{"689467":{"#nid":"689467","#data":{"type":"event","title":"PhD Defense by Yuhao Wang","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E \u003Cem\u003ESimulation-Based Decision Making with Streaming Data\u003C\/em\u003E\u003Cbr\u003E\u003Cstrong\u003ECandidate:\u003C\/strong\u003E\u0026nbsp;Yuhao Wang\u003Cbr\u003E\u003Cstrong\u003EAffiliation:\u003C\/strong\u003E\u0026nbsp;H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate and Time:\u003C\/strong\u003E\u0026nbsp;Friday, April 17, 2026, 9:00 AM \u2013 11:00 AM\u003Cbr\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E\u0026nbsp;Groseclose 404\u003Cbr\u003E\u003Cstrong\u003EMicrosoft Teams:\u003C\/strong\u003E \u003Ca href=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_N2E3MjIxZGYtOTk4MS00MzhjLWI4YjctODA5OGM5YjdiZjFk%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%2205b99bdc-ec60-4153-8a6d-ce83fbf47cd7%22%7d\u0022\u003Ehttps:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_N2E3MjIxZGYtOTk4MS00MzhjLWI4YjctODA5OGM5YjdiZjFk%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%2205b99bdc-ec60-4153-8a6d-ce83fbf47cd7%22%7d\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThesis Committee\u003C\/strong\u003E\u003Cbr\u003EDr. Enlu Zhou (advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology\u003Cbr\u003EDr. Seong-Hee Kim, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology\u003Cbr\u003EDr. Eunhye Song, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology\u003Cbr\u003EDr. Guanghui Lan, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology\u003Cbr\u003EDr. David Eckman, Industrial and Systems Engineering, Texas A\u0026amp;M University\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EThis dissertation studies simulation-based decision making in the presence of streaming data and model uncertainty. The central objective is to develop methodologies that integrate simulation, optimization, and learning in dynamic environments where data arrive sequentially over time.\u003C\/p\u003E\u003Cp\u003EThe \u003Cstrong\u003Efirst part\u003C\/strong\u003E\u0026nbsp;of the dissertation studies the ranking and selection problem under input uncertainty with streaming data. A data-driven framework is developed to simultaneously allocate resources to collect input data and conduct simulation experiments, while providing statistical guarantees on selection performance.\u003C\/p\u003E\u003Cp\u003EThe \u003Cstrong\u003Esecond part\u003C\/strong\u003E\u0026nbsp;focuses on simulation-based bilevel optimization problems, where each alternative system involves an internal continuous optimization problem. Both multi-stage and fully sequential procedures are proposed to integrate pruning of suboptimal systems with optimization of decision variables, significantly improving computational efficiency.\u003C\/p\u003E\u003Cp\u003EThe \u003Cstrong\u003Ethird part\u003C\/strong\u003E\u0026nbsp;investigates reinforcement learning under model uncertainty through a Bayesian risk framework. A Bayesian risk-averse formulation of Markov decision processes is developed, together with a Q-learning algorithm that updates posterior beliefs using streaming observations.\u003C\/p\u003E\u003Cp\u003EThe \u003Cstrong\u003Efinal part\u003C\/strong\u003E\u0026nbsp;extends this framework to online reinforcement learning, where policies are adaptively updated as new data arrive. The proposed methods achieve sublinear regret and demonstrate how Bayesian risk formulations naturally adjust the degree of risk aversion over time.\u003C\/p\u003E\u003Cp\u003EOverall, this dissertation contributes flexible and adaptive methodologies for simulation-based decision making in modern data-rich stochastic systems.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ESimulation-Based Decision Making with Streaming Data\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Simulation-Based Decision Making with Streaming Data"}],"uid":"27707","created_gmt":"2026-04-06 12:34:22","changed_gmt":"2026-04-06 12:34:55","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-04-17T09:00:00-04:00","event_time_end":"2026-04-17T11:00:00-04:00","event_time_end_last":"2026-04-17T11:00:00-04:00","gmt_time_start":"2026-04-17 13:00:00","gmt_time_end":"2026-04-17 15:00:00","gmt_time_end_last":"2026-04-17 15:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 404","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}