Anthony Lamattina
About Anthony Lamattina
Anthony Lamattina is a Scientist I specializing in Computational Biology at Wave Life Sciences, where he has worked since 2022. He has a strong background in bioinformatics, with previous roles at El-Chemaly Lab and Broad Institute, and holds a Master of Science in Computational Biology and Quantitative Genetics from Harvard T.H. Chan School of Public Health.
Work at Wave Life Sciences
Anthony Lamattina has been employed at Wave Life Sciences since 2019. He initially held the position of Associate Scientist in Computational Biology for three years, during which he contributed to various projects in the field. In 2022, he advanced to the role of Scientist I, Computational Biology. In this current position, he continues to apply his expertise in computational biology, focusing on data analysis and machine learning applications.
Education and Expertise
Anthony Lamattina earned a Bachelor of Science (BS) in Biology from Northeastern University, completing his studies from 2010 to 2015. He furthered his education at Harvard T.H. Chan School of Public Health, where he obtained a Master of Science (M.S.) in Computational Biology and Quantitative Genetics from 2016 to 2019. His academic background supports his expertise in bioinformatics, data visualization, and pipeline development, utilizing programming languages and tools such as Python, R, and KNIME.
Background in Research
Prior to his current role at Wave Life Sciences, Anthony Lamattina gained extensive research experience. He worked at El-Chemaly Lab at Brigham and Women's Hospital in various capacities from 2015 to 2019, including Technical Research Assistant I, II, and Senior Technical Research Assistant. He also served as a Research Assistant at the Broad Institute for seven months in 2014. These positions allowed him to develop a strong foundation in computational biology and bioinformatics.
Technical Skills and Proficiencies
Anthony Lamattina has developed a strong skill set in computational biology, particularly in data visualization and machine learning applications. He is proficient in programming languages such as Python and R, as well as tools like KNIME. His technical skills enable him to contribute effectively to projects in the biotechnology sector, enhancing data analysis and interpretation.