{"630116":{"#nid":"630116","#data":{"type":"event","title":"ISyE Seminar - Jing Li","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle: Knowledge-infused statistical machine learning in modeling and inference of Medical Image Data\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn many areas of medicine, domain knowledge is available in the forms of bio-mechanistic models, human anatomy, and even descriptive statements. Although typically approximate and incomplete, this knowledge represents a wealth of cumulative human intelligence, which can be leveraged and integrated with data-driven learning algorithms for greater efficiency, interpretability, and robustness. My research develops modeling frameworks and associated estimation\/inference algorithms to integrate human intelligence and machine intelligence, which is called \u0026ldquo;knowledge-infused statistical machine learning.\u0026rdquo; The methodological developments are within the context of using medical imaging and other data to improve the characterization, diagnosis, and treatment of cancer and other diseases.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn this talk, I will introduce several new models and algorithms developed under this theme, driven by the need of improving cancer treatment precision to tackle not only inter- but also intra-tumor heterogeneity. I will present a modeling framework that integrates bio-mechanistic models with MRI and biopsy data to predict the spatial distribution of treatment-informed molecular markers within each tumor. Several extensions of this framework will also be presented, including an algorithm for simultaneous feature and instance selection and a Gaussian process model with knowledge regularization for uncertainty reduction. Furthermore, I will briefly talk about a few other medical domains where knowledge-infusion statistical machine learning has been investigated. I will end the talk by briefly going over my other research efforts and plans.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Jing Li is an Associate Professor in Industrial Engineering \u0026amp; Computer Engineering at Arizona State University (\u003Ca href=\u0022https:\/\/www.public.asu.edu\/~jli09\/\u0022\u003Ehttps:\/\/www.public.asu.edu\/~jli09\/\u003C\/a\u003E). She received her B.S. from Tsinghua University, and an M.A. in Statistics and a Ph.D. in Industrial and Operations Engineering from the University of Michigan. Her research interests are data fusion and statistical machine learning intersecting with health\/medical domains having complex data structures.\u0026nbsp; Dr. Li\u0026rsquo;s research is sponsored by NIH, NSF, DOD, Arizona State, Mayo Clinic, and biomedical industry. She co-founded the ASU-Mayo Clinic Center for Innovative Imaging, conducting various collaborative projects with the Departments of Radiology, Neurology, Neurosurgery, and Radiation Oncology at Mayo Clinic. She is an NSF CAREER awardee, a recipient of a Best Paper Award and a Best Application Paper Award from \u003Cem\u003EIISE Transactions\u003C\/em\u003E, a recipient of the Harold Wolff-John Graham Award (Best Paper) from the American Academy of Neurology, and a recipient of the Harold G. Wolff Lecture Award (Best Paper) by the American Headache Society. She is a former Chair for the Data Mining Subdivision of INFORMS. She is currently the Editor-in-Chief for \u003Cem\u003EQuality Technology and Quantitative Management\u003C\/em\u003E, an Associate Editor \u003Cem\u003Efor IEEE Transactions on Automation Science and Engineering\u003C\/em\u003E, an Associate Editor for \u003Cem\u003EIISE Transactions on Healthcare Systems Engineering\u003C\/em\u003E, and on the editorial board of \u003Cem\u003EJournal of Quality Technology\u003C\/em\u003E.\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle: Knowledge-infused statistical machine learning in modeling and inference of Medical Image Data\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn many areas of medicine, domain knowledge is available in the forms of bio-mechanistic models, human anatomy, and even descriptive statements. Although typically approximate and incomplete, this knowledge represents a wealth of cumulative human intelligence, which can be leveraged and integrated with data-driven learning algorithms for greater efficiency, interpretability, and robustness. My research develops modeling frameworks and associated estimation\/inference algorithms to integrate human intelligence and machine intelligence, which is called \u0026ldquo;knowledge-infused statistical machine learning.\u0026rdquo; The methodological developments are within the context of using medical imaging and other data to improve the characterization, diagnosis, and treatment of cancer and other diseases.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn this talk, I will introduce several new models and algorithms developed under this theme, driven by the need of improving cancer treatment precision to tackle not only inter- but also intra-tumor heterogeneity. I will present a modeling framework that integrates bio-mechanistic models with MRI and biopsy data to predict the spatial distribution of treatment-informed molecular markers within each tumor. Several extensions of this framework will also be presented, including an algorithm for simultaneous feature and instance selection and a Gaussian process model with knowledge regularization for uncertainty reduction. Furthermore, I will briefly talk about a few other medical domains where knowledge-infusion statistical machine learning has been investigated. I will end the talk by briefly going over my other research efforts and plans.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Knowledge-infused statistical machine learning in modeling and inference of Medical Image Data"}],"uid":"34868","created_gmt":"2019-12-17 13:38:35","changed_gmt":"2020-01-14 16:29:33","author":"sbryantturner3","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2020-01-22T11:00:00-05:00","event_time_end":"2020-01-22T12:00:00-05:00","event_time_end_last":"2020-01-22T12:00:00-05:00","gmt_time_start":"2020-01-22 16:00:00","gmt_time_end":"2020-01-22 17:00:00","gmt_time_end_last":"2020-01-22 17:00:00","rrule":null,"timezone":"America\/New_York"},"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":"78771","name":"Public"},{"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":""}}}