{"680845":{"#nid":"680845","#data":{"type":"event","title":"PhD Defense by Jie Wang","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp;Reliable Decision-Making Under Uncertainty Through the Lens of Statistics and Optimization\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E\u0026nbsp;April 7th, 2025\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E\u0026nbsp;2:00pm \u2013 4:00pm\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E\u0026nbsp;Groseclose 303\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EZoom Meeting Link:\u0026nbsp;\u003C\/strong\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/7377156804?pwd=FaalZkHqEWyzRJVayUUNVeBpOFtOWq.1\u0026amp;omn=98775438085\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/7377156804?pwd=FaalZkHqEWyzRJVayUUNVeBpOFtOWq.1\u0026amp;omn=98775438085\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EJie Wang\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EIndustrial Engineering PhD Candidate\u003C\/p\u003E\u003Cp\u003EH. Milton Stewart School of Industrial and Systems Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. Yao Xie (Advisor),\u0026nbsp;H. Milton Stewart School of Industrial and Systems Engineering\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Xin Chen, H. Milton Stewart School of Industrial and Systems Engineering\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. George Lan, H. Milton Stewart School of Industrial and Systems Engineering\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Alexander Shapiro, H. Milton Stewart School of Industrial and Systems Engineering\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Rui Gao, Department of Information, Risk, and Operations Management at the McCombs School of Business at the University of Texas at Austin\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp;In this thesis, we develop computationally efficient algorithms with statistical guarantees for problems of decision-making under uncertainty, particularly in the presence of large-scale, noisy, and high-dimensional data. In Chapter 2, we propose a kernelized projected Wasserstein distance for high-dimensional hypothesis testing, which finds the nonlinear mapping that maximizes the discrepancy between projected distributions. In Chapter 3, we provide an in-depth analysis of the computational and statistical guarantees of the kernelized projected Wasserstein distance. In Chapter 4, we study the variable selection problem in two-sample testing, aiming to select the most informative variables to determine whether two datasets follow the same distribution. In Chapter 5, we present a novel framework for distributionally robust stochastic optimization (DRO), which seeks an optimal decision that minimizes expected loss under the worst-case distribution within a specified set. This worst-case distribution is modeled using a variant of the Wasserstein distance based on entropic regularization. In Chapter 6, we incorporate Phi-divergence regularization into the infinity-type Wasserstein DRO, which is a formulation particularly useful for adversarial machine learning tasks. Chapter 7 concludes with an overview of future research directions.\u00ad\u00ad\u00ad\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EReliable Decision-Making Under Uncertainty Through the Lens of Statistics and Optimization\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Reliable Decision-Making Under Uncertainty Through the Lens of Statistics and Optimization"}],"uid":"27707","created_gmt":"2025-03-03 17:12:58","changed_gmt":"2025-03-03 17:13:53","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-07T14:00:00-04:00","event_time_end":"2025-04-07T16:00:00-04:00","event_time_end_last":"2025-04-07T16:00:00-04:00","gmt_time_start":"2025-04-07 18:00:00","gmt_time_end":"2025-04-07 20:00:00","gmt_time_end_last":"2025-04-07 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 303","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":""}}}