Abstract:
In 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 “knowledge-infused statistical machine learning.” 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.
In 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.
Bio:
Dr. Jing Li is an Associate Professor in Industrial Engineering & Computer Engineering at Arizona State University (https://www.public.asu.edu/~jli09/). 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. Dr. Li’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 IISE Transactions, 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 Quality Technology and Quantitative Management, an Associate Editor for IEEE Transactions on Automation Science and Engineering, an Associate Editor for IISE Transactions on Healthcare Systems Engineering, and on the editorial board of Journal of Quality Technology.
]]>Abstract:
In 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 “knowledge-infused statistical machine learning.” 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.
In 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.
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