{"672395":{"#nid":"672395","#data":{"type":"event","title":"ISyE Seminar - Jonathan Stallrich","body":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003ETitle: \u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EStatistical Methods for $mall Data Problems\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract: \u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EWith data collection, storage, and management becoming faster and cheaper, statisticians and data scientists have developed important statistical and computational methods for Big Data problems. However, there are still many significant problems that involve small quantities of data that are \u201cbig\u201d in terms of monetary or temporal cost. These $mall (small) data problems are best solved with careful planning of both the data collection and analysis methods. In this talk, I will discuss some examples of these problems and new statistical methods to tackle them. The talk will primarily focus on new screening experiments that jointly target efficient estimation of both the main effects of the manipulated factors and the process variance. I will then survey new ideas for problems that have received less attention in the literature, including optimal designs for penalized estimation, screening designs for generalized linear models, and design and analysis for screening under functional linear models.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EBio:\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EJon Stallrich is an Associate Professor in the Department of Statistics at NC State University. He earned his Ph.D. in Statistics from Virginia Tech in 2014. \u003Cspan\u003EHe has focused his career on innovating and teaching practical statistical methods under the tenet that data collection and analysis procedures should be jointly determined to make efficient statistical conclusions. \u003C\/span\u003EJon\u2019s research interests include design and analysis of screening experiments, computer experiments, functional data analysis, and variable selection. \u003Cspan\u003EHis methodological research is often motivated by interdisciplinary collaborations with researchers from industrial and systems engineering, biomedical engineering, material science, toxicology, and computer science.\u003C\/span\u003E In 2021, he and his coauthors were awarded the ASA\u2019s Statistics in Physical Engineering Sciences Award for the paper, \u201cOptimal EMG placement for a robotic prosthesis controller with sequential, adaptive functional estimation.\u201d He is currently serving as Chair of the ASA\u2019s Section on Physical and Engineering Sciences and General Conference Chair of the 2024 Fall Technical Conference to be held in Nashville, TN.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract: \u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EWith data collection, storage, and management becoming faster and cheaper, statisticians and data scientists have developed important statistical and computational methods for Big Data problems. However, there are still many significant problems that involve small quantities of data that are \u201cbig\u201d in terms of monetary or temporal cost. These $mall (small) data problems are best solved with careful planning of both the data collection and analysis methods. In this talk, I will discuss some examples of these problems and new statistical methods to tackle them. The talk will primarily focus on new screening experiments that jointly target efficient estimation of both the main effects of the manipulated factors and the process variance. I will then survey new ideas for problems that have received less attention in the literature, including optimal designs for penalized estimation, screening designs for generalized linear models, and design and analysis for screening under functional linear models.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Statistical Methods for $mall Data Problems"}],"uid":"34977","created_gmt":"2024-01-23 19:56:20","changed_gmt":"2024-01-23 19:56:20","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-02-06T11:00:00-05:00","event_time_end":"2024-02-06T12:00:00-05:00","event_time_end_last":"2024-02-06T12:00:00-05:00","gmt_time_start":"2024-02-06 16:00:00","gmt_time_end":"2024-02-06 17:00:00","gmt_time_end_last":"2024-02-06 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}