Kizen Yamaguchi

I recently graduated from the University of Cambridge with a BA in Natural Sciences, specializing in genetics. My coursework covered various subjects such as pharmacology, neuroscience, biochemistry, and molecular biology. During my time as an undergrad, I completed internships that involved using Python for machine learning-based experimental design at Cellcraft and creating gene regulatory network models using Julia. I'm now aspiring to become a computational biologist.

During my involvement in the iGEM competition, I gained hands-on experience in molecular biology, and I also took on the role of team management. In the realm of dry lab work, I have a strong background in Python, which I've used for coding and designing experiments. On the wet lab side, I possess foundational knowledge in tissue engineering and have experience in designing culture media. Importantly, I have developed effective communication skills that allow me to bridge the gap between computer scientists and biologists, facilitating collaboration and understanding between these two disciplines.

In my third year of undergraduate studies, I concentrated on genetics as my major. This coursework introduced me to the world of omics, including Genome-Wide Association Studies (GWAS), and delved into various facets of biology such as systems biology, developmental biology, and evolution. As part of my academic journey, I conducted a dissertation on dynamic Flux Balance Analysis (FBA), which provided valuable insights into modeling biological systems.

In my second year, I ventured into a wide range of subjects, including pharmacology, molecular biology, and neuroscience, all of which deeply captivated my interest. I was particularly intrigued by personalized medicine.

My career aspirations are centered around the field of organoids and regenerative medicine. I am especially keen on harnessing algorithms, particularly for omics data analysis, to uncover molecular pathways that can be manipulated to influence organoid development. This aligns perfectly with my passion for bridging biology and computational methods to advance our comprehension of intricate biological systems.