| Mllib: Machine learning in apache spark X Meng, J Bradley, B Yavuz, E Sparks, S Venkataraman, D Liu, ... Journal of Machine Learning Research 17 (34), 1-7, 2016 | 2476 | 2016 |
| MLI: An API for distributed machine learning ER Sparks, A Talwalkar, V Smith, J Kottalam, X Pan, J Gonzalez, ... 2013 IEEE 13th International Conference on Data Mining, 1187-1192, 2013 | 247 | 2013 |
| Paleo: A performance model for deep neural networks H Qi, ER Sparks, A Talwalkar International Conference on Learning Representations, 2017 | 238 | 2017 |
| Automating model search for large scale machine learning ER Sparks, A Talwalkar, D Haas, MJ Franklin, MI Jordan, T Kraska Proceedings of the Sixth ACM Symposium on Cloud Computing, 368-380, 2015 | 201 | 2015 |
| Keystoneml: Optimizing pipelines for large-scale advanced analytics ER Sparks, S Venkataraman, T Kaftan, MJ Franklin, B Recht 2017 IEEE 33rd international conference on data engineering (ICDE), 535-546, 2017 | 178 | 2017 |
| Matrix computations and optimization in apache spark R Bosagh Zadeh, X Meng, A Ulanov, B Yavuz, L Pu, S Venkataraman, ... Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016 | 140 | 2016 |
| TOCTOU, traps, and trusted computing S Bratus, N D’Cunha, E Sparks, SW Smith International Conference on Trusted Computing, 14-32, 2008 | 103 | 2008 |
| A Security Assessment of Trusted Platform Modules ER Sparks Technical report, Department of Computer Science, 2007 | 98 | 2007 |
| Scientific computing meets big data technology: An astronomy use case Z Zhang, K Barbary, FA Nothaft, E Sparks, O Zahn, MJ Franklin, ... 2015 IEEE International Conference on Big Data (Big Data), 918-927, 2015 | 78 | 2015 |
| Kira: Processing astronomy imagery using big data technology Z Zhang, K Barbary, FA Nothaft, ER Sparks, O Zahn, MJ Franklin, ... IEEE Transactions on Big Data 6 (2), 369-381, 2016 | 42 | 2016 |
| Diagnosing machine learning pipelines with fine-grained lineage Z Zhang, ER Sparks, MJ Franklin Proceedings of the 26th international symposium on high-performance parallel …, 2017 | 33 | 2017 |
| Sysml: The new frontier of machine learning systems A Ratner, D Alistarh, G Alonso, P Bailis, S Bird, N Carlini, B Catanzaro, ... arXiv preprint arXiv:1904.03257 98, 2019 | 29 | 2019 |
| Tupaq: An efficient planner for large-scale predictive analytic queries ER Sparks, A Talwalkar, MJ Franklin, MI Jordan, T Kraska arXiv preprint arXiv:1502.00068, 2015 | 25 | 2015 |
| Mlsys: The new frontier of machine learning systems A Ratner, D Alistarh, G Alonso, DG Andersen, P Bailis, S Bird, N Carlini, ... arXiv preprint arXiv:1904.03257, 2019 | 21 | 2019 |
| Mlbase: A distributed machine learning wrapper A Talwalkar, T Kraska, R Griffith, J Duchi, J Gonzalez, D Britz, X Pan, ... NIPS Big Learning Workshop, 35, 2012 | 21 | 2012 |
| ML pipelines: a new high-level API for MLlib X Meng, J Bradley, E Sparks, S Venkataraman | 14 | 2015 |
| Nine Young Bankers Who Changed America: Thomas Sudman E Sparks ABA Banking Journal, 2017 | 11 | 2017 |
| MLlib & E Sparks | 11 | 2014 |
| End-to-end large scale machine learning with KeystoneML ER Sparks UC Berkeley, 2016 | 10 | 2016 |
| Exploiting reuse in pipeline-aware hyperparameter tuning L Li, E Sparks, K Jamieson, A Talwalkar arXiv preprint arXiv:1903.05176, 2019 | 9 | 2019 |
Michael FranklinUniversity of ChicagoVerified email at uchicago.edu
