Michail Kovanis
About Michail Kovanis
Michail Kovanis is a Senior Machine Learning Engineer at Faurecia, where he has worked since 2018. He specializes in developing machine learning solutions and mentoring teams in MLOps principles, with a strong academic background in Computational and Applied Mathematics.
Work at Faurecia
Michail Kovanis has been employed at Faurecia since 2018 as a Senior Machine Learning Engineer. He operates within the Greater Paris Metropolitan Region and has contributed to various projects over his six years at the company. His responsibilities include leading the development and deployment of Deep Learning models for visual inspection of car parts. Additionally, he co-developed a cloud-based application utilizing a micro-services architecture, which is now deployed in over 15 Faurecia plants worldwide.
Previous Experience at INSERM
Before joining Faurecia, Michail Kovanis worked at INSERM from 2014 to 2018. He initially served as a PhD Candidate for three years, during which he focused on research in the field of Computational and Applied Mathematics. Following this role, he transitioned to a Data Scientist position for ten months, where he applied his expertise in data analysis and machine learning.
Education and Expertise
Michail Kovanis holds a Doctor of Philosophy (PhD) in Computational and Applied Mathematics from Université de Paris, which he completed from 2014 to 2017. He also earned a Master's degree in Computational and Applied Mathematics - Complex Systems from Ecole Polytechnique in 2014, and another Master's degree in the same field from the University of Gothenburg in 2013. His academic background is complemented by a Bachelor of Science (BS) in Physics from the National & Kapodistrian University of Athens, obtained in 2012.
Machine Learning Innovations
Kovanis has developed an unsupervised machine learning algorithm designed for clustering time-series signals based on shape characteristics. This innovation is currently pending a patent and aims to predict root causes of defects. He also mentors Data Scientists and DevOps teams in implementing MLOps principles within data and Deep Learning pipelines, showcasing his commitment to advancing machine learning practices.
Technical Skills and Tools
Michail Kovanis utilizes a diverse tech stack in his work, which includes programming languages and tools such as Python, Java, and Javascript. He also employs PostgreSQL for database management, Kubernetes for container orchestration, Kafka for stream processing, and Redis for data storage. This combination of technologies supports his development of machine learning solutions and enhances operational efficiency.