NCI Data Jamboree (Project Abstract Submission): Submission #45

Submission information
Submission Number: 45
Submission ID: 189111
Submission UUID: 8294eab2-67ff-47c1-9bd9-095ab4dd6f63

Created: Mon, 07/27/2026 - 13:01
Completed: Mon, 07/27/2026 - 13:05
Changed: Mon, 07/27/2026 - 13:05

Remote IP address: 10.208.28.116
Submitted by: Anonymous
Language: English

Is draft: No
First Name Hairong
Middle Initial
Last Name Wang
Degree(s) Ph.D.
Position/Title/Career Status Assistant Professor
Organization the University of Texas at Austin
Organization Address Austin
Email hairong@utexas.edu
List of Additional Authors
Abstract Category Developing, refining, or validating tools, methods, algorithms, and pipelines
Abstract Keywords
Abstract Title Project Seeker Statement: Medical Imaging AI, Multimodal Learning, and Longitudinal Cancer Modeling
Abstract I am an Assistant Professor in Operations Research and Industrial Engineering at The University of Texas at Austin. My research focuses on artificial intelligence and machine learning for cancer research, particularly medical imaging, multimodal data integration, longitudinal disease modeling, and clinically informed prediction. I have worked on projects involving glioblastoma, liver cancer, pediatric brain tumors, and radiogenomic modeling. My expertise includes deep learning, generative modeling, uncertainty quantification, model evaluation, and the design of clinically meaningful AI studies.

I am interested in joining projects involving medical imaging, longitudinal or multimodal cancer data, data integration, and the evaluation of data-driven methods using publicly available cancer datasets. I can contribute to scientific question formulation, study design, model and evaluation strategy, interpretation of imaging and clinical endpoints, and hands-on computational analysis as needed.

I hope to learn more about NCI-supported data resources and data-sharing infrastructure, collaborate with researchers from complementary backgrounds, and contribute to a focused project that can produce a reproducible analysis, prototype, or framework during the jamboree. I would also be interested in continuing productive collaborations after the event when appropriate.