Maximilian Böther


Hey, nice to meet you! 👋

I am pursuing a Ph.D. in Computer Science at the ETH Zurich Systems Group and the Efficient Architectures and Systems Lab (EASL), supervised by Ana Klimovic and Gustavo Alonso. My interests span data-centric machine learning systems and efficient data processing.

Currently, I am working on Modyn, a platform for training machine learning models on dynamic datasets, i.e., datasets that change over time. We believe there is a gap between research and practice, as research often focusses on static benchmarking datasets (speak: ImageNet, CIFAR, …), while in practice, we have to deal with many challenges from dynamic datasets, such as distribution shift.

I obtained B.Sc. and M.Sc. degrees in IT-Systems Engineering from Hasso Plattner Institute, Potsdam, Germany in 2020 and 2022. I have published several papers at renowned venues, e.g., VLDB, ICLR, and DaMoN, have received a Best Paper Award at GECCO’21, and have several years of experience administrating Linux servers. Furthermore, I have interned at Google on the CoreML team, working on distributed data subset selection. As a side interest, I enjoy interdisciplinary work on the intersection of law and computer science in cooperation with the Center for Legal Technology and Data Science of Bucerius Law School. Please find my CV here.


Jun 16, 2023 Our paper on analyzing vectorized hash tables across CPU architectures just got accepted at VLDB’23 in Vancouver!
Jun 4, 2023 I joined Google for a summer research internship in Sunnyvale, California, USA! I am working on scaling out submodular data subset selection.
Apr 10, 2023 Our work-in-progress workshop paper. on Modyn, our research platform for model training on dynamic datasets, has been accepted at EuroMLSys’23 in Rome!
Oct 31, 2022 I joined the ETH Zurich Systems Group and the Efficient Architectures and Systems Lab (EASL) to do a Ph.D. in Machine Learning Systems, supervised by Professor Ana Klimovic. Looking forward to the new adventures in Switzerland!
Oct 14, 2022 Our paper on efficiently computing directed minimum spanning trees (arboresence) has been accepted for publication at ALENEX 2023. Check out the final version here.
Jun 5, 2022 Our Law Smells paper, which applies concepts of software engineering to the law, has been published in AI&Law. Check out the final version here.
Jan 23, 2022 Our paper on deep learning for combinatorial optimization just got accepted at ICLR! Check out the final version here.
Dec 26, 2021 This website just went online!

selected publications

  1. VLDB
    Analyzing Vectorized Hash Maps Across CPU Architectures
    Böther, Maximilian, Benson, Lawrence,  Klimovic, Ana and 1 more author
    Proceedings of the VLDB Endowment 2023
  2. EuroMLSys
    Towards A Platform and Benchmark Suite for Model Training on Dynamic Datasets
    Böther, Maximilian, Strati, Foteini,  Gsteiger, Viktor and 1 more author
    In Proceedings of the Workshop on Machine Learning and Systems (EuroMLSys) 2023
  3. ICLR
    What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization
    Böther, Maximilian, Kißig, Otto,  Taraz, Martin and 3 more authors
    In Proceedings of the International Conference on Learning Representations (ICLR) 2022
    Drop It In Like It’s Hot: An Analysis of Persistent Memory as a Drop-in Replacement for NVMe SSDs
    Böther, Maximilian, Kißig, Otto,  Benson, Lawrence and 1 more author
    In Proceedings of the International Workshop on Data Management on New Hardware (DaMoN@SIGMOD) 2021