Cedric Renggli

cedric_renggli.jpg

I am a Senior Researcher and Lecturer at ETH’s Systems Group working with Ana Klimovic.

My research lies at the intersection of AI systems and data management. I design efficient and reliable systems for emerging AI workloads, focusing on how the data they consume and generate is managed and used. My work spans data systems for AI, scalable serving architectures, and methods for separating data and evaluation effects from model quality.

Previously, I was a Senior Researcher at Apple, a PostDoc at UZH (DaST with Dan Olteanu) and defended my thesis at ETH’s Systems Group with Ce Zhang.

If you are interested in collaborating, please reach out directly. I also welcome motivated students at ETH for projects and theses. To apply, send me an email with your CV and transcript of records attached.

Research Focus Areas

I develop systems for efficiently organizing, maintaining, and retrieving data for AI applications, including scalable vector search. I also study how traces of interactions among users, models, and tools can be represented and learned from.
I build scalable and resource-efficient architectures for serving emerging AI workloads. This work includes general mechanisms for execution and state management, as well as adaptive optimizations informed by workload and interaction signals.
I study how data quality and evaluation choices influence observed AI-system performance during both evaluation and operation. My goal is to disentangle these effects from model quality and identify the underlying limitations of the complete AI system.

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. SHiFT: an efficient, flexible search engine for transfer learning
    Cedric Renggli, Xiaozhe Yao, Luka Kolar, and 3 more authors
    Proceedings of the VLDB Endowment, 2022
    arXiv PDF
  3. 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
  4. 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
  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