CIMBID Seminar

Understanding Mental-Physical Comorbidity via Integrated Modeling of Brain-Body Axis

Ye Ella Tian, PhD
NHMRC Emerging Leadership Fellow
Senior Leadership Fellow
Department of Psychiatry
The University of Melbourne

Personalizing Interventions Through Imaging-based Digital Twins

Thomas Yankeelov, PhD
Professor, Director of Center for Computational Oncology
W. A. "Tex" Moncrief, Jr. Chair in Computational Engineering and Sciences IV - Computational Oncology
University of Texas at Austin
Cockrell School of Engineering

Our lab focuses on developing tumor forecasting methods by integrating medical imaging data with biology-based, mathematical models to predict tumor growth and treatment response on an individual patient basis. These data are acquired before therapy begins, and again at one or more time points during therapy. All model parameters that are not directly measured are calibrated to the longitudinal imaging data from a patient. Then, the personalized model is run forward to predict tumor size and other characteristics of the patient’s tumor at future times which can be directly tested against observation and, ultimately, guide intervention. In this presentation, we will provide an overview of this effort through four vignettes focusing on 1) incorporating patient-specific data into biology-based mathematical models, 2) simulating outcomes via digital twins, 3) guiding interventions through optimal control theory, and 4) updating interventions through data assimilation. To tell a complete story we will focus on breast cancer, but also summarize our efforts in brain, cervical, and prostate cancer.

Artificial Intelligence and Computational Imaging: Opportunities for Precision Medicine in Oncology and Neurological Disorders 

Pallavi Tiwari, PhD
Associate Professor
Departments of Radiology & Biomedical Engineering
University of Wisconsin, Madison
Co-Director, Imaging & Radiation Sciences Program
Carbone Cancer Center 

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Medical Image Segmentation and Synthesis

Ipek Oguz, PhD
Associate Professor
Department of Computer Science
Vanderbilt University 

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Collaborative Medical Vision for Precision Medicine

Prateek Prasanna, PhD
Assistant Professor
Department of Biomedical Informatics
Stony Brook University 

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Reflection on Precision Medicine in the Era of AI/ML: Reproducibility, Disease Heterogeneity, and Multi-Scale Modeling

Junhao (Hao) Wen, PhD
Assistant Professor and PI
LABS at the University of Southern California 

Abstract: Dr. Wen’s research endeavors focus on developing and applying artificial intelligence and machine learning (AI/ML) techniques to analyze multi-organ, multi-omics biomedical data for studying human aging and disease. This talk encompasses three intertwined yet progressive perspectives: i) scrutinizing the reproducibility of AI/ML in neuroimaging research; ii) depicting the neuroanatomical heterogeneity of brain disorders using AI/ML and imaging; and iii) embracing multi-scale (organs and omics) approaches to investigate human aging and disease beyond the brain. Integrating AI-driven decision support systems into clinical settings to identify potential genetic, proteomic, metabolomics, and imaging biomarkers for future therapeutic interventions is central to his research interests. 

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