Detecting insider threats in a real corporate database of computer usage activity. - Ted E. Senator; Henry G. Goldberg; Alex Memory; William T. Young; Brad Rees; Robert Pierce; Daniel Huang 003; Matthew Reardon; David A. Bader; Edmond Chow; Irfan A. Essa; Joshua Jones; et al. (KDD, 2013)

Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters. - Seunghwa Kang; Alexandre Fender; Joe Eaton; Brad Rees (HPEC, 2020)

Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS. - Bartley Richardson; Bradley Rees; Tom Drabas; Even Oldridge; David A. Bader; Rachel Allen (KDD, 2020)

Semiring Primitives for Sparse Neighborhood Methods on the GPU. - Corey J. Nolet; Divye Gala; Edward Raff; Joe Eaton; Brad Rees; John Zedlewski; Tim Oates 001 (CoRR, 2021)

Iterative Learning Control With Time-Delay Compensation: An Application to Twin-Roll Strip Casting. - Florian Browne; Brad Rees; George T.-C. Chiu; Neera Jain (IEEE Trans. Control. Syst. Technol., 2021)

RAPIDS cuGraph. - Alexandre Fender; Brad Rees; Joe Eaton (Massive Graph Analytics, 2022)

Analyzing Multi-trillion Edge Graphs on Large GPU Clusters: A Case Study with PageRank. - Seunghwa Kang; Joseph Nke; Brad Rees (HPEC, 2022)

Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass. - Xin Huang 020; Jongryool Kim; Bradley Rees; Chul-Ho Lee (IISWC, 2022)

Accelerated GNN Training with DGL and RAPIDS cuGraph in a Fraud Detection Workflow. - Brad Rees; Xiaoyun Wang 001; Joe Eaton; Onur Yilmaz; Rick Ratzel; Dominique LaSalle (KDD, 2022)

GPU Semiring Primitives for Sparse Neighborhood Methods. - Corey J. Nolet; Divye Gala; Edward Raff; Joe Eaton; Brad Rees; Tim Oates 001 (MLSys, 2022)

cuGraph C++ primitives: vertex/edge-centric building blocks for parallel graph computing. - Seunghwa Kang; Chuck Hastings; Joe Eaton; Brad Rees (IPDPS Workshops, 2023)

cuSLINK: Single-Linkage Agglomerative Clustering on the GPU. - Corey J. Nolet; Divye Gala; Alexandre Fender; Mahesh Doijade; Joe Eaton; Edward Raff; John Zedlewski; Brad Rees; Tim Oates 001 (ECML/PKDD (1), 2023)

CATGNN: Cost-Efficient and Scalable Distributed Training for Graph Neural Networks. - Xin Huang 020; Weipeng Zhuo; Minh Phu Vuong; Shiju Li 001; Jongryool Kim; Bradley Rees; Chul-Ho Lee (CoRR, 2024)