{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/19834"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/19834","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"Adaptive, Federated, and Resilient Health Monitoring via Novel Online Collaborative Learning Algorithms","abstract":"Adaptive monitoring of a large population of dynamic processes is critical for the timely detection of abnormal events under limited resources. This issue is pervasive in various sectors, including healthcare and engineering systems, due to the disparity between available monitoring resources and the large population of units, and the uncertain and heterogeneous dynamics of unit progression. To effectively address this problem, in this dissertation, we introduce advanced methodologies for designing adaptive monitoring strategies. We first develop an online collaborative learning framework that efficiently models and monitors a population of dependent units under resource constraints. We then develop a decentralized online collaborative framework that enables online modeling and monitoring of units with latent dynamics while preserving data privacy. Finally, we develop a novel robust online multi-task learning algorithm designed to capture latent structures inherent in the population from sequentially observed data under corruption. We have demonstrated the effectiveness of the proposed methods through rigorously proven theoretical analysis and experiments, including simulation studies and real-world world applications including cognitive degradation monitoring in Alzheimer’s Disease (AD) and battery degradation monitoring.","abstract_html":"Adaptive monitoring of a large population of dynamic processes is critical for the timely detection of abnormal events under limited resources. This issue is pervasive in various sectors, including healthcare and engineering systems, due to the disparity between available monitoring resources and the large population of units, and the uncertain and heterogeneous dynamics of unit progression. To effectively address this problem, in this dissertation, we introduce advanced methodologies for designing adaptive monitoring strategies. We first develop an online collaborative learning framework that efficiently models and monitors a population of dependent units under resource constraints. We then develop a decentralized online collaborative framework that enables online modeling and monitoring of units with latent dynamics while preserving data privacy. Finally, we develop a novel robust online multi-task learning algorithm designed to capture latent structures inherent in the population from sequentially observed data under corruption. We have demonstrated the effectiveness of the proposed methods through rigorously proven theoretical analysis and experiments, including simulation studies and real-world world applications including cognitive degradation monitoring in Alzheimer’s Disease (AD) and battery degradation monitoring.","abstract_has_math":false,"creators":["Kosolwattana, Tanapol 1996-"],"institution":"University of Houston","degree_name":"Doctor of Philosophy","degree_level":null,"degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Lin, Ying"],"committee_chairs":[],"committee_members":["Wang, Huazheng","Hu, Renjie","Feng, Qianmei","Lim, Gino"],"year":2025,"date_issued":"2025-05","date_published":"2025-05","updated_at":"2026-07-24T02:32:27Z","subjects":["Industrial engineering"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10657/19834","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lin, Ying"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Wang, Huazheng","Hu, Renjie","Feng, Qianmei","Lim, Gino"]},{"key":"dc:creator","label":"Author","values":["Kosolwattana, Tanapol 1996-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-22T18:29:28Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Industrial engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10657/19834"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Adaptive monitoring of a large population of dynamic processes is critical for the timely detection of abnormal events under limited resources. This issue is pervasive in various sectors, including healthcare and engineering systems, due to the disparity between available monitoring resources and the large population of units, and the uncertain and heterogeneous dynamics of unit progression. To effectively address this problem, in this dissertation, we introduce advanced methodologies for designing adaptive monitoring strategies. We first develop an online collaborative learning framework that efficiently models and monitors a population of dependent units under resource constraints. We then develop a decentralized online collaborative framework that enables online modeling and monitoring of units with latent dynamics while preserving data privacy. Finally, we develop a novel robust online multi-task learning algorithm designed to capture latent structures inherent in the population from sequentially observed data under corruption. We have demonstrated the effectiveness of the proposed methods through rigorously proven theoretical analysis and experiments, including simulation studies and real-world world applications including cognitive degradation monitoring in Alzheimer’s Disease (AD) and battery degradation monitoring."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Adaptive, Federated, and Resilient Health Monitoring via Novel Online Collaborative Learning Algorithms"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lin, Ying"],"dc:contributor.committeemember":["Wang, Huazheng","Hu, Renjie","Feng, Qianmei","Lim, Gino"],"dc:creator":["Kosolwattana, Tanapol 1996-"],"dc:date.accessioned":["2025-07-22T18:29:28Z"],"dc:date.issued":["2025-05"],"dc:description.abstract":["Adaptive monitoring of a large population of dynamic processes is critical for the timely detection of abnormal events under limited resources. This issue is pervasive in various sectors, including healthcare and engineering systems, due to the disparity between available monitoring resources and the large population of units, and the uncertain and heterogeneous dynamics of unit progression. To effectively address this problem, in this dissertation, we introduce advanced methodologies for designing adaptive monitoring strategies. We first develop an online collaborative learning framework that efficiently models and monitors a population of dependent units under resource constraints. We then develop a decentralized online collaborative framework that enables online modeling and monitoring of units with latent dynamics while preserving data privacy. Finally, we develop a novel robust online multi-task learning algorithm designed to capture latent structures inherent in the population from sequentially observed data under corruption. We have demonstrated the effectiveness of the proposed methods through rigorously proven theoretical analysis and experiments, including simulation studies and real-world world applications including cognitive degradation monitoring in Alzheimer’s Disease (AD) and battery degradation monitoring."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10657/19834"],"dc:language.iso":["English"],"dc:subject":["Industrial engineering"],"dc:title":["Adaptive, Federated, and Resilient Health Monitoring via Novel Online Collaborative Learning Algorithms"],"dc:type":["Thesis"],"thesis:degree_discipline":["Industrial Engineering"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:32:27Z"}