Cedric Renggli

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I am a Senior ML Researcher at Apple. My main research interest lies at the intersection of data management and AI/ML systems, with a particular focus on how to efficiently, reliably, and responsibly use and manage data throughout the development, evaluation, and maintenance process of AI/ML-based solutions.

Previously, I was a PostDoc in the Data Systems and Theory group lead by Dan Olteanu at UZH. I defended my thesis on Building Data-Centric Systems for Machine Learning Development and Operations at ETH Zurich’s System Group under the supervision of Ce Zhang. I hold a bachelor’s degree from the Bern University of Applied Sciences and received my MSc in Computer Science from ETH Zurich. My work on Efficient Sparse AllReduce For Scalable Machine Learning was awarded with the silver medal of ETH Zurich for outstanding master thesis. During my PhD, I have worked as a research intern and student research consultant at Google Brain (now Google DeepMind).

News

May 15, 2025 New website is online.

Selected Publications

  1. Fundamental Challenges in Evaluating Text2SQL Solutions and Detecting Their Limitations
    Cedric Renggli, Ihab F Ilyas, and Theodoros Rekatsinas
    arXiv preprint arXiv:2501.18197, 2025
  2. Automatic feasibility study via data quality analysis for ml: A case-study on label noise
    Cedric Renggli, Luka Rimanic, Luka Kolar, and 2 more authors
    2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023
  3. A Data Quality-Driven View of MLOps
    Cedric Renggli, Luka Rimanic, Nezihe Merve Gürel, and 3 more authors
    IEEE Data Engineering Bulletin, 2021
  4. Evaluating Bayes Error Estimators on Read-World Datasets with FeeBee
    Cedric Renggli, Luka Rimanic, Nora Hollenstein, and 1 more author
    In Advances in Neural Information Processing Systems (Datasets and Benchmarks), 2021
  5. On Convergence of Nearest Neighbor Classifiers over Feature Transformations
    Luka Rimanic, Cedric Renggli, Bo Li, and 1 more author
    In Advances in Neural Information Processing Systems, 2020
  6. Continuous Integration of Machine Learning Models with ease.ML/CI: Towards a Rigorous Yet Practical Treatment
    Cedric Renggli, Bojan Karlas, Bolin Ding, and 4 more authors
    In SysML Conference, 2019