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Babak Hosseini, M.Sc.

Babak Hosseini, M.Sc. Photo of Babak Hosseini, M.Sc. babak.hosseini(at)cs.tu-dortmund.de

(+49)231 755-4616

(+49)231 755-4655



Pattern Recognition in Embedded Systems
Campus Nord
Otto-Hahn-Str. 16

Room 104


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 in charge of the Hinkelstein project, which is about data-driven recognition of a specific heat disease problem, Atrial Fibrillation, defined by a biomedical company. From the technical perspective, the project is focused on designing an advanced time-series classification algorithm that is computationally efficient for implementation on a specialized embedded device. 

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)