M.Sc. David Neumann

PhD Candidate and Research Associate in Machine Learning
Working at Fraunhofer HHI, Berlin, Germany
in the Artificial Intelligence Department

I'm a research scientist at Fraunhofer HHI, where I focus on developing efficient machine learning solutions. My research interests involve federated learning, recommender systems, neural network compression, and explainable AI, with a particular focus on developing novel techniques and algorithms. I have a strong foundation in computer science, having completed my M.Sc. degree from Technische Universität Berlin in 2019. Prior to joining the research community, I worked as a software engineer on large-scale projects for various industries.

In addition to machine learning, I'm interested in formal languages, automata, and compiler construction – topics that underpin many modern software systems. As someone with entrepreneurial experience, I value innovation and collaboration in driving technical solutions. I'm currently pursuing my PhD while working on developing federated learning methods highly personalized and privacy-focused recommender systems.

Recent Publications

A Privacy Preserving System for Movie Recommendations Using Federated Learning

November 24, 2023

David Neumann, Andreas Lutz, Karsten Müller, Wojciech Samek

Just Accepted for Publication in the ACM Transactions on Recommender Systems (TORS) Special Issue on Trustworthy Recommender Systems.

@article{bibliography:a-privacy-preserving-system-for-movie-recommendations-using-federated-learning,
    author       = {Neumann, David and Lutz, Andreas and Müller, Karsten and Samek, Wojciech},
    title        = {A Privacy Preserving System for Movie Recommendations Using Federated Learning},
    doi          = {10.1145/3634686},
    journaltitle = {ACM Transactions on Recommender Systems},
    shortjournal = {TORS},
    issn         = {2770-6699},
    issuetitle   = {Special Issue on Trustworthy Recommender Systems},
    date         = {2023-11-24},
    pagetotal    = {51},
    publisher    = {Association for Computing Machinery ({ACM})},
    address      = {New York City, New York, United States of America}
}

Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy

February 28, 2023

Christopher J. Anders, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin

@article{bibliography:software-for-dataset-wide-xai,
    author       = {Anders, Christopher J. and Neumann, David and Samek, Wojciech and Müller, {Klaus-Robert} and Lapuschkin, Sebastian},
    title        = {Software for Dataset-wide {XAI}},
    subtitle     = {From Local Explanations to Global Insights with Zennit, {CoRelAy}, and {ViRelAy}},
    doi          = {10.48550/arXiv.2106.13200},
    journaltitle = {Computer Research Repository},
    shortjournal = {CoRR},
    issn         = {2331-8422},
    date         = {2023-02-28},
    pagetotal    = {20},
    eprinttype   = {arXiv},
    eprint       = {2106.13200v2},
    eprintclass  = {cs.LG}
}

Finding and Removing Clever Hans: Using Explanation Methods to Debug and Improve Deep Models

January 2022

Christopher J. Anders, Leander Weber, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin

Published in Elsevier Information Fusion Volume 77.

@article{bibliography:finding-and-removing-clever-hans,
    author       = {Anders, Christopher J. and Weber, Leander and Neumann, David and Samek, Wojciech and Müller, {Klaus-Robert} and Lapuschkin, Sebastian},
    title        = {Finding and removing Clever Hans},
    subtitle     = {Using explanation methods to debug and improve deep models},
    doi          = {10.1016/j.inffus.2021.07.015},
    journaltitle = {Information Fusion},
    issn         = {1566-2535},
    volume       = {77},
    date         = {2022-01-01},
    pages        = {261--295},
    pagetotal    = {35},
    publisher    = {Elsevier Science Publishers B. V.},
    address      = {PO Box 211 1000 AE Amsterdam, Netherlands}
}