Retaining sandbox containment despite bugs in privileged memory-safe code. - Justin Cappos; Armon Dadgar; Jeff Rasley; Justin Samuel; Ivan Beschastnikh; Cosmin Barsan; Arvind Krishnamurthy; Thomas E. Anderson (CCS, 2010)

Low-latency Network Monitoring via Oversubscribed Port Mirroring. - Jeff Rasley; Brent E. Stephens; Colin Dixon; Eric Rozner; Wes Felter; Kanak Agarwal 001; John B. Carter; Rodrigo Fonseca (ONS, 2014)

Planck: millisecond-scale monitoring and control for commodity networks. - Jeff Rasley; Brent E. Stephens; Colin Dixon; Eric Rozner; Wes Felter; Kanak Agarwal 001; John B. Carter; Rodrigo Fonseca (SIGCOMM, 2014)

Crowdsourcing from Scratch: A Pragmatic Experiment in Data Collection by Novice Requesters. - Alexandra Papoutsaki; Hua Guo; Danaë Metaxa-Kakavouli; Connor Gramazio; Jeff Rasley; Wenting Xie; Guan Wang; Jeff Huang 002 (HCOMP, 2015)

Detecting latent cross-platform API violations. - Jeff Rasley; Eleni Gessiou; Tony Ohmann; Yuriy Brun; Shriram Krishnamurthi; Justin Cappos (ISSRE, 2015)

Efficient queue management for cluster scheduling. - Jeff Rasley; Konstantinos Karanasos; Srikanth Kandula; Rodrigo Fonseca; Milan Vojnovic; Sriram Rao (EuroSys, 2016)

HyperDrive: exploring hyperparameters with POP scheduling. - Jeff Rasley; Yuxiong He; Feng Yan 001; Olatunji Ruwase; Rodrigo Fonseca (Middleware, 2017)

Accelerating Large Scale Deep Learning Inference through DeepCPU at Microsoft. - Minjia Zhang; Samyam Rajbhandari; Wenhan Wang; Elton Zheng; Olatunji Ruwase; Jeff Rasley; Jason Li; Junhua Wang; Yuxiong He (OpML, 2019)

ZeRO: Memory Optimization Towards Training A Trillion Parameter Models. - Samyam Rajbhandari; Jeff Rasley; Olatunji Ruwase; Yuxiong He (CoRR, 2019)

Application-Aware Cluster Resource Management. - Jeff Rasley (2019)

DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters. - Jeff Rasley; Samyam Rajbhandari; Olatunji Ruwase; Yuxiong He (KDD, 2020)

ZeRO: memory optimizations toward training trillion parameter models. - Samyam Rajbhandari; Jeff Rasley; Olatunji Ruwase; Yuxiong He (SC, 2020)

ZeRO-infinity: breaking the GPU memory wall for extreme scale deep learning. - Samyam Rajbhandari; Olatunji Ruwase; Jeff Rasley; Shaden Smith; Yuxiong He (SC, 2021)

DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale. - Samyam Rajbhandari; Conglong Li; Zhewei Yao; Minjia Zhang; Reza Yazdani Aminabadi; Ammar Ahmad Awan; Jeff Rasley; Yuxiong He (ICML, 2022)

DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale. - Reza Yazdani Aminabadi; Samyam Rajbhandari; Ammar Ahmad Awan; Cheng Li 001; Du Li; Elton Zheng; Olatunji Ruwase; Shaden Smith; Minjia Zhang; Jeff Rasley; Yuxiong He (SC, 2022)

MCR-DL: Mix-and-Match Communication Runtime for Deep Learning. - Quentin Anthony; Ammar Ahmad Awan; Jeff Rasley; Yuxiong He; Aamir Shafi; Mustafa Abduljabbar; Hari Subramoni; Dhabaleswar K. Panda 001 (IPDPS, 2023)

DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales. - Zhewei Yao; Reza Yazdani Aminabadi; Olatunji Ruwase; Samyam Rajbhandari; Xiaoxia Wu; Ammar Ahmad Awan; Jeff Rasley; Minjia Zhang; Conglong Li; Connor Holmes; Zhongzhu Zhou; Michael Wyatt; et al. (CoRR, 2023)

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. - Shuaiwen Leon Song; Bonnie Kruft; Minjia Zhang; Conglong Li; Shiyang Chen 004; Chengming Zhang 006; Masahiro Tanaka; Xiaoxia Wu; Jeff Rasley; Ammar Ahmad Awan; Connor Holmes; Martin Cai; et al. (CoRR, 2023)

DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference. - Connor Holmes; Masahiro Tanaka; Michael Wyatt; Ammar Ahmad Awan; Jeff Rasley; Samyam Rajbhandari; Reza Yazdani Aminabadi; Heyang Qin; Arash Bakhtiari; Lev Kurilenko; Yuxiong He (CoRR, 2024)

Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences. - Stas Bekman; Samyam Rajbhandari; Michael Wyatt; Jeff Rasley; Tunji Ruwase; Zhewei Yao; Aurick Qiao; Yuxiong He (CoRR, 2025)

Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment. - Pawel Renc; Michal K. Grzeszczyk; Linglong Qian; Nassim Oufattole; Jeff Rasley; Arkadiusz Sitek (CoRR, 2025)

Arctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI. - Samyam Rajbhandari; Mert Hidayetoglu; Aurick Qiao; Ye Wang; Juncheng Yang; Jeff Rasley; Michael Wyatt; Yuxiong He (CoRR, 2025)