Andreas Funke
About Andreas Funke
Andreas Funke is a Research Scientist currently working at DeepL in Köln, Nordrhein-Westfalen, Deutschland. He has a diverse academic background in Informatik and Magister Artium, along with experience in various research and data science roles.
Current Role at DeepL
Andreas Funke has been employed as a Research Scientist at DeepL since 2020. In this role, he focuses on advancing research in natural language processing and machine learning. His work contributes to the development of innovative AI solutions that enhance language translation and understanding. Funke's expertise in the field supports DeepL's mission to improve communication across languages.
Previous Experience in Academia
Prior to his current position, Funke held multiple roles at Heinrich-Heine-Universität Düsseldorf. He served as a Research Assistant from 2016 to 2017 and again in 2018 for a total of 1 year and 8 months. Additionally, he worked as a Student Assistant in 2015 for 3 months. His academic experience provided a foundation in research methodologies and data analysis.
Educational Background
Andreas Funke studied at Heinrich-Heine-Universität Düsseldorf, where he earned a Bachelor of Science in Informatik from 2014 to 2017. He continued his education at the same institution, achieving a Master of Science in Informatik from 2017 to 2019. In addition, he obtained a Magister Artium in Geschichte, Politik, and Medienwissenschaft from 1998 to 2012. This diverse academic background supports his interdisciplinary approach to research.
Experience in Machine Learning and Data Science
Funke has experience in the field of machine learning and data science. He worked as a Junior Data Scientist at StepStone from 2017 to 2018 for 5 months. He later served as a Machine Learning Engineer at ella media gmbh from 2019 to 2020. His roles involved applying data-driven techniques to solve complex problems and enhance AI applications.
Research Contributions
Andreas Funke has contributed to various research initiatives, focusing on topics such as topic modeling, labeling, and semantic coherence metrics. He co-authored papers on argument mining using deep learning and distributed data structures in self-organizing networks. His research efforts demonstrate a commitment to advancing knowledge in the fields of artificial intelligence and data science.