{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129342"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129342","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Enhancing safety and resilience in AI-driven systems: from autonomous vehicles to data centers","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Ziheng Chen, accepted the attached license on 2025-05-07 at 16:54.","The student, Ziheng Chen, submitted this Thesis for approval on 2025-05-07 at 17:09.","This Thesis was approved for publication on 2025-05-08 at 14:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22258 on 2025-10-19 at 18:13:49","Artificial intelligence (AI) has been widely adopted due to its advanced capabilities, finding applications in autonomous vehicles and, more recently, in large language models (LLMs) powered by transformers. As AI systems continue to expand in scope and influence, ensuring their reliability and robustness becomes increasingly critical, particularly in unforeseen scenarios where failures can have significant consequences. Furthermore, given the substantial computational and economic costs associated with training and deploying large-scale models, which often require extensive GPU clusters, the underlying infrastructure must be highly fault-tolerant to maintain efficiency and stability. This thesis presents our work on iPrism, a framework designed to enhance the safety of autonomous vehicles by reducing collision rates through AI-driven risk assessment and mitigation. This thesis also discusses the work Characterizing GPU Resilience and Impact on AI/HPC Systems, which examines GPU failure patterns in the Delta Supercomputer to improve system reliability."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Enhancing safety and resilience in AI-driven systems: from autonomous vehicles to data centers"]}]}],"canonical_facts":{"dc:contributor":["Iyer, Ravishankar K."],"dc:creator":["Chen, Ziheng"],"dc:date":["2025-05-08","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Ziheng Chen, accepted the attached license on 2025-05-07 at 16:54.","The student, Ziheng Chen, submitted this Thesis for approval on 2025-05-07 at 17:09.","This Thesis was approved for publication on 2025-05-08 at 14:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22258 on 2025-10-19 at 18:13:49","Artificial intelligence (AI) has been widely adopted due to its advanced capabilities, finding applications in autonomous vehicles and, more recently, in large language models (LLMs) powered by transformers. As AI systems continue to expand in scope and influence, ensuring their reliability and robustness becomes increasingly critical, particularly in unforeseen scenarios where failures can have significant consequences. Furthermore, given the substantial computational and economic costs associated with training and deploying large-scale models, which often require extensive GPU clusters, the underlying infrastructure must be highly fault-tolerant to maintain efficiency and stability. This thesis presents our work on iPrism, a framework designed to enhance the safety of autonomous vehicles by reducing collision rates through AI-driven risk assessment and mitigation. This thesis also discusses the work Characterizing GPU Resilience and Impact on AI/HPC Systems, which examines GPU failure patterns in the Delta Supercomputer to improve system reliability."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129342"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Ziheng Chen"],"dc:subject":["Dependable Systems","Autonomous Vehicles","Gpu Resilience","High-performance Computing"],"dc:title":["Enhancing safety and resilience in AI-driven systems: from autonomous vehicles to data centers"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}