TOCTOU, Traps, and Trusted Computing. - Sergey Bratus; Nihal D'Cunha; Evan Randall Sparks; Sean W. Smith (TRUST, 2008)

MLI: An API for Distributed Machine Learning. - Evan Randall Sparks; Ameet Talwalkar; Virginia Smith; Jey Kottalam; Xinghao Pan; Joseph E. Gonzalez; Michael J. Franklin; Michael I. Jordan; Tim Kraska (ICDM, 2013)

Scientific computing meets big data technology: An astronomy use case. - Zhao Zhang 007; Kyle Barbary; Frank Austin Nothaft; Evan Randall Sparks; Oliver Zahn; Michael J. Franklin; David A. Patterson 001; Saul Perlmutter (IEEE BigData, 2015)

Automating model search for large scale machine learning. - Evan Randall Sparks; Ameet Talwalkar; Daniel Haas; Michael J. Franklin; Michael I. Jordan; Tim Kraska (SoCC, 2015)

Embarrassingly Parallel Time Series Analysis for Large Scale Weak Memory Systems. - Francois Belletti; Evan Randall Sparks; Michael J. Franklin; Alexandre M. Bayen (CoRR, 2015)

TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries. - Evan Randall Sparks; Ameet Talwalkar; Michael J. Franklin; Michael I. Jordan; Tim Kraska (CoRR, 2015)

linalg: Matrix Computations in Apache Spark. - Reza Bosagh Zadeh; Xiangrui Meng; Burak Yavuz; Aaron Staple; Li Pu; Shivaram Venkataraman; Evan Randall Sparks; Alexander Ulanov; Matei Zaharia (CoRR, 2015)

Matrix Computations and Optimization in Apache Spark. - Reza Bosagh Zadeh; Xiangrui Meng; Alexander Ulanov; Burak Yavuz; Li Pu; Shivaram Venkataraman; Evan Randall Sparks; Aaron Staple; Matei Zaharia (KDD, 2016)

Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies. - Francois W. Belletti; Evan Randall Sparks; Michael J. Franklin; Alexandre M. Bayen; Joseph E. Gonzalez (CoRR, 2016)

MLlib: Machine Learning in Apache Spark. - Xiangrui Meng; Joseph K. Bradley; Burak Yavuz; Evan Randall Sparks; Shivaram Venkataraman; Davies Liu; Jeremy Freeman; D. B. Tsai; Manish Amde; Sean Owen; Doris Xin; Reynold Xin; et al. (J. Mach. Learn. Res., 2016)

End-to-End Large Scale Machine Learning with KeystoneML. - Evan Randall Sparks (2016)

Random projection design for scalable implicit smoothing of randomly observed stochastic processes. - Francois Belletti; Evan Randall Sparks; Alexandre M. Bayen; Joseph Gonzalez 001 (AISTATS, 2017)

Diagnosing Machine Learning Pipelines with Fine-grained Lineage. - Zhao Zhang 007; Evan Randall Sparks; Michael J. Franklin (HPDC, 2017)

KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics. - Evan Randall Sparks; Shivaram Venkataraman; Tomer Kaftan; Michael J. Franklin; Benjamin Recht (ICDE, 2017)

Paleo: A Performance Model for Deep Neural Networks. - Hang Qi; Evan Randall Sparks; Ameet Talwalkar (ICLR (Poster), 2017)

Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning. - Liam Li; Evan Randall Sparks; Kevin G. Jamieson; Ameet Talwalkar (CoRR, 2019)

SysML: The New Frontier of Machine Learning Systems. - Alexander Ratner; Dan Alistarh; Gustavo Alonso; David G. Andersen; Peter Bailis; Sarah Bird; Nicholas Carlini; Bryan Catanzaro; Eric S. Chung; Bill Dally; Jeff Dean; Inderjit S. Dhillon; et al. (CoRR, 2019)

Kira: Processing Astronomy Imagery Using Big Data Technology. - Zhao Zhang 007; Kyle Barbary; Frank Austin Nothaft; Evan Randall Sparks; Oliver Zahn; Michael J. Franklin; David A. Patterson 001; Saul Perlmutter (IEEE Trans. Big Data, 2020)

AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. - Nicholas Roberts; Samuel Guo; Cong Xu; Ameet Talwalkar; David Lander; Lvfang Tao; Linhang Cai; Shuaicheng Niu; Jianyu Heng; Hongyang Qin; Minwen Deng; Johannes Hog; et al. (NeurIPS (Competition and Demos), 2021)