Panos Galanis , Ph.D.
About Panos Galanis , Ph.D.
Panos Galanis, Ph.D., is a Senior Quantitative Risk Analyst specializing in Market Risk at UBS Group, where he has worked since 2019. He holds a Ph.D. in Engineering and Statistics from the University of California, Berkeley, and has experience in model validation, project management, and developing IT solutions for data quality assessment.
Work at UBS
Panos Galanis serves as a Senior Quantitative Risk Analyst in the Market Risk department at UBS, a position he has held since 2019. His responsibilities include model validation for both Pillar 1 and Pillar 2 Market Risk models, which are crucial for regulatory compliance and risk assessment. He is based in the Zürich Area, Switzerland, where he contributes to project planning and management within the organization. His role involves developing internal R and Python packages aimed at process automation and standardization, enhancing operational efficiency.
Education and Expertise
Panos Galanis has an extensive educational background in engineering, statistics, and finance. He earned his Doctor of Philosophy (Ph.D.) from the University of California, Berkeley, where he studied from 2009 to 2014. Prior to this, he obtained a Diploma in Civil Engineering from the National Technical University of Athens, studying from 2003 to 2008. He also holds a Master's degree in Finance from the University of Zurich, which he completed between 2016 and 2018. His diverse academic qualifications support his expertise in quantitative risk analysis.
Background
Panos Galanis has a strong foundation in engineering and quantitative analysis, beginning with his studies in Civil Engineering at the National Technical University of Athens. His academic journey continued with a focus on statistics and finance, culminating in a Ph.D. from the University of California, Berkeley. This background has equipped him with the analytical skills necessary for his current role in market risk analysis at UBS.
Achievements
In his role at UBS, Panos Galanis has made significant contributions to model validation for market risk, ensuring compliance with regulatory standards. He has developed IT solutions for automated assessment of input data quality, which includes techniques for anomaly detection and data imputation. His work in creating internal R and Python packages has facilitated process automation and standardization, reflecting his commitment to enhancing operational practices within the organization.