UPCOMING TALKS


April 9, 2026: 

Title: "Adaptive Radiation Therapy at Scale: Clinical Necessity or Selective Application?"

Speaker: Ergys D. Subashi, Ph.D., Associate Professor, Department of Radiation Physics, MD Anderson Cancer Center

Join Zoom Meeting
https://utexas.zoom.us/j/87121024650?pwd=kkV0qG3NF7BkeuOHL7bHWeIO4nB0Uv.1

Meeting ID: 871 2102 4650
Passcode: 517642

Abstract: Adaptive radiation therapy improves treatment precision by accounting for geometric and functional changes in patient anatomy, enabling better target coverage and sparing of normal tissue. However, routine adaptation is challenging due to workflow complexity, time constraints, staffing demands, and stringent quality assurance requirements. This talk reviews our work on implementation of adaptive MR-guided radiotherapy and discusses how AI-driven tools—such as auto-contouring, rapid planning, and decision support—can streamline workflows to support a safe and scalable implementation of daily adaptive radiotherapy.

Bio: Dr. Ergys D. Subashi is an associate professor in the Department of Radiation Physics at MD Anderson Cancer Center. His work is focused on MRI-guided adaptive radiation therapy, with an emphasis on developing imaging methods that improve tumor delineation, treatment planning, and real-time monitoring of anatomical changes during therapy. His research includes techniques that characterize motion and functional heterogeneity in tumors, enabling more precise and individualized radiation therapy. In his clinical role, Dr/ Subashi focuses on treatment planning, workflow optimization, and quality assurance for MR-linac systems, addressing uncertainties related to patient motion, imaging performance, and treatment delivery. His work aims to enhance the safety, accuracy, and robustness of MRI-guided radiotherapy, particularly in anatomically complex regions such as the abdomen and pelvis.



April 23, 2026: 

Title: "Efficient Medical Image Segmentation Across the Pipeline"
 

Speaker:  Radu Marculescu, Ph.D., Professor, Department Electrical and Computer Engineering, UT Austin

Join Zoom Meeting
https://utexas.zoom.us/j/87121024650?pwd=kkV0qG3NF7BkeuOHL7bHWeIO4nB0Uv.1

Meeting ID: 871 2102 4650
Passcode: 517642

Abstract:  Medical image segmentation is a central problem in AI for health, but improvements in accuracy often come with growing computational and memory costs. This talk presents a recent line of work on efficient segmentation across the pipeline, showing how efficiency can be built directly into decoding, 3D network design, and multi-scale prediction fusion. Rather than treating efficiency as a post hoc constraint or compression step, I will argue that it can serve as a core design principle. By rethinking where computation is most valuable, we can build segmentation models that remain accurate while also improving scalability, inference speed, and practical deployability across a range of real-world medical imaging settings.

Bio: Radu Marculescu is a Professor and the Laura Jennings Turner Chair in Engineering in the Department of Electrical and Computer Engineering at The University of Texas at Austin. Between 2000-2019, he was a Professor in the Electrical and Computer Engineering department at Carnegie Mellon University. His current research focuses on developing AI/ML algorithms for computer vision, bioimaging, and Internet-of-Things (IoT) applications. He is an IEEE Fellow, an ACM Fellow, and an AAAS Fellow. More info available here: http://radum.ece.utexas.edu.
 

PAST TALKS:

2026

AIHealthTalk: 03/26/26 - "Trustworthy Health AI: Challenges & Lessons Learned"
 

AIHealthTalk: 03/12/26 - "Generative magnetic resonance multitasking: patient-specific AI models for high-dimensional imaging"
 

AIHealthTalk: 02/26/26 - "Real-World Data to Real-World Evidence with some AI: Successes, Challenges, and Opportunities"
 

AIHealthTalk: 02/12/26 - "Knowledge-Informed Weakly-Supervised Deep Learning Models for Cancer Applications" 
 

AIHealthTalk: 01/29/26 - "Enhancing GI Tract Cancer Diagnosis Through Generative Models and Vision-based Robotic Tactile Sensing" 
 

2025 

AIHealthTalk: 11/06/25 - Semantics in Medicine: Expert, Data, and Application Perspectives

AIHealth Talk: 10/23/25 - Predicting Long Term Mortality in COPD Using Deep Learning Imaging Markers

AIHealthTalk:10/9/25 - Using Large Language Models to Simulate Patients for Training Mental Health

 AIHealthTalk: 09/25/25 - PanEcho: Toward Complete Al-Enabled Echocardiography Interpretation

AIHealthTalk: 09/11/25 - Clinical Deployment of AI:From Single Models to Compound Agentic Systems

April 10, 2025: Na Zou, Assistant Professor, University of Houston
Exploring and Exploiting Fairness in AI/ML: Algorithms and Applications

April 24, 2025: Edison Thomaz, Associate Professor and William H. Hartwig Fellow, Electrical and Computer Engineering, UT Austin
Identifying Digital Biomarkers of Cognitive Impairment from Real World Activity Data


Past Talks: 

Fall 2024

Nov. 14: Ziyue Xu, NVIDIA Health
Flexible Modality Learning: Modeling Arbitrary Modality Combination via the Mixture-of-Experts Framework

Oct 31: Greg Durrett, Associate Professor, The University of Texas at Austin
Specializing LLMs for Factuality and Soft Reasoning

Oct 17: Akshay Chaudhari, Stanford University
Towards Multi-modal Foundation Models for 3D Medical Imaging

Oct 3: Tianlong Chen, UNC 

Sept 19: Carl Yang, Assistant Professor of Computer Science, Emory University
KG-LLM Co-Learning for Health