Empirical Studies of Three Commonly Used Process Mining Algorithms. - Wenyu Peng; Zhenyu Zhang 009; Ryan Hildebrant; Shangping Ren (SMC, 2021)

Improving Process Discovery Results by Filtering Out Outliers from Event Logs with Hidden Markov Models. - Zhenyu Zhang 009; Ryan Hildebrant; Fatemeh Asgarinejad; Nalini Venkatasubramanian; Shangping Ren (CBI (1), 2021)

A Generalizable Approach for Determining The Sensitivity of A Trace within An Event Log. - Ryan Hildebrant; Zhenyu Zhang 009; Shangping Ren (EMISA Forum, 2021)

Using Domain Knowledge to Assist Process Scenario Discoveries. - Zhenyu Zhang 009; Junjie Zhu; Ryan Hildebrant; Nalini Venkatasubramanian; Shangping Ren (COMPSAC, 2022)

PMDG: Privacy for Multi-perspective Process Mining Through Data Generalization. - Ryan Hildebrant; Stephan A. Fahrenkrog-Petersen; Matthias Weidlich 001; Shangping Ren (CAiSE, 2023)

Towards Better Bounds for Finding Quasi-Identifiers. - Ryan Hildebrant; Quoc-Tung Le; Duy-Hoang Ta 001; Hoa T. Vu (PODS, 2023)

BatchIT: Intelligent and Efficient Batching for IoT Workloads at the Edge. - Guoxi Wang; Ryan Hildebrant; Andrew Chio; Sharad Mehrotra; Nalini Venkatasubramanian (NOMS, 2024)

Optimized Early Prediction of Business Processes with Hyperdimensional Computing. - Fatemeh Asgarinejad; Anthony Thomas; Ryan Hildebrant; Zhenyu Zhang 009; Shangping Ren; Tajana Rosing; Baris Aksanli (Inf., 2024)

DIM-SUM: Dynamic IMputation for Smart Utility Management. - Ryan Hildebrant; Rahul Atul Bhope; Sharad Mehrotra; Christopher Tull; Nalini Venkatasubramanian (CoRR, 2025)