CIMBID Seminar
The CIMBID seminar series features both internal and external faculty speakers, industry professionals, and broader experts to deliver lectures on topics related to CIMBID’s mission and research innovation. CIMBID seminars are offered with both in-person and virtual options and are open to the Columbia community.
Upcoming CIMBID Seminars
October 23, 2026 - Gagandeep Singh, MD
"Beyond the Single Scan: From Generative Imaging and Foundation Models to Patient-Specific Digital Twins"
November 20, 2026 - Yading Yuan, PhD, DABR
"Toward Scalable and Generalizable AI for Medical Image Analysis: From Federated Learning to Probabilistic Representation Learning"
December 18, 2026 - Shalmali Joshi, PhD
"Multimodal Reasoning in Radiology: Application to Chest Radiograph-Based Diagnostics and Report Generation"
Past Seminars
Watch our past lectures with the video link provided.
September 25, 2026 - Keenan Walker, PhD
Connecting Proteomics with Neuroimaging to Decode Brain Aging and Dementia
Keenan Walker, PhD
Senior Investigator
National Institute on Aging (NIA), Intramural Research Program (IRP)
National Institutes of Health
Abstract: Plasma proteomics provides a scalable window into systemic biology, while neuroimaging captures structural, vascular, and neurodegenerative changes in the brain. In this talk, I will describe how large-scale plasma proteomic profiling can be integrated with longitudinal neuroimaging, cognition, genetics, and cerebrospinal fluid biomarkers to identify proteins and pathways associated with brain aging and dementia risk. Following initially cognitively normal individuals over time can reveal molecular signals that precede imaging changes and cognitive impairment, distinguish early disease processes from downstream consequences, and clarify heterogeneity in progression. These approaches may improve biologically informed risk prediction and identify new targets for preventing and treating age-related brain disease.
May 5, 2025 - Ye Ella Tian, PhD
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
March 20, 2025 - Thomas Yankeelov, PhD
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.
November 7, 2024 - Pallavi Tiwari, PhD
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
September 25, 2024 - Ipek Oguz, PhD
Medical Image Segmentation and Synthesis
Ipek Oguz, PhD
Associate Professor
Department of Computer Science
Vanderbilt University
September 10, 2024 - Prateek Prasanna, PhD
Collaborative Medical Vision for Precision Medicine
Prateek Prasanna, PhD
Assistant Professor
Department of Biomedical Informatics
Stony Brook University
July 31, 2024 - Junhao Wen, PhD
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.