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. My intro slides summarizing the fast-moving space are available here.

Previously, I was a PostDoc at UZH (DaST with Dan Olteanu) and defended my thesis at ETH (System Group with 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