Michail Chatzizacharias
About Michail Chatzizacharias
Michail Chatzizacharias is a Deep Learning Research Engineer at Inria in Paris, specializing in optimizing deep learning pipelines for medical imaging. He has a background in software engineering and has contributed to projects involving endometriosis and semi-supervised learning techniques.
Work at Inria
Michail Chatzizacharias has been employed at Inria as a Deep Learning Research Engineer since 2023. His role involves analyzing MRI acquisition data to identify patterns that enhance the performance of deep learning models. He works under the supervision of Elise Mekkaoui and collaborates on a project concerning endometriosis, which includes partnerships with Matricis.ai and AP-HP. His focus is on optimizing deep learning pipelines to achieve high accuracy and efficiency in medical imaging.
Previous Employment History
Before joining Inria, Michail Chatzizacharias held several positions in the tech industry. He worked at TheraPanacea as a Full Stack Engineer from 2020 to 2021 and previously served as a Frontend Engineer there from 2019 to 2020. Prior to that, he was employed at Akka Technologies as a Frontend Engineer from 2018 to 2019, and as a Graduate Software Engineer for one month in 2018. He also completed military service with the Hellenic Army from 2015 to 2016 and worked at IMS PLIROFORIKI SA as a Full Stack Engineer from 2016 to 2017.
Education and Expertise
Michail Chatzizacharias holds a Master's degree in Computer Engineering from EPITA: Ecole d'Ingénieurs en Informatique, which he completed from 2021 to 2023. He also earned a Bachelor's degree in Computer Engineering from the Technological Educational Institute of Crete between 2010 and 2015. Additionally, he has a Certificate of Proficiency in English from the University of Michigan and a Ruby on Rails Certification from Coding School. His educational background supports his expertise in deep learning and software engineering.
Research Focus and Contributions
In his current role, Michail Chatzizacharias specializes in implementing and refining semi-supervised learning techniques for medical image segmentation. His research aims to enhance the accuracy and efficiency of deep learning models applied to medical imaging. He contributes to collaborative projects that leverage advanced technologies to address medical challenges, particularly in the field of endometriosis.