The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps. - Hamza Harkous; Rameez Rahman; Bojan Karlas; Karl Aberer (Proc. Priv. Enhancing Technol., 2016)

Network Scheduling in the Dark. - Vojislav Dukic; Sangeetha Abdu Jyothi; Bojan Karlas; Muhsen Owaida; Ce Zhang 001; Ankit Singla (SoCC, 2018)

Multi-device, Multi-tenant Model Selection with GP-EI. - Chen Yu 009; Bojan Karlas; Jie Zhong; Ce Zhang 001; Ji Liu 002 (CoRR, 2018)

Ease.ml in Action: Towards Multi-tenant Declarative Learning Services. - Bojan Karlas; Ji Liu 002; Wentao Wu 001; Ce Zhang 001 (Proc. VLDB Endow., 2018)

AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI. - Chen Yu 009; Bojan Karlas; Jie Zhong; Ce Zhang 001; Ji Liu 002 (AISTATS, 2019)

Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment. - Cédric Renggli; Bojan Karlas; Bolin Ding; Feng Liu; Kevin Schawinski; Wentao Wu 001; Ce Zhang 001 (SysML, 2019)

Is advance knowledge of flow sizes a plausible assumption? - Vojislav Dukic; Sangeetha Abdu Jyothi; Bojan Karlas; Muhsen Owaida; Ce Zhang 001; Ankit Singla (NSDI, 2019)

Data Science through the looking glass and what we found there. - Fotis Psallidas; Yiwen Zhu; Bojan Karlas; Matteo Interlandi; Avrilia Floratou; Konstantinos Karanasos; Wentao Wu 001; Ce Zhang 001; Subru Krishnan; Carlo Curino; Markus Weimer (CoRR, 2019)

Ease.ml/ci and Ease.ml/meter in Action: Towards Data Management for Statistical Generalization. - Cédric Renggli; Frances Ann Hubis; Bojan Karlas; Kevin Schawinski; Wentao Wu 001; Ce Zhang 001 (Proc. VLDB Endow., 2019)

Building Continuous Integration Services for Machine Learning. - Bojan Karlas; Matteo Interlandi; Cédric Renggli; Wentao Wu 001; Ce Zhang 001; Deepak Mukunthu Iyappan Babu; Jordan Edwards; Chris Lauren; Andy Xu; Markus Weimer (KDD, 2020)

End-to-end Robustness for Sensing-Reasoning Machine Learning Pipelines. - Zhuolin Yang; Zhikuan Zhao; Hengzhi Pei; Boxin Wang; Bojan Karlas; Ji Liu 002; Heng Guo 001; Bo Li 026; Ce Zhang 001 (CoRR, 2020)

Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions. - Bojan Karlas; Peng Li 062; Renzhi Wu; Nezihe Merve Gürel; Xu Chu 002; Wentao Wu 001; Ce Zhang 001 (Proc. VLDB Endow., 2020)

Online Active Model Selection for Pre-trained Classifiers. - Mohammad Reza Karimi; Nezihe Merve Gürel; Bojan Karlas; Johannes Rausch; Ce Zhang 001; Andreas Krause 001 (AISTATS, 2021)

Ease.ML: A Lifecycle Management System for Machine Learning. - Leonel Aguilar Melgar; David Dao; Shaoduo Gan; Nezihe Merve Gürel; Nora Hollenstein; Jiawei Jiang 001; Bojan Karlas; Thomas Lemmin; Tian Li 005; Yang Li 106; Susie Xi Rao; Johannes Rausch; et al. (CIDR, 2021)

A Data Quality-Driven View of MLOps. - Cédric Renggli; Luka Rimanic; Nezihe Merve Gürel; Bojan Karlas; Wentao Wu 001; Ce Zhang 001 (IEEE Data Eng. Bull., 2021)

Screening Native Machine Learning Pipelines with ArgusEyes. - Sebastian Schelter; Stefan Grafberger; Shubha Guha; Olivier Sprangers; Bojan Karlas; Ce Zhang 001 (CIDR, 2022)

dcbench: a benchmark for data-centric AI systems. - Sabri Eyuboglu; Bojan Karlas; Christopher Ré; Ce Zhang 001; James Zou 001 (DEEM@SIGMOD, 2022)

Improving Certified Robustness via Statistical Learning with Logical Reasoning. - Zhuolin Yang; Zhikuan Zhao; Boxin Wang; Jiawei Zhang 002; Linyi Li 001; Hengzhi Pei; Bojan Karlas; Ji Liu 002; Heng Guo 001; Ce Zhang 001; Bo Li 026 (NeurIPS, 2022)

Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines. - Bojan Karlas; David Dao; Matteo Interlandi; Bo Li 026; Sebastian Schelter; Wentao Wu 001; Ce Zhang 001 (CoRR, 2022)

DataPerf: Benchmarks for Data-Centric AI Development. - Mark Mazumder; Colby R. Banbury; Xiaozhe Yao; Bojan Karlas; William Gaviria Rojas; Sudnya Frederick Diamos; Greg Diamos; Lynn He; Douwe Kiela; David Jurado; David Kanter; Rafael Mosquera; et al. (CoRR, 2022)

Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines. - Fotis Psallidas; Yiwen Zhu; Bojan Karlas; Jordan Henkel; Matteo Interlandi; Subru Krishnan; Brian Kroth; K. Venkatesh Emani; Wentao Wu 001; Ce Zhang 001; Markus Weimer; Avrilia Floratou; et al. (SIGMOD Rec., 2022)

Data Systems for Managing and Debugging Machine Learning Workflows. - Bojan Karlas (2022)

schelterlabs/arguseyes. - Sebastian Schelter; Stefan Grafberger; Shubha Guha; Olivier Sprangers; Bojan Karlas; Ce Zhang 001 (Zenodo, 2022)

DataPerf: Benchmarks for Data-Centric AI Development. - Mark Mazumder; Colby R. Banbury; Xiaozhe Yao; Bojan Karlas; William Gaviria Rojas; Sudnya Frederick Diamos; Greg Diamos; Lynn He; Alicia Parrish; Hannah Rose Kirk; Jessica Quaye; Charvi Rastogi; et al. (NeurIPS, 2023)

Proactively Screening Machine Learning Pipelines with ARGUSEYES. - Sebastian Schelter; Stefan Grafberger; Shubha Guha; Bojan Karlas; Ce Zhang 001 (SIGMOD Conference Companion, 2023)

RAB: Provable Robustness Against Backdoor Attacks. - Maurice Weber; Xiaojun Xu; Bojan Karlas; Ce Zhang 001; Bo Li 026 (SP, 2023)

DMLR: Data-centric Machine Learning Research - Past, Present and Future. - Luis Oala; Manil Maskey; Lilith Bat-Leah; Alicia Parrish; Nezihe Merve Gürel; Tzu-Sheng Kuo; Yang Liu 018; Rotem Dror; Danilo Brajovic; Xiaozhe Yao; Max Bartolo; William Gaviria Rojas; et al. (CoRR, 2023)

Data Debugging with Shapley Importance over Machine Learning Pipelines. - Bojan Karlas; David Dao; Matteo Interlandi; Sebastian Schelter; Wentao Wu 001; Ce Zhang 001 (ICLR, 2024)

Navigating Data Errors in Machine Learning Pipelines: Identify, Debug, and Learn. - Bojan Karlas; Babak Salimi; Sebastian Schelter (ICDE, 2025)