Welcome to TrAC Day! Hosted by The Translational AI Center When: Wednesday, April 3rdTime: 3:00 p.m.Where: Student Innovation Center, Iowa State University About TrAC Day The Translational AI Center is thrilled to announce our inaugural TrAC Day, a pioneering event that celebrates the intersection of artificial intelligence, research, and industry collaboration. What to Expect Join […]
TrAC ADLRG Mini-course on Generative AI (class 3/4) In this mini-course, we will cover concepts in Generative AI such as Diffusion, etc.
Title: ∇-Prox: Differentiable Proximal Algorithm Modeling for Large-Scale Optimization Link to the paper: https://light.princeton.edu/publication/delta_prox/
TrAC DLRG Mini-course on MLOps (class 2/3) In this mini-course, we will cover DevOps and best practices for training, monitoring, and deploying deep learning models.
Title: A Graph Neural Network Surrogate for Epitaxial Crystal Growth Abstract Predicting grain (crystal) formation and growth during alloy solidification is of great importance in additive manufacturing (AM) as it one of the key factors in determining the final microstructure and the mechanical properties of the printed part. Numerical simulations of grain formation involve moving […]
TrAC ADLRG Mini-course on Generative AI (class 4/4) In this mini-course, we will cover concepts in Generative AI such as Diffusion, etc.
Title: Hyperspectral Neural Radiance Fields
TrAC DLRG Mini-course on MLOps (class 3/3) In this mini-course, we will cover DevOps and best practices for training, monitoring, and deploying deep learning models.
Title: Living Scenes: Creating and updating digital twins of evolving indoor scenes Abstract Buildings are like living organisms, i.e., they evolve over time due to interaction with natural phenomena and humans. How can we realistically maintain their digital twins throughout their lifespan? Or else, how can we maintain a living building model as the space is […]
TrAC ADLRG Mini-course on Federated Learning (class 1/3) In this mini-course, we will cover concepts in Federated Learning.
