Summary
Karin Hrovatin is a bioinformatics scientist with 7 years of experience at the intersection of computational biology and machine learning, currently advancing protein design and Bayesian experimental design at Merck in Darmstadt. Her background spans single-cell data integration, deep learning for biological sequence-function translation, and productionizing academic methods into industry-ready tools and open-source Python packages. She combines hands-on modelling (PyTorch) and data engineering with practical experimentation strategies, including transfer learning for protein language models and optimization-guided lab design. Karin has a proven track record in knowledge transfer—mentoring students, giving workshops, and coaching innovation challenges—and has translated research during an MIT CSAIL exchange. Beyond typical bioinformatics roles, she communicates failed experiments and unpublished insights publicly through a dedicated blog, signaling a commitment to transparent, reproducible science. Fluent in bridging academia, startups, and industry, she brings interdisciplinary curiosity and product-minded rigor to biological data science.
7 years of coding experience
1 year of employment as a software developer
Master's degree Bioinformatics, Master's degree Bioinformatics at The University of Edinburgh
LISEAD, LISEAD at ESMT Berlin
Bachelor's degree Biotechnology, Bachelor's degree Biotechnology at University of Ljubljana, Faculty of Biotechnology
Mathematical class, Mathematical class at High School Bežigrad