I use computational tools to analyze genetic and epigenetic information from our labs. We hope to learn how cell type, drug treatments, and environment influence gene expression. Without a solid understanding of biology and experimental motivation, it’s impossible to apply computational methods effectively. In the BSCB program at Carnegie Mellon, I gained not only a fundamental skill set in computer science, but also a strong foundation in genetics and cell biology. The most important skill I learned, however, was how to better understand and communicate scientific ideas.
The MSCB program at CMU is interdisciplinary by nature. Coming into the program as a molecular biologist, I acquired lots of knowledge and skills in coding, algorithms and machine learning. Moreover, it introduced these concepts in the context of biology, which taught me how to leverage biology by using computer science.My job at Roche is to analyze the big data generated by prototypes and to optimize their performance at system integration level. Roche is an exciting and interdisciplinary work space where biologists, chemists work with electronic and software engineers to make the dream of personalized medicine true. The MSCB program helped me to advance my career and greatly expanded my horizon.
As a consultant, I have worked in problem solving in a business context. Although the context is different, my work at CMU prepared me well for day-to-day work. As with computational biology research, often multiple iterations are required to derive a solution acceptable to all parties involved. Similarly, the themes of the scientific process – generating a hypothesis, testing, and correcting the hypothesis – carry over from research to consulting.
Because CMU gave me an early grounding in programming, computational modeling and biology, many fascinating research areas and career paths have opened up for me. I especially appreciate the fact that this field is so diverse, allowing me to tailor my research projects to my evolving interests whether they be more biological (i.e. virology and molecular self assembly) or more computational (i.e. machine learning and optimization).
The solid training in computational biology that I earned at CMU helped me transition from an undergraduate degree in biochemistry to a software engineering job in biotech. My life is no different from that of many other software engineers – coffee, meetings, stackoverflow and coding! Yet a lot of my work involves communicating with computational biologists, understanding their needs and finding solutions for their requirements. Thanks to CMU, I understand our scientists better than other engineers do.
I came to CMU without any experience in programming and algorithms. I quickly connected knowledge in these areas, which prepared me for implementing the algorithms and models that I now design. I also received a biology education in both a broad and systematical way so that I have a better understanding of the data I work with every day. My experience at CMU is definitely the most critical factor in my career success so far.
I was lucky enough to take the first comp bio class at CMU before it was expanded to become a focused discipline. My training in CS and Biology from CMU enabled me to think systematically about solving real biological problems with the power of computational tools and algorithms. My focus area has been in bioinformatics for genome sequencing and the analysis of genomic data to answer various biological questions. I leveraged this experience in my transition to Product Management where I now strategically drive the direction of informatics products to meet market needs.