Merging Monte Carlo Methods with Machine Learning

Machine learning is proving to be a powerful tool in modern scientific research. In this talk, we give a brief tutorial of classical Monte Carlo methods and quasi-Monte Carlo methods. We then move to highlight Message-Passing Monte Carlo (MPMC), the first machine learning approach for generating highly uniform (named, low-discrepancy) point sets. We discuss the methodology and promote the open-source python code for MPMC and discuss some future adaptations, open questions and applications of this framework in scientific computation.

This is a hybrid event. To attend online, join us on Zoom here at 6pm:
https://numfocus-org.zoom.us/j/88165744160?pwd=DQ7Vq2uig6l2g62x5az0yMkO0MrD10.1

Sponsor: Discovery Partners Institute (DPI) and PyData Chicago co-host this event. DPI will provide the meeting site. Adyen Chicago will sponsor pizza and soft drinks for the onsite participants.

• DPI Address: 200 S. Wacker Drive, Chicago IL 60606
• Logistics: To access the meeting room Discovery on the 4th floor at DPI, we require first and last names of those who RSVP'd by 8am on Dec 12. Attendees will then present their IDs when they arrive at the front desk (right when they enter the building).