Yavor Panev
About Yavor Panev
Yavor Panev is a Fire Engineer at Arup with expertise in computational modelling and machine learning solutions for fire safety engineering.
Current Role at Arup
Yavor Panev is a Fire Engineer currently working at Arup since 2018. He leverages his expertise in computational modelling of structural fire engineering problems to deliver insightful solutions in various projects. His role involves working on the safety and integrity of structures under fire conditions, contributing to the enhancement of fire safety engineering practices within the company.
Previous Roles at Arup
Before stepping into his current role, Yavor Panev had several stints at Arup. From 2016 to 2018, he served as a Graduate Fire Engineer in Edinburgh, United Kingdom. He also participated as a Vacation Student in 2015, gaining experience in multiple locations including Edinburgh and Glasgow. These roles allowed him to build a strong foundation in fire engineering early in his career.
Educational Background
Yavor Panev holds a Master of Engineering (MEng) degree in Structural Engineering with Fire Safety from the University of Edinburgh, where he studied from 2011 to 2016. Prior to this, he attended the American College of Sofia from 2006 to 2011. His academic journey provided him with a solid grounding in both structural engineering and fire safety principles, forming the foundation for his professional career.
Experience in Fire Engineering Tools and Methods
Yavor Panev has extensive experience in using advanced engineering tools for fire safety analysis. He employs LS-DYNA and Abaqus for computational modelling of structural fire engineering problems and utilizes CFD smoke modelling with Fire Dynamics Simulator (FDS). His proficiency in these tools underpins his ability to tackle complex fire safety challenges effectively.
Engineering Solutions Using Data Science
Yavor Panev integrates his fire engineering expertise with data science and machine learning to develop innovative solutions. He is proficient in Python, utilizing libraries such as numpy, pandas, and scikit-learn for data-driven engineering analysis. He also uses data visualization tools like matplotlib and seaborn to present his findings clearly. His interest lies in leveraging these skills to address contemporary challenges in fire engineering.