**Prof. Robert D. Nowak(University of Wisconsin-Madison)**

**Bio**:

Rob is the Grace Wahba Professor of Data Science and Keith and Jane Nosbusch Professor in Electrical and Computer Engineering at the University of Wisconsin-Madison. His research focuses on signal processing, machine learning, optimization, and statistics.

**Prof. Xiao Wang(Purdue University)**

**Title: Harnessing AI for Bayesian Inference: From Neural Conformal Inference to Neural Adaptive Empirical Bayes**

**Abs **:

The fusion of artificial intelligence and Bayesian inference has unlocked new possibilities for tackling complex statistical challenges. This talk delves into two AI-driven methodologies that advance Bayesian inference with neural network capabilities. The first, Neural Conformal Inference, offers a likelihood-free approach that maps observed data to model parameters using deep neural networks. By circumventing traditional discretization errors and integrating conformal prediction, this new framework ensures rigorous uncertainty quantification and reliable posterior coverage. The second approach, Neural Adaptive Empirical Bayes, constructs flexible priors using implicit generative models and combines this with variational inference to optimize hyperparameters directly from data. This adaptive strategy enhances predictive accuracy and provides comprehensive uncertainty measures, addressing the challenges of high-dimensional, complex data structures.

**Bio**:

**Education**

2005 Ph.D. in Statistics, University of Michigan, Ann Arbor, MI

2000 M.S. in Mathematics, University of Science and Technology of China

1997 B.S. in Mathematics, University of Science and Technology of China

**Research**

AI

Machine Learning

Nonparametric Statistics

Functional Data Analysis

Reliability

**Honors & Awards**

Professional Achievement Award, Purdue University, 2022-2023

Elected Fellow of the American Statistical Association (ASA) 2021

Elected Fellow of the Institute of Mathematical Statistics (IMS) 2021

Regina and Norman F. Caroll Research Award, Purdue University 2017- 2018

**Prof. Andrew Saxe(University College London)**

**Bio**:

Andrew Saxe is a Henry Dale Fellow and Joint Group Leader at the Gatsby Computational Neuroscience Unit and Sainsbury Wellcome Centre. He was previously an Associate Professor in the Department of Experimental Psychology at the University of Oxford. He completed a Swartz Postdoctoral Fellowship in Theoretical Neuroscience at Harvard University with Haim Sompolinsky, and completed his PhD in Electrical Engineering at Stanford University, advised by Jay McClelland, Surya Ganguli, Andrew Ng, and Christoph Schreiner. His dissertation received the Robert J. Glushko Dissertation Prize from the Cognitive Science Society. His research focuses on the theory of deep learning and its applications to phenomena in neuroscience and psychology. He was awarded a Sir Henry Dale Fellowship from the Wellcome Trust and Royal Society, and the Wellcome-Beit Prize. He is also a CIFAR Azrieli Global Scholar in the CIFAR Learning in Machines & Brains programme.

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**Bio**:

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