Uncertainty quantification is used to determine how uncertainties in data affect a model. There are many different ways to quantify uncertainty. Choosing the correct method of quantification can be tough because there are many different factors. The different methods of quantification share similar operations. However, using the correct method can be complicated and is time-consumingContinue reading “Simplifying and Improving Uncertainty Quantification with UM-Bridge”
Category Archives: News
From Speedy Simulations to Trustworthy AI: Undergraduates Take on Research Challenges at Illinois Tech for the Summer
A dozen students from across the United States have been chosen for the 10-week Summer Undergraduate Research Experience (SURE) program at the Illinois Institute of Technology. The SURE program offers opportunities for conducting research in computational mathematics and data science. The students worked on one of the five different research topics. One group worked withContinue reading “From Speedy Simulations to Trustworthy AI: Undergraduates Take on Research Challenges at Illinois Tech for the Summer”
QMCPY talks at the 14th International Conference on Monte Carlo Methods and Applications
The conference covers a wide range of topics related to Monte Carlo methods, including traditional areas such as computational statistical physics, Quasi Monte Carlo methods, and Markov Chain Monte Carlo in high dimensions. It also addresses emerging topics such as generative models, experimental design in Uncertainty Quantification, Monte Carlo simulations and High-Performance Computing, reinforcement learningContinue reading “QMCPY talks at the 14th International Conference on Monte Carlo Methods and Applications”
Combining the Expertise of the StaSASticians and a Commitment to ESG Principles to Deliver Comprehensive Wealth Management and Investment
The StaSASticians are a group of five people who created an innovative solution for better investing during the SAS hackathon. Three students from IIT applied mathematics, Narges, Kan, and Thi were part of the group. Their solution simplifies investment for both institutions and individuals. Their user-friendly application enables responsible and profitable investments by filtering companiesContinue reading “Combining the Expertise of the StaSASticians and a Commitment to ESG Principles to Deliver Comprehensive Wealth Management and Investment”
Illinois Tech Receives NSF Grant to Offer Intensive Summer Research Program in Computational Mathematics and Data Science for Undergraduates
Illinois Institute of Technology has secured a grant from the National Science Foundation’s Research Experiences for Undergraduates program to allow students across the country to participate in an intensive summer research project. The grant will allow ten undergraduate students to spend ten weeks working on computational mathematics and data science research projects on the IllinoisContinue reading “Illinois Tech Receives NSF Grant to Offer Intensive Summer Research Program in Computational Mathematics and Data Science for Undergraduates”
QMCPy Events Coming Soon
Our team is excited to announce that we will be presenting on QMCPy at the following upcoming events SAMO 2022: The Tenth International Conference on Sensitivity of Model Output. March 14 – 16, 2022. 2022 CORS/INFORMS International Conference. June 5 – 8, 2022. MCQMC 2022: The 15th International Conference on Monte Carlo and Quasi-Monte CarloContinue reading “QMCPy Events Coming Soon”
Elliptic PDE Demo
Special thanks to Pieterjan Robbe for his new demo on Elliptic PDEs and continued collaboration on multilevel (Quasi-) Monte Carlo! The Jupyter notebook version of this demo is available in the QMCPy GitHub.
Monte Carlo Methods 2021
Hope to see you virtually at Monte Carlo Methods 2021, in August 2021. This the prime odd year Monte Carlo conference featuring computer scientists, mathematicians, and statisticians. We are organizing a special session on Quasi-Monte Carlo software. More info coming soon.
Quasi-Monte Carlo Software Article
We recently uploaded an article on Quasi-Monte Carlo Software to https://arxiv.org/abs/2102.07833. Abstract: Practitioners wishing to experience the efficiency gains from using low discrepancy sequences need correct, well-written software. This article, based on our MCQMC 2020 tutorial, describes some of the better quasi-Monte Carlo (QMC) software available. We highlight the key software components required to approximateContinue reading “Quasi-Monte Carlo Software Article”
QMCPy Version 1.0
The developers of QMCPy are happy to announce the release of version 1.0 on February 12, 2021, Chinese New Year! We would like to thank all those who have made this development possible. A special thank you to Developers: Sou-Cheng T. Choi, Fred J. Hickernell, Michael McCourt, Jagadeeswaran Rathinavel, and Aleksei Sorokin; Collaborators: Mike Giles,Continue reading “QMCPy Version 1.0”