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 data management, machine learning, and computer systems. I develop principled abstractions and systems for efficient and reliable AI, spanning data-quality and feasibility analysis, model selection and evaluation, and interaction-aware optimizations.

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 design declarative abstractions and system mechanisms that reduce the data, computational, and operational costs of AI workloads. This work spans feasibility analysis, model search and reuse, continuous integration, scalable training and inference, and state management.
I investigate how traces of interactions among users, models, and tools can be transformed into reliable signals about workload characteristics, successful strategies, failure modes, and reusable knowledge. These signals can inform model selection, context augmentation, clarification, early termination, and systems-level resource management.
I develop methods for characterizing data-quality limitations, disentangling sources of evaluation noise, and measuring AI-system behavior under realistic conditions. This work distinguishes inherent task and model limitations from artifacts introduced by data, evaluation metrics, or execution environments.

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
  6. SparCML: High-performance sparse communication for machine learning
    Cedric Renggli, Saleh Ashkboos, Mehdi Aghagolzadeh, and 2 more authors
    In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2019
    arXiv PDF