Agenda

Rajarshi Guhaniyogi (Texas A&M University) - Bridging Statistical, Scientific and Artificial Intelligence: Trustworthy Deep Neural Networks for Complex Structured Data

Abstract:

The explosive expansion of large, structured datasets is radically transforming the landscape of statistical inference, unlocking unprecedented possibilities while simultaneously introducing formidable challenges. Although hierarchical Bayesian methods offer a gold standard for principled inference and rigorous uncertainty quantification, they falter in terms of scalability when confronted with the high dimensionality and sheer scale of modern data. Deep Neural Networks (DNNs) have made remarkable strides on the scalability front, yet their use in the literature remains largely as opaque, black-box that are ill-suited for inference, particularly with structured data. To overcome these barriers, we introduce DNN-based generative models, precisely engineered for two complex and impactful domains: (i) functional output regression with functional and network-valued inputs, and (ii) functional factor modeling for multivariate functional datasets. Our framework leverages the connection between variational deep Gaussian processes and DNNs, delivering transparent and interpretable inference via well-calibrated uncertainty quantification, and achieving rapid, Markov Chain Monte Carlo (MCMC)-free optimization for high-resolution, large-sample scenarios. We reveal how Bayesian nonparametric principles and cutting-edge deep learning can be seamlessly unified, positioning deep Gaussian process priors at the forefront for scalable generative modeling of object-valued data. Demonstrating the framework’s practical strength, we showcase compelling real-world applications, including image-on-image regression and large-scale remote sensing for the carbon-water cycle. This work is in collaboration with the postdoctoral scholar Dr. Yeseul Jeon, as well as scientists from the UC San Francisco Medical School.

The seminar can be attended also remotely, connecting to ZOOM.

Link Zoom: bit.ly/insem-2425
ID riunione:  880 2639 9452
Passcode: InSem-2425