Scholar
Ce Zhang
Google Scholar ID: GkXqbmMAAAAJ
Together AI; University of Chicago
Citations & Impact
Citations
19,092
H-index
63
i10-index
187
Publications
20
Co-authors
106
list available
Publications
Resume (English only)
Academic Achievements
- Authored or co-authored high-impact publications including:
- — 'Advances, challenges and opportunities in creating data for trustworthy AI' (Nature Machine Intelligence);
- — 'DataPerf: Benchmarks for Data-Centric AI Development' (MLCommons);
- — 'A Data Quality-Driven View of MLOps' (IEEE Data Engineering Bulletin);
- — 'MLSys: The New Frontier of Machine Learning Systems' (positioning paper for the inaugural MLSys conference).
- Mentored students who received numerous awards:
- — ICLR Outstanding Paper (Shuai Zhang);
- — SIGMOD Best Demo Runner-Up (ArgusEyes);
- — Google Focused Research Award (2018);
- — IBM Q Best Paper Award (Zhikua);
- — Generation Google Scholarship (Nezihe Merve Gurel);
- — MIT Technology Review Latin American Innovators Under 35 (Leonel Aguilar);
- — SNSF Eccellenza Professorial Fellowship (Thomas Lemmin);
- — ERC Grant.
- Many former group members now hold faculty positions at top institutions (e.g., HKUST, TU Delft, University of Copenhagen, Wuhan University) or lead roles in industry.
Research Experience
- Associate Professor at the University of Chicago, leading research in ML systems and data management for ML.
- Former Associate Professor at ETH Zurich.
- Co-Editor-in-Chief of DMLR (Data-Centric Machine Learning Research), a new journal in the JMLR family.
- Active in community-building efforts in MLSys, data management for ML, and Data-centric AI.
- Led or co-led influential projects such as DataPerf (benchmarks for data-centric AI) and DataScope/ArgusEyes (data quality systems based on PTIME Data Shapley).
Background
- Currently the CTO of Together, building a cloud platform tailored for artificial intelligence.
- Neubauer Associate Professor in the Department of Computer Science at the University of Chicago.
- Formerly an Associate Professor at ETH Zurich.
- Currently a PhD mentor at INSAIT.
- Research interests center on the fundamental tension between data, model, computation, and infrastructure, with the goal of democratizing machine learning for societal benefit.
- Advocates a systems approach to emerging challenges; current research focuses on next-generation ML platforms that are data-centric, human-centric, and declaratively scalable.
