Research Interests

Anything graphs, such as graph algorithms, graph theory, graph mining, graph learning, and network analysis; recently with an emphasis on hypergraphs and higher-order relations.
Anything data, including collection, cleaning, modeling, analysis, communication, and the question of what data is.
Interdisciplinary and transdisciplinary research on complex systems.
With my lawyer hat on, I do legal data science projects, too.


  • Mapping the Multiverse of Latent Representations (with Bastian Rieck and Jeremy Wayland).
    We introduce Presto, a topological framework to explore and exploit representational variability in latent-space machine-learning models.

  • Beyond Flatland: Exploring Graphs in Many Dimensions
    My computer-science dissertation which, based on my KDD 2021, AAAI 2022, DSH 2023, ICLR 2023, and KDD 2023 publications, explores graphs in five dimensions: descriptivity, multiplicity, complexity, expressivity, and responsibility.

  • Legal Hypergraphs (with Dirk Hartung and Daniel Martin Katz).
    Philosophical Transactions of the Royal Society A, to appear.
    Preprint | Replication Material (coming soon)
    Also presented as an Extended Abstract at the Conference on Complex Systems (CCS 2023).
    We introduce temporal hypergraphs as representations of legal network data, demonstrating their utility in case studies on legal citation networks and legal collaboration networks.

  • Evaluating the "Learning on Graphs" Conference Experience (with Bastian Rieck).
    Preprint | Replication Material
    We present the results of a survey distributed to participants of the first "Learning on Graphs" conference.

  • Reducing Exposure to Harmful Content via Graph Rewiring (with Aristides Gionis and Stefan Neumann).
    Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023), 323–334.
    (A*, 22.1% acceptance rate)
    PDF | Replication Material (coming soon)
    We introduce Gamine, a fast greedy algorithm for reducing the exposure to harm in recommendation graphs via edge rewiring, based on the theory of absorbing random walks.

  • Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework (with Sebastian Dalleiger and Bastian Rieck).
    International Conference on Learning Representations (ICLR 2023).
    (A*, 31.8% acceptance rate)
    PDF | Replication Material
    We develop Orchid, a flexible framework generalizing Ollivier-Ricci curvature to hypergraphs, prove that the resulting curvatures have favorable theoretical properties, and demonstrate that they are both scalable and useful to perform a variety of hypergraph tasks in practice.

  • All the World's a (Hyper)Graph: A Data Drama (with Bastian Rieck and Jilles Vreeken).
    Digital Scholarship in the Humanities (DSH 2023), fqad071.
    PDF (Extended Version) | Dataset
    Also presented as a long oral presentation at the Cultural Data Analytics Conference (CUDAN 2023).
    Raw data stem from all of Shakespeare's plays / We model them as graphs in many ways / And demonstrate representations matter.

  • Rechtsstrukturvergleichung (with Dirk Hartung).
    RabelsZ–The Rabel Journal of Comparative and International Private Law 86.4 (2022), 935–975.
    Theoretically grounded in systems theory and complexity science, we propose structural comparative law as a data-driven approach to explore the similarities and differences between the structures of legal systems.

  • Sharing and Caring: Creating a Culture of Constructive Criticism in Computational Legal Studies (with Dirk Hartung).
    MIT Computational Law Report 2023.
    Building on the scientific literature regarding reproducible research and peer review, we introduce seven foundational principles for creating a culture of constructive criticism in the transdisciplinary field of computational legal studies.

  • Differentially Describing Groups of Graphs (with Sebastian Dalleiger and Jilles Vreeken).
    Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2022), 3959–3967.
    (A*, 15% acceptance rate overall; with oral presentation; oral presentation acceptance rate ~5.5%)
    PDF | Replication Material
    Given a set of graphs and a partition of these graphs into groups, we introduce Gragra (Graph group analysis) to discover what graphs in one group have in common, how they systematically differ from graphs in other groups, and how multiple groups of graphs are related.

  • Law Smells: Defining and Detecting Problematic Patterns in Legal Drafting (with Janis Beckedorf, Maximilian Böther, Dirk Hartung, and Daniel Martin Katz).
    Artificial Intelligence and Law 31 (2023), 335–368.
    PDF | Replication Material
    Also presented as an Extended Abstract at ProLaLa@POPL 2022.
    Building on the computer science concept of code smells, we initiate the systematic study of law smells (i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law), introduce a comprehensive law smell detection toolkit, and demonstrate its utility on twenty-two years of legislation from the United States Code.

  • Simplify Your Law: Using Information Theory to Deduplicate Legal Documents (with Jyotsna Singh and Holger Spamann).
    Proceedings of the IEEE International Conference on Data Mining Workshops (ICDMW 2021), 631–638.
    Free PDF | Paywalled PDF | Replication Material
    We introduce the duplicated phrase detection problem for legal texts and propose the Dupex (Duplicated phrase extractor) algorithm to solve it, leveraging the Minimum Description Length principle to identify a set of duplicated phrases that together best compress the input text.

  • Graph Similarity Description: How Are These Graphs Similar? (with Jilles Vreeken).
    Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021), 185–195.
    (A*, 15.4% acceptance rate)
    Free PDF | Paywalled PDF | Replication Material | Code
    We treat graph similarity assessment as a description problem, rather than as a measurement problem. Having formalized this problem as a model selection task using the Minimum Description Length principle, we propose Momo (Model of models), which solves the problem by breaking it into two parts and introducing efficient algorithms for each.

