Kostas Kouvaris, Ph.D.
About Kostas Kouvaris, Ph.D.
Kostas Kouvaris, Ph.D., serves as the Lead Machine Learning Engineer at Corpay One, where he has worked since 2021. He has a strong background in machine learning and research, having held positions at various institutions and developed advanced models for information extraction and automated recommendation systems.
Current Role at Corpay One
Kostas Kouvaris serves as the Lead Machine Learning Engineer at Corpay One since 2021. In this role, he is responsible for overseeing the setup and management of data annotation efforts. This includes developing domain-specific ontologies and creating comprehensive guides for the data entry team. His contributions focus on enhancing the efficiency and accuracy of machine learning models through effective data management.
Previous Experience in Machine Learning and Research
Before joining Corpay One, Kouvaris held several positions in research and consultancy. He worked as a Senior Researcher at the Center for Technology, Research & Innovation (CETRI) in 2018, where he developed multi-modal neural network models for information extraction from expense documents. He also served as a Data Consultant at Synthex and as a Research Engineer at the IT Innovation Centre. His experience includes pioneering the integration of Large Language Models into service offerings and designing automated recommendation systems.
Educational Background and Qualifications
Kostas Kouvaris has a strong academic background in mathematics and artificial intelligence. He earned his Bachelor of Science in Mathematics from the National & Kapodistrian University of Athens in 2011. He then completed a Master of Science in Artificial Intelligence at the University of Southampton in 2012. Currently, he is pursuing a Doctor of Philosophy (PhD) in Complex System Simulations at the University of Southampton, which he began in 2018.
Technical Skills and Innovations
Kouvaris has developed a range of technical skills throughout his career. He has implemented automated machine learning pipelines on AWS, covering processes from training data preparation to model training and testing. His work includes designing systems that leverage past user activity to create automated recommendations, thereby improving user experience and operational efficiency.