Bjorn Burkle
About Bjorn Burkle
Bjorn Burkle is a Data Scientist II at Cambridge Mobile Telematics, specializing in data quality management and machine learning techniques. He holds a PhD in Elementary Particle Physics from Brown University and has conducted significant research in collaboration with CERN.
Work at Cambridge Mobile Telematics
Currently, Bjorn Burkle serves as a Data Scientist II at Cambridge Mobile Telematics, a position he has held since 2022. In this role, he applies his expertise in data analysis and machine learning to enhance telematics solutions. His work involves managing and analyzing large datasets to improve the accuracy and effectiveness of mobile telematics applications.
Education and Expertise
Bjorn Burkle earned a Doctor of Philosophy (PhD) in Elementary Particle Physics from Brown University, where he studied from 2016 to 2022. Prior to that, he completed a Bachelor of Science (BS) in Physics at UC Santa Barbara from 2012 to 2016. His academic background provides him with a strong foundation in advanced statistical analysis and machine learning techniques.
Background
Before joining Cambridge Mobile Telematics, Bjorn Burkle worked as a Doctoral Student at Brown University from 2016 to 2022. During this time, he conducted significant research, including testing silicon sensors in a clean room environment for the Phase II upgrade of the CMS outer tracker. He also collaborated with CERN and the CMS collaboration on the VH(H->cc) process using collision data.
Research and Publications
Bjorn Burkle has contributed to the field of particle physics through various research initiatives. He utilized computer vision techniques to identify the decay of hadronically decaying top jets, with his findings published on arXiv. Additionally, he performed dosimetry studies to assess the energy spectrum of neutron test reactors, contributing to the understanding of neutron fluence during CMS upgrade material testing.
Technical Skills
Bjorn Burkle possesses extensive experience in machine learning and statistical analysis techniques, which he developed through his work with the CMS collaboration. His technical skills include data quality management, computer vision, and dosimetry studies, making him proficient in handling complex datasets and deriving meaningful insights from them.