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Pattern Recognition in Embedded Systems Campus Nord Otto-Hahn-Str. 16
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Babak Hosseini received his bachelor's degree in control engineering (Steuerungstechnik) from the K. N. Toosi University (Iran) in 2006 and his master's degree in control engineering (Steuerungstechnik) from the University of Tehran (Iran) in 2009. The topic of his master's project was "Concept Learning and Transfer among Heterogeneous Agents". After graduating, he was employed in industrial sectors (Iran) as a robotics and control systems engineer.
In June 2014, he joined the Machine Learning group of the cognitive interaction technology center (CITEC) at Bielefeld University (Germany) as a Ph.D. student funded by a DFG scholarship. The subject of his Ph.D. project was the semantic analysis of motion data, an interpretable focus on using advanced machine learning methods to analyze multi-dimensional time-series and human movement data specifically.
After finishing his Ph.D. project (expected disputation in March-April 2021), he joined the IT-service company SYNAXON AG as a data scientist for four months. During that short period, he worked on several data mining ideas to automate the customer-service and sale-management platforms. He developed a successful prototype for prioritizing the alarm in the client-monitoring system.
In October 2019, Babak joined the Pattern Recognition in Embedded Systems Group in the Department of Computer Science at the University of Dortmund as a researcher. He is assigned to the Hinkelstein project (funded by BMBF) focused on designing an advanced time-series classification algorithm that is efficient for implementation on a specialized embedded device. This project is a part of the Energy-efficient AI system Innovation competition, which belongs to the large scale framework program "Microelectronics from Germany - innovation driver in the Digitalization" from the Gerrman ministry of research and innovation.
Babak Hosseini's research interests lie in the development and application of machine learning and data mining methods for time-series analysis, deep learning, interpretable machine learning, and kernel-based methods.
Publications (Google Scholar)
