Ambiguity quantifies the number of EDGE CASES, alternative annotations, or the amount of uncertainty in annotations
We have a wealth of experience in the worlds of artificial intelligence, machine learning and annotation. Our goal is to raise the standard in quality assured data sets, with unique annotation strategies and a global userbase of contributors that can scale throughput to unprecedented levels.
Daniel received his habilitation at Heidelberg University in the field of machine learning and data science. His startup, Pallas Ludens, enabled automotive and medical imaging companies to collect large machine learning training datasets. After about three years, in 2016, Daniel and his team joined Apple, where he worked for three years on dataset design, annotation and data quality for computer vision tasks.
In 2019, Daniel co-founded Quality Match, which he is leading as managing director. He has also co-founded a social networking start-up, and works as business angel with a number of investments in gaming and machine learning companies.
Mirko received his Ph.D. in physics from Heidelberg University in 2012. His research in the field of image processing for 3D cameras was carried out in close collaboration with Sony. He then relocated to Silicon Valley with Microsoft; in their research department, he focused on technology which launched in Kinect for XBox One and HoloLens.
In 2016 he joined the AR/VR team at Google, as the technical lead of a team developing benchmarking and reference data solutions for novel imaging systems and algorithms. His team created reference data pipelines including data collection hardware, data processing, and (cloud based) continuous metric evaluation/testing.
Mirko is now a co-founder of Quality Match after joining as chief technology officer in June 2020.
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