Title: Towards Third Wave AI: Interpretable, Robust Trustworthy Machine Learning for Diverse Applications in Science and Engineering Speaker: Guang Lin, Professor Departments of Mathematics...
Title: Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments Speaker: Youngdeok Hwang, Paul H. Chook Department of Information System and Statistics in the Zicklin...
Speaker: Sifan Liu, PHD, statistics department, Stanford University Title: Quasi-Monte Carlo Quasi-Newton for Variational Bayes Abstract: Many machine learning problems optimize an objective that must...
Speaker: Yuling Jiao, Professor, Wuhan University Title: Generative Learning and Unnormalized Sampling Abstract: Estimating underlying distributions from data and sampling from unnormalized densities...
Speaker: Yiwei Wang, Postdoc, Department of Applied Mathematics, Illinois Institute of Technology Title: Energetic Variational Inference Abstract: In this talk, we present a variational inference (VI)...
Speaker: Aleksei Sorokin, Ph.D., Illinois Institute of Technology Title: (Quasi-) Monte Carlo Importance Sampling with QMCPy Abstract: Aleksei is in his final semester of the co-terminal program in...
Speaker: Yiou Li, Assistant Professor, Mathematical Sciences Department, DePaul University Title: An Efficient Design of Pilot Experiments for Generalized Linear Models Abstract: The experimental...
Speaker: Yindong Chen, Ph.D., Applied Math, Illinois Institute of Technology Title: Low-Discrepancy Points via Energetic Variational Approach Abstract: In this talk, a method to generate a set of low...
Speaker: Qiong Zhang, Assistant Professor, School of Mathematical and Statistical Sciences, Clemson University Title: A Brief Overview on Bayesian Optimization Abstract: In this talk, I will give a...
Speaker: Lulu Kang, associate professor of applied mathematics, Illinois Institute of Technology Description: We aim to design a fairness-aware allocation approach to maximize the geographical...
