the work environment at MRCG?

MRCG fosters a highly committed research environment, encouraging collaboration and innovation in medical research. Working closely with lab scientists provides exciting challenges and facilitates a deeper understanding of biological concepts.

What's the most enjoyable aspect of working with your team?

The exceptional people in my team make working here truly enjoyable. We thrive on remarkable teamwork, strong support, and an approachable atmosphere that fosters seamless collaboration.

Beyond work, what are your hobbies and interests?

Outside of work, I enjoy playing rugby with colleagues, swimming, gaming, socializing with friends, and engaging on social media.

What do you do when you're not working?

During my free time, I delve into reading, watching tutorials, or participating in various social activities.

Where do you see yourself post-MSc?

Post-MSc, I envision making significant contributions to medical research, particularly in cancer studies. In the short term, pursuing a Ph.D. in health data science is my goal. I aim to integrate my bioinformatics, machine learning, deep learning, analytics, and statistical knowledge to design effective diagnostic models for seamless malignant tumor detection