Aleksandar Bojchevski

I am a full professor for Computer Science at the University of Cologne where I lead the research group on Trustworthy Artificial Intelligence (website). Broadly speaking our research is about models and algorithms that are not only accurate or efficient, but also robust, uncertainty-aware, privacy-preserving, fair, and interpretable. One focus area of our research is trustworthy graph-based models such as graph neural networks. Previously I was faculty at the CISPA Helmholtz Center for Information Security. Before that I did a PostDoc and completed my PhD on machine learning for graphs at the Technical University of Munich, advised by Stephan Günnemann.

We have multiple open positions in our research group on a variety of (trustworthy) machine learning topics.

news

Oct '24 Together with colleagues from RWTH Aachen we are co-organising a Learning on Graphs Meet Up.
Oct '24 Our paper “SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors” was accepted at NeurIPS 2024.
Jun '24 Together with Christian Sohler (UoC), Michael Shaub (RWTH Aachen), and Christopher Morris (RWTH Aachen) we organised the first seminar in a series on “Next Generation Graph Neural Networks” in Cologne.
May '24 Our paper on robust conformal prediction was accepted at ICML 2024.
Feb '24 Two papers were accepted at ICLR 2024, one on label poisoning and one on conformal GNNs.
Dec '23 I attended the Dagstuhl seminar on Scalabale Graph Mining and Learning.
Sep '23 Two papers, one on certificates and one on GATs, were accepted at NeurIPS 2023.
Sep '23 I joined the Center for Data and Simulation Science as a core scientist.
Apr '23 One paper on conformal predictions sets for GNNs was accepted at ICML 2023.
Apr '23 Our group joined the key profile area “Intelligent Methods for Earth System Sciences”.

selected publications [full list]

  1. ICML
    Conformal Prediction Sets for Graph Neural Networks
    Soroush H. Zargarbashi, Simone Antonelli, and Aleksandar Bojchevski
    In International Conference on Machine Learning, ICML 2023
  2. NeurIPS
    Are Defenses for Graph Neural Networks Robust?
    Felix Mujkanovic, Simon Geisler, Stephan Günnemann, and Aleksandar Bojchevski
    In Neural Information Processing Systems, NeurIPS 2022
  3. ICLR
    Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks
    Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger, and Stephan Günnemann
    In International Conference on Learning Representations, ICLR 2021
  4. ICML
    Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
    Aleksandar Bojchevski, Johannes Gasteiger, and Stephan Günnemann
    In International Conference on Machine Learning, ICML 2020
  5. NeurIPS
    Certifiable Robustness to Graph Perturbations
    Aleksandar Bojchevski, and Stephan Günnemann
    In Neural Information Processing Systems, NeurIPS 2019