  • Measuring Law Over Time: A Network Analytical Framework with an Application to Statutes and Regulations in the United States and Germany (with Janis Beckedorf, Michael Bommarito, Dirk Hartung, and Daniel Martin Katz).
    Frontiers in Physics 9 (2021), 269:1–269:31.
    PDF | Appendix
    Also presented as an Extended Abstract at Complex Networks 2021.
    We present a comprehensive framework for analyzing legal documents as multi-dimensional, dynamic document networks and demonstrate its utility by applying it to an original dataset of statutes and regulations from two different countries, the United States and Germany, spanning more than twenty years (1998–2019).

  • A Breezing Proof of the KMW Bound (with Christoph Lenzen).
    Proceedings of the Fourth SIAM Symposium on Simplicity in Algorithms (SOSA 2021), 184–195.
    (29.4% acceptance rate)
    Free PDF (Extended Version) | Paywalled PDF (Conference Version)
    We give a simple and (in the extended version) fully self-contained proof of the KMW lower bound, proving a hardness result for several fundamental graph problems in the LOCAL model of distributed computing.

  • Complex Societies and the Growth of the Law (with Janis Beckedorf, Dirk Hartung, and Daniel Martin Katz).
    Sci. Rep. 10 (2020), 18737:1–18737:14.
    PDF | Appendix
    Also presented as a non-archival paper at NLLP@KDD 2020.
    We examine 25 years of statutory legislation in the United States and Germany through the lens of network science, finding that the main driver behind the growth of the law in both jurisdictions is the expansion of the welfare state, backed by an expansion of the tax state.

  • Das Wertpapierhandelsgesetz (1994–2019): Eine quantitative juristische Studie (with Andreas Martin Fleckner).
    Festschrift 25 Jahre WpHG (2019), 53–85.
    PDF | Appendix
    A legal data science project investigating the evolution of Germany's Securities Trading Act over the first 25 years of its lifetime.

  • Juristische Netzwerkforschung: Modellierung, Quantifizierung und Visualisierung relationaler Daten im Recht.
    XVIII, 376 p. Tübingen: Mohr Siebeck (2019).
    PDF | Appendix
    Awards: Otto Hahn Medal of the Max Planck Society (2020), Bucerius Law School Dissertation Award (2018)
    Review: Archiv für die civilistische Praxis (AcP), 221 (2021), 923–928 (Moritz Renner)
    My legal dissertation. I introduce network science to the German legal discourse and explore what legal network science could mean. This is how I got into graphs.

  • Quantitative Rechtswissenschaft: Sammlung, Analyse und Kommunikation juristischer Daten (with Andreas Martin Fleckner).
    Juristenzeitung 73 (2018), 379–389.
    PDF | Appendix
    We explain what legal data analysis is and discuss how German legal research could profit from it.


  • 2023 (Winter Semester)
    Lecturer, Ideas of Informatics (Saarland University, with Kurt Mehlhorn)
  • 2022 (Winter Semester)
    Lecturer, Ideas of Informatics (Saarland University, with Kurt Mehlhorn)
  • 2021 (Winter Semester)
    Lecturer, Ideas of Informatics (Saarland University, with Kurt Mehlhorn)
  • 2020 (Winter Semester)
    Lecturer, Ideas of Informatics (Saarland University, with Kurt Mehlhorn)
    Teaching Assistant, Theory of Distributed Systems (Saarland University)
  • 2019–2022 (Various Trimesters)
    Lecturer, Introduction to Informatics (Bucerius Law School)
  • 2018–2022 (Summer Semesters)
    Lecturer, Programming for Lawyers (University of Heidelberg, with Janis Beckedorf and Philipp Sahrmann)
  • 2018 (Winter Semester)
    Tutor, Data Networks (Saarland University)
  • 2017 (Winter Semester)
    Tutor, Introduction to Informatics (LMU Munich)
  • 2017–2019 (Various Trimesters)
    Lecturer, Introduction to Programming (Bucerius Law School, with Philipp Sahrmann)

Awards and Honors

  • Best Reviewer Award, Learning on Graphs Conference (2022)
  • Outstanding Reviewer, NeurIPS D&B (2022)
  • Caroline von Humboldt Prize (2022)
  • Otto Hahn Medal of the Max Planck Society (2020)
  • Bucerius Law School Dissertation Award (2018)

Academic Service

  • Program Committees
    • KDD: 2023
    • LoG: 2022, 2023
    • MLG@ECMLPKDD: 2023
    • NeurIPS D&B: 2022, 2023
    • NLLP@EMNLP: 2022
  • Reviewing
    • Applied Network Science: 2023
    • Distributed Computing: 2023
    • ICDM: 2022
    • JCSS: 2022
    • KDD: 2021, 2022
    • NeurIPS Ethics: 2023
    • PODC: 2022
    • TKDE: 2022


  • 2023
    Ph.D. in Computer Science (Dr. rer. nat., Saarland University – Thesis Written at the MPI for Informatics)
  • 05/2023–08/2023
    Visiting Researcher at Aalto University Helsinki (Hosted by Mikko Kivelä)
  • 06/2022–09/2022
    Visiting Researcher at KTH Stockholm (Hosted by Aristides Gionis, Supported by SoBigData++)
  • 2020–2023
    Ph.D. Student in Computer Science (MPI for Informatics)
  • 2020
    M.Sc. in Computer Science (Saarland University)
  • 2018
    B.Sc. in Computer Science (LMU Munich)
  • 2018
    Ph.D. in Law (Dr. iur., Bucerius Law School – Thesis Written at the MPI for Tax Law and Public Finance)
  • 2015
    First Legal State Exam (HansOLG)