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...
Speaker: Kinjal Basu, staff applied researcher LinkedIn Description: Key problems arising in web applications (with millions of users and thousands of items) can be formulated as Linear Programs (LP)...
Speaker: Yiou Li, assistant professor of statistics, DePaul University Description: Experimental designs for a generalized linear model (GLM) often depend on the specification of the model, including...
Speaker: Fred Hickernell, professor of mathematics, Illinois Institute of Technology Description: Quasi-Monte Carlo (QMC) methods achieve substantial efficiency gains by replacing independent and...
Speaker: Hadis Anahideh, Research Assistant Professor of Mechanical and Industrial Engineering, UIC Description: limited informative data remains the primary challenge for the optimization expensive...
Speaker: Jiuhai Chen, Ph.D. candidate (AMAT) Illinois Tech Description: In this work, we present a new particle-based VI methods for Bayesian inference, inspired by a discrete energetic variational...
