Bálint Fodor

Bálint Fodor has a diverse work experience in the field of software engineering and research. Bálint started their career in 2009 as a Software Engineer at Simptech kft. From 2010 to 2011, they worked as a Research Associate at MTA SZTAKI. Bálint then became self-employed as a Software Engineer from November 2011 to January 2012.

In 2012, Bálint joined Devsigner as a Software Engineer, and later that year, they also worked as a Software Engineer at API - Automotive Process Institute GmbH from February 2012 to December 2013. From December 2013 to September 2014, they worked at BME-Infokom Innovátor Nonprofit Kft. as a Software Engineer.

From September 2014 to February 2017, Bálint worked at CourtPro as a Software Engineer. Bálint then joined MTA KOKI, Microscopy Center as a Software Engineer from 2017 to 2018.

Bálint's most recent work experience was at Formlabs, where they worked as a Software Engineer from October 2018 to July 2023. Currently, they are a Research Engineer at Colossyan since July 2023.

Bálint Fodor has a diverse education history. In 2017, they completed the Deep Learning Nanodegree Foundation program at Udacity, focusing on deep learning. Prior to that, from 2011 to 2014, they pursued a Doctor of Philosophy (PhD) degree in Computer Software Engineering, with a specialization in computer vision and image processing, at the Budapest University of Technology and Economics. Before their PhD, Bálint earned a Master of Science (MSc) degree in Computer Software Engineering from the same university, completing their studies from 2009 to 2011. Their undergraduate education took place from 2005 to 2009, where they obtained a Bachelor of Science (BSc) degree in Computer Software Engineering from the Budapest University of Technology and Economics.

Bálint also holds a graduation certificate from Tóparti Gimnázium és Művészeti Szakközépiskola, where they pursued a class specializing in the English language from 2001 to 2005.

In terms of additional certifications, Bálint earned a certificate in Machine Learning from Coursera in January 2017.