Daniel Becking

Daniel Becking

Daniel Becking is a research associate and Ph.D. student in the Efficient Deep Learning Group at Fraunhofer HHI and TU Berlin, supervised by Wojciech Samek. He received a B.Eng. in microsystems technology and an M.Sc. in biomedical engineering from HTW Berlin and TU Berlin in 2016 and 2020, respectively. His research focuses on neural network compression and the efficient transmission of incremental neural data in distributed learning, leveraging explainable AI techniques and concepts from information theory. Since 2020, he has actively contributed compression tools and high-level syntax to the ISO/IEC 15938-17 Neural Network Coding (NNC) standard. His current interests include neural codecs and language model-based general-purpose compressors.

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  • Organization
    Fraunhofer HHI and TU Berlin
  • Profession
    Research associate and Ph.D. student
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9 July 2025
09:00 - 17:15
EST - New York
CST - Beijing
PST - Los Angeles
AWST - Perth, Australia
This workshop marks the 3rd edition of the MLComm series,...