Ryan Murphy, Ph.D.
About Ryan Murphy, Ph.D.
Ryan Murphy, Ph.D., is a data scientist with extensive experience in academia and industry, currently working at FINRA. His research focuses on deep learning and financial datasets, and he has published in prestigious conferences such as ICLR and ICML.
Work at FINRA
Ryan Murphy has been employed at FINRA as a Data Scientist since 2022. In this role, he focuses on analyzing financial data and developing algorithms to identify anomalous market activities. His work involves utilizing advanced technologies and methodologies to enhance the understanding of market dynamics. Ryan operates remotely, contributing to FINRA's mission of ensuring market integrity and protecting investors.
Education and Expertise
Ryan Murphy holds a Doctor of Philosophy in Statistics from Purdue University, where he studied from 2014 to 2021. He also earned a Master's degree in Mathematical Statistics and Probability from the same institution between 2014 and 2016. Additionally, he completed a Bachelor of Arts in Economics and Mathematics at the University of Rochester from 2008 to 2012. His educational background equips him with a strong foundation in statistical analysis and data science.
Background
Ryan Murphy's professional journey includes various roles in data science and research. He served as a Research Assistant at the Human Language Processing Lab at the University of Rochester from 2008 to 2010. He later worked at Mathematica as a Research Assistant/Programmer from 2012 to 2014 and as a Graduate Teaching Assistant at Purdue University from 2014 to 2018. His experience also includes a position as a Data Scientist at SFL Scientific, a Deloitte Business, for five months in 2021 and a Summer Internship at Novartis in 2020.
Research Contributions
Ryan Murphy has published research in prominent conferences such as ICLR and ICML, focusing on deep learning applied to graphs and sets. His contributions to the field demonstrate his expertise in advanced data analysis techniques. He has collaborated with data engineers to operationalize methods, including the development of a monitoring and surveillance dashboard, showcasing his ability to integrate research with practical applications.
Technical Skills
Ryan Murphy possesses extensive experience in handling large financial datasets, utilizing technologies such as PySpark and AWS. He has developed algorithms for identifying anomalous market activity through classical unsupervised learning and signal processing techniques. Additionally, he has employed deep learning-based clustering and anomaly detection methods to improve insights into market activities, reflecting his technical proficiency in data science.