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Technical and engineering sciences

Benjamin Maschler

Mail

benjamin.maschler@ias.uni-stuttgart.de

Age

30

Occupation

PhD student


Institution

Universität Stuttgart

Stuttgart

Baden Wurttemberg

Biography

Benjamin Maschler studied Renewable Energies and Sustainable Electrical Power Supply at the Universities of Stuttgart and Cape Town. Since 2017, he has been a research assistant at the Institute of Industrial Automation and Software Systems at the University of Stuttgart. His research focusses on solving applied problems of distributed or dynamic machine learning to make this learning more suitable for everyday use, more robust and less prone to abuse. For this purpose, he uses methods of continual and transfer learning. His research was well received on national and international conferences and is currently leading to several high-profile journal publications. In addition to his research work, he is committed to an informed, social debate about technology in our everyday lives (e.g. https://youtu.be/x-6_X_xoJR0).

Questions with regard to Artificial Intelligence

Benjamin Maschler is researching how to use deep transfer learning to make machine learning more suitable for everyday use, more robust, and less prone to abuse. Conventional machine learning requires the compilation of large training data sets, from which algorithms then extract correlations. This favors large corporations with existing market access, takes away much of the control users have over their data and how it is used, and is energy inefficient. Moreover, such algorithms have so far shown little flexibility in responding to dynamic changes. Transfer learning, on the other hand, enables learning on distributed datasets directly at the user's end and, at the same time, permits much smaller-scale adaptation to local, even dynamic, conditions. Here, Benjamin developed first description approaches and is currently dedicated to the creation of a practical, open framework - primarily in industrial automation, but transferable to other areas where a central merging of data is (or should be) undesirable.