GRNN: Low-Latency and Scalable RNN Inference on GPUs. - Connor Holmes; Daniel Mawhirter; Yuxiong He; Feng Yan 001; Bo Wu 002 (EuroSys, 2019)

GraphZero: Breaking Symmetry for Efficient Graph Mining. - Daniel Mawhirter; Sam Reinehr; Connor Holmes; Tongping Liu; Bo Wu 002 (CoRR, 2019)

Dryadic: Flexible and Fast Graph Pattern Matching at Scale. - Daniel Mawhirter; Sam Reinehr; Wei Han; Noah Fields; Miles Claver; Connor Holmes; Jedidiah McClurg; Tongping Liu; Bo Wu 002 (PACT, 2021)

NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM. - Connor Holmes; Minjia Zhang; Yuxiong He; Bo Wu 002 (NeurIPS, 2021)

ELIχR: Eliminating Computation Redundancy in CNN-Based Video Processing. - Jordan Schmerge; Daniel Mawhirter; Connor Holmes; Jedidiah McClurg; Bo Wu 002 (RSDHA@SC, 2021)

Practical Design Considerations for Performance and Robustness in the Face of Uncertain Flexible Dynamics in Space Manipulators. - Connor Holmes (Frontiers Robotics AI, 2021)

GraphZero: A High-Performance Subgraph Matching System. - Daniel Mawhirter; Sam Reinehr; Connor Holmes; Tongping Liu; Bo Wu 002 (ACM SIGOPS Oper. Syst. Rev., 2021)

DGSM: A GPU-Based Subgraph Isomorphism framework with DFS exploration. - Wei Han; Connor Holmes; Bo Wu 002 (RSDHA@SC, 2022)

A Fine Line: Total Least-Squares Line Fitting as QCQP Optimization. - Timothy D. Barfoot; Connor Holmes; Frederike Dümbgen (CoRR, 2022)

Compressing Pre-trained Transformers via Low-Bit NxM Sparsity for Natural Language Understanding. - Connor Holmes; Minjia Zhang; Yuxiong He; Bo Wu 002 (CoRR, 2022)

Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers. - Zhewei Yao; Xiaoxia Wu; Conglong Li; Connor Holmes; Minjia Zhang; Cheng Li 001; Yuxiong He (CoRR, 2022)

Towards Open World NeRF-Based SLAM. - Daniil Lisus; Connor Holmes; Steven Lake Waslander (CRV, 2023)

Towards Open World NeRF-Based SLAM. - Daniil Lisus; Connor Holmes (CoRR, 2023)

ZeRO++: Extremely Efficient Collective Communication for Giant Model Training. - Guanhua Wang; Heyang Qin; Sam Ade Jacobs; Connor Holmes; Samyam Rajbhandari; Olatunji Ruwase; Feng Yan 001; Lei Yang 001; Yuxiong He (CoRR, 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)

STAR-loc: Dataset for STereo And Range-based localization. - Frederike Dümbgen; Mohammed Ayman Shalaby; Connor Holmes; Charles Champagne Cossette; James Richard Forbes; Jerome Le Ny; Timothy D. Barfoot (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)

Safe and Smooth: Certified Continuous-Time Range-Only Localization. - Frederike Dümbgen; Connor Holmes; Timothy D. Barfoot (IEEE Robotics Autom. Lett., 2023)

An Efficient Global Optimality Certificate for Landmark-Based SLAM. - Connor Holmes; Timothy D. Barfoot (IEEE Robotics Autom. Lett., 2023)

DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing. - Conglong Li; Zhewei Yao; Xiaoxia Wu; Minjia Zhang; Connor Holmes; Cheng Li 001; Yuxiong He (AAAI, 2024)

ZeRO++: Extremely Efficient Collective Communication for Large Model Training. - Guanhua Wang; Heyang Qin; Sam Ade Jacobs; Xiaoxia Wu; Connor Holmes; Zhewei Yao; Samyam Rajbhandari; Olatunji Ruwase; Feng Yan 001; Lei Yang 001; Yuxiong He (ICLR, 2024)

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)

Exploiting Chordal Sparsity for Fast Global Optimality with Application to Localization. - Frederike Dümbgen; Connor Holmes; Timothy D. Barfoot (CoRR, 2024)

Toward Globally Optimal State Estimation Using Automatically Tightened Semidefinite Relaxations. - Frederike Dümbgen; Connor Holmes; Ben Agro; Timothy D. Barfoot (IEEE Trans. Robotics, 2024)

On Semidefinite Relaxations for Matrix-Weighted State-Estimation Problems in Robotics. - Connor Holmes; Frederike Dümbgen; Timothy D. Barfoot (IEEE Trans. Robotics, 2024)

Certifiably optimal rotation and pose estimation based on the Cayley map. - Timothy D. Barfoot; Connor Holmes; Frederike Dümbgen (Int. J. Robotics Res., 2025)

SDPRLayers: Certifiable Backpropagation Through Polynomial Optimization Problems in Robotics. - Connor Holmes; Frederike Dümbgen; Timothy D. Barfoot (IEEE Trans. Robotics, 2025)

RenAIssance: A Survey Into AI Text-to-Image Generation in the Era of Large Model. - Fengxiang Bie; Yibo Yang; Zhongzhu Zhou; Adam Ghanem; Minjia Zhang; Zhewei Yao; Xiaoxia Wu; Connor Holmes; Pareesa Ameneh Golnari; David A. Clifton; Yuxiong He; Dacheng Tao; et al. (IEEE Trans. Pattern Anal. Mach. Intell., 2025)