Martin Skarzynski, Ph.D., M.P.H., joined the Biostatistics Branch (BB) as a Cancer Prevention Fellow in November 2018. He holds an M.P.H. in epidemiologic and biostatistical methods for public health and clinical research, and a certificate in data science from the Johns Hopkins Bloomberg School of Public Health. He earned his Ph.D. in tumor biology from Georgetown University, an M.S. in biotechnology from Jagiellonian University in Poland, and a B.A. in biology from St. Mary’s College of Maryland. Dr. Skarzynski is passionate about bioinformatics, data science, epidemiology, and statistical computing. He uses the Python and R programming languages and command line tools to explore, analyze, visualize and present data, and he has a strong interest in reproducibility, scientific publishing workflows, and open data/science best practices. Dr. Skarzynski applies his computational skills in combination with his genomics and immunology background to the study and prevention of cancer. In DCEG, Dr. Skarzynski is working under the mentorship of Hormuzd Katki, Ph.D., senior investigator, BB. Outside of DCEG, Dr. Skarzynski is co-chair of the Bioinformatics and Data Science Department at the Foundation for the Advancement of Education in the Sciences (FAES), where he has been an instructor since 2015, and he currently teaches Introduction to Python. Dr. Skarzynski is also an instructor for Software and Data Carpentry, a non-profit organization that teaches computational skills.
Research interests: My primary research interest is in understanding cancer risk factors by combining scientific expertise from diverse fields with machine intelligence. I believe I am uniquely equipped to bridge the gaps between scientific disciplines and deliver on the promise of data science in cancer research. My preferred tools are R and Python, open source programming languages kept on the cutting edge by their active and supportive communities. Through research and teaching, I am constantly improving my ability to obtain, tidy, explore, transform, visualize, model, and communicate data. I aim to utilize my technical skills and science background to become a leader among the next generation of multidisciplinary cancer researchers.