Diagnosability of stochastic automata. - David Thorsley; Demosthenis Teneketzis (CDC, 2003)

Diagnosability of stochastic discrete-event systems. - David Thorsley; Demosthenis Teneketzis (IEEE Trans. Autom. Control., 2005)

Intrusion Detection in Controlled Discrete Event Systems. - David Thorsley; Demosthenis Teneketzis (CDC, 2006)

Active Acquisition of Information for Diagnosis and Supervisory Control of Discrete Event Systems. - David Thorsley; Demosthenis Teneketzis (Discret. Event Dyn. Syst., 2007)

Model reduction of stochastic processes using Wasserstein pseudometrics. - David Thorsley; Eric Klavins (ACC, 2008)

Diagnosability of stochastic discrete-event systems under unreliable observations. - David Thorsley; Tae-Sic Yoo; Humberto E. Garcia (ACC, 2008)

Sequential window diagnoser for discrete-event systems under unreliable observations. - Wen-Chiao Lin; Humberto E. Garcia; David Thorsley; Tae-Sic Yoo (Allerton, 2009)

Hidden Markov Models for non-well-mixed reaction networks. - Nils Napp; David Thorsley; Eric Klavins (ACC, 2009)

Diagnosability of stochastic chemical kinetic systems: a discrete event systems approach. - David Thorsley (ACC, 2010)

Individualized performance prediction during total sleep deprivation: Accounting for trait vulnerability to sleep loss. - Sridhar Ramakrishnan 002; Srinivas Laxminarayan; David Thorsley; Nancy J. Wesensten; Thomas J. Balkin; Jaques Reifman (EMBC, 2012)

A necessary and sufficient condition for diagnosability of stochastic discrete event systems. - David Thorsley (Discret. Event Dyn. Syst., 2017)

Post-training Piecewise Linear Quantization for Deep Neural Networks. - Jun Fang; Ali Shafiee; Hamzah Abdel-Aziz; David Thorsley; Georgios Georgiadis; Joseph Hassoun (ECCV (2), 2020)

Near-Lossless Post-Training Quantization of Deep Neural Networks via a Piecewise Linear Approximation. - Jun Fang; Ali Shafiee; Hamzah Abdel-Aziz; David Thorsley; Georgios Georgiadis; Joseph Hassoun (CoRR, 2020)

Learned Token Pruning for Transformers. - Sehoon Kim 001; Sheng Shen 001; David Thorsley; Amir Gholami; Joseph Hassoun; Kurt Keutzer (CoRR, 2021)

Learned Token Pruning for Transformers. - Sehoon Kim 001; Sheng Shen 001; David Thorsley; Amir Gholami; Woosuk Kwon; Joseph Hassoun; Kurt Keutzer (KDD, 2022)

SaiT: Sparse Vision Transformers through Adaptive Token Pruning. - Ling Li; David Thorsley; Joseph Hassoun (CoRR, 2022)

PROV-IO+: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems. - Runzhou Han; Mai Zheng; Suren Byna; Houjun Tang; Bin Dong 002; Dong Dai 001; Yong Chen 001; Dongkyun Kim; Joseph Hassoun; David Thorsley; Matthew Wolf (CoRR, 2023)

PROV-IO$^+$+: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems. - Runzhou Han; Mai Zheng; Suren Byna; Houjun Tang; Bin Dong 002; Dong Dai 001; Yong Chen 001; Dongkyun Kim; Joseph Hassoun; David Thorsley (IEEE Trans. Parallel Distributed Syst., 2024)