Inverted indices for particle tracking in petascale cosmological simulations. - Daniel Crankshaw; Randal C. Burns; Bridget Falck; Tamas Budavari; Alexander S. Szalay; Jie Wang 075 (SSDBM, 2013)

GraphX: Graph Processing in a Distributed Dataflow Framework. - Joseph E. Gonzalez; Reynold S. Xin; Ankur Dave; Daniel Crankshaw; Michael J. Franklin; Ion Stoica (OSDI, 2014)

GraphX: Unifying Data-Parallel and Graph-Parallel Analytics. - Reynold S. Xin; Daniel Crankshaw; Ankur Dave; Joseph E. Gonzalez; Michael J. Franklin; Ion Stoica (CoRR, 2014)

The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. - Daniel Crankshaw; Peter Bailis; Joseph E. Gonzalez; Haoyuan Li 001; Zhao Zhang 007; Michael J. Franklin; Ali Ghodsi 002; Michael I. Jordan (CIDR, 2015)

Clipper: A Low-Latency Online Prediction Serving System. - Daniel Crankshaw; Xin Wang 066; Giulio Zhou; Michael J. Franklin; Joseph E. Gonzalez; Ion Stoica (NSDI, 2017)

IDK Cascades: Fast Deep Learning by Learning not to Overthink. - Xin Wang 066; Yujia Luo; Daniel Crankshaw; Alexey Tumanov; Joseph E. Gonzalez (CoRR, 2017)

Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning. - Richard Liaw; Sanjay Krishnan; Animesh Garg; Daniel Crankshaw; Joseph E. Gonzalez; Ken Goldberg (CoRR, 2017)

IDK Cascades: Fast Deep Learning by Learning not to Overthink. - Xin Wang 066; Yujia Luo; Daniel Crankshaw; Alexey Tumanov; Fisher Yu 001; Joseph E. Gonzalez (UAI, 2018)

Research for practice: prediction-serving systems. - Daniel Crankshaw; Joseph Gonzalez 001; Peter Bailis (Commun. ACM, 2018)

InferLine: ML Inference Pipeline Composition Framework. - Daniel Crankshaw; Gur-Eyal Sela; Corey Zumar; Xiangxi Mo; Joseph E. Gonzalez; Ion Stoica; Alexey Tumanov (CoRR, 2018)

The Design and Implementation of Low-Latency Prediction Serving Systems. - Daniel Crankshaw (2019)

InferLine: latency-aware provisioning and scaling for prediction serving pipelines. - Daniel Crankshaw; Gur-Eyal Sela; Xiangxi Mo; Corey Zumar; Ion Stoica; Joseph Gonzalez 001; Alexey Tumanov (SoCC, 2020)

SOL: safe on-node learning in cloud platforms. - Yawen Wang; Daniel Crankshaw; Neeraja J. Yadwadkar; Daniel S. Berger; Christos Kozyrakis; Ricardo Bianchini (ASPLOS, 2022)

Mitigating the Performance Impact of Network Failures in Public Clouds. - Pooria Namyar; Behnaz Arzani; Daniel Crankshaw; Daniel S. Berger; Kevin Hsieh; Srikanth Kandula; Ramesh Govindan (CoRR, 2023)

Solving Max-Min Fair Resource Allocations Quickly on Large Graphs. - Pooria Namyar; Behnaz Arzani; Srikanth Kandula; Santiago Segarra; Daniel Crankshaw; Umesh Krishnaswamy; Ramesh Govindan; Himanshu Raj (NSDI, 2024)

Enhancing Network Failure Mitigation with Performance-Aware Ranking. - Pooria Namyar; Arvin Ghavidel; Daniel Crankshaw; Daniel S. Berger; Kevin Hsieh; Srikanth Kandula; Ramesh Govindan; Behnaz Arzani (NSDI, 2025)