The Center for Generative AI is excited to launch the Fall 2024 AI Health Invited Talk Series. The seminars will take place on Thursdays at 12pm at Dell Medical School and will consist of a 30 minute hybrid Zoom talk followed by a 30 minute in-person lunch discussion (lunch will be provided).

The goal of the lunch discussion is to connect computer scientists and engineers with clinicians to tackle actionable AI/Health projects. We ask that you kindly fill out the RSVP form if you are interested in attending the in-person lunch discussion. While the talk is open to everyone, due to limited space the in-person lunch will be provided to those who RSVP under a first-come, first-serve basis until we reach capacity.

RSVP here to attend in person.

Zoom link to join virtually: https://utexas.zoom.us/j/5128555388 

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Fall 2024 Speakers &  Locations:
Sept 19: Carl Yang, Emory | HDB 1.202
Oct 3: Tianlong Chen, UNC | HDB 1.204
Oct 17: Akshay Chaudhari, Stanford | HLB 1.111 Auditorium
Oct 31: Grett Durrett, UT | HDB 1.202
Nov 14: Ziyue XU, Nvidia Health | HDB 1.202

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This Week's Speaker: TZiyue XU, Nvidia Health 

Title: Data Federation and Synthesis: Possibilities in the Age of GenAI

Abstract: In the ever-evolving landscape of artificial intelligence, handling and leveraging data effectively has been and will continue to be a critical challenge, especially in the age of GenAI. Recent development in utilizing them, e.g. large language models (LLMs), has opened new horizons in the research. Although most algorithms are trained in a centralized fashion, access to necessary data can be restricted due to various factors such as privacy, regulation, geopolitics, and the sheer effort to move the datasets. Such restrictions are especially prominent for healthcare applications. Two potential answers are data federation, and synthesis, addressing the pivotal balance between data access and the collaborative enhancement of AI models. In this talk, we explore how federated learning can address the data sharing challenges with easy and scalable integration capabilities, and how realistic medical image synthesis can be effectively achieved and deployed. Enabled by practical frameworks like NVIDIA FLARE and NIM, we will discuss the special challenges and solutions for embedding federation in GenAI model development and synthesis to enhance its accuracy and robustness. Ultimately, this talk underscores the transformative potential of data federation and synthesis in GenAI models, offering insights into its current achievements and future possibilities.

Speaker Bio: Ziyue Xu is a Senior Scientist at NVIDIA, before which he was a Staff Scientist and Lab Manager at National Institutes of Health, USA. His research interests lie in the area of image analysis and computer vision with applications in biomedical imaging. He has been working on collaborative medical AI development over the years along with fellow researchers and clinicians. He is an IEEE Senior Member, Area Chair for major conferences, and Associate Editor for several journals including IEEE Transactions of Medical Imaging, and International Journal of Computer Vision.