We are partnering with Podcraftr to explore using AI to reinterpret our monthly newsletter. This is the November 2025 edition of the Vanderbilt Lab of Immersive AI Translation newsletter, podcast-style. Feedback welcome. Our current newsletter and recent news are can be found here: https://www.vanderbilt.edu/valiant/in-the-news/

This is our monthly newsletter produced in partnership with Podcraftr.

This Fall we are teaming up with Podcraftr to explore AI podcasting. In Spring 2026, our ES3890 Studio in AI Course will explore how to actually build AI systems to transform information into new experiences.

Can deep learning help in the fight against emerging infectious diseases? In this episode of VALIANT Pulse, we explore the collaborative work led by Vanderbilt University, the NIH, and the Institut National de Recherche Biomédicale to automate mpox lesion counting using AI. Building on field data from the Democratic Republic of the Congo, the team compares leading segmentation models—including UNet++, Mask R-CNN, YOLOv8, and E2EC—to determine how well machines can count and classify skin lesions in challenging, real-world settings. Led by Dr. Eric Tkaczyk and Dr. Benoit Dawant, the project blends engineering and global health to support more accurate clinical trial endpoints and faster disease tracking. Opinions shared reflect scholarly perspectives and are not official policy.

In this episode of VALIANT Pulse, we explore a bold attempt to unify aspects unsupervised learning, network science, and imaging neuroscience through the lens of brain data. Mika Rubinov from Vanderbilt describes a series of analytical equivalences and unifications—connecting modularity and k-means, canonical covariance and co-clustering, diffusion maps and co-neighbor matrices—all with mathematical rigor and a nod to neuroscience's central patterns. These insights challenge how we analyze, interpret, and even discover structure in the brain, providing both a toolbox and a philosophical shift. Join us as we trace the common threads—and uncover surprising overlaps—across disciplines once thought distinct. The views expressed reflect an individual interpretation of the work, not official peer-reviewed consensus.

For more details: https://zenodo.org/records/15340856

In this episode of VALIANT Pulse, we feature the dissertation work of Can Cui at Vanderbilt, who pushes the frontier of machine learning in healthcare by tackling the complex challenge of multimodal data. From deep brain stimulation planning to cancer diagnosis with missing data, Cui’s work spans nine projects and modalities—from MRI and histopathology to genomics and free text. Central to this dissertation is the drive to make AI flexible, interpretable, and resilient—capable of thriving even when data is incomplete or labels are weak. Tune in to learn how multimodal fusion, prompt-guided segmentation, and self-supervised learning are changing the rules for clinical decision-making. The interpretations presented reflect scholarly insight, not peer-reviewed consensus or official clinical guidance.

In this episode of VALIANT Pulse, we feature the dissertation work of Thomas Z. Li, who takes on one of medicine’s most pressing challenges: how to predict and diagnose lung cancer with the data we already have. By integrating longitudinal chest CTs and multimodal electronic health records, Li develops a suite of AI models—transformers, joint embeddings, contrastive learning—to predict lung cancer diagnosis and long-term risk. From emphysema quantification to contrastive pretraining, his work explores how to make sense of asynchronous, sparse, and multimodal data with clinical rigor. Join us for an in-depth look at how machine learning might close the diagnostic gap for one of the world’s deadliest cancers. Opinions shared are individual interpretations and not peer-reviewed consensus.