Cedric Renggli

cedric_renggli.jpg

I am a Senior Researcher at Apple. I work on efficient AI and data systems, focusing on how large-scale AI workloads, such as retrieval-augmented generation and agent-based inference, can be served reliably and efficiently under real-world constraints. My research emphasizes principled abstractions, declarative system design, and the identification of fundamental trade-offs that shape scalable, trustworthy AI infrastructure.

I am also an external lecturer at ETH co-teaching a seminar on Systems for AI with Ana Klimovic.

Previously, I was a PostDoc in the Data Systems and Theory group lead by Dan Olteanu at UZH and defended my thesis at ETH Zurich’s System Group under the supervision of Ce Zhang. During my PhD, I have worked as a research intern and student research consultant at Google Brain (now Google DeepMind).

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
    arXiv
  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
    arXiv PDF
  3. A Data Quality-Driven View of MLOps
    Cedric Renggli, Luka Rimanic, Nezihe Merve Gürel, and 3 more authors
    IEEE Data Engineering Bulletin, 2021
    arXiv PDF
  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
    arXiv PDF
  5. 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
    arXiv PDF