Machine Learning for Resilient Systems Conference
Join us for the annual Machine Learning for Resilient Systems Conference hosted by the Machine Learning Resilient Institute. This premier event brings together leading experts, researchers, and practitioners to explore the latest advancements in machine learning technologies for enhancing system resilience.
Date: September 15-17, 2024
Theme: “Empowering System Resilience with Machine Learning”
Format: Keynote speeches, panel discussions, workshops, and poster sessions
The conference will be held at the downtown campus of the Machine Learning Resilient Institute, located in the heart of the city, offering easy access to local amenities and transport links.
Registration Information
Early Bird Registration: Available until July 1, 2024
Standard Registration: From July 2 to August 31, 2024
Late Registration: September 1-15, 2024
Register online at [Institute’s Event Page]
Program or Schedule Speakers and Presenters
Day 1: Keynote speeches and panel discussions on the impact of machine learning in resilient systems.
Day 2: Interactive workshops and technical sessions focused on practical applications and research findings.
Day 3: Poster sessions, networking events, and closing ceremony.
Speakers and Presenters:
Featuring distinguished speakers from academia and industry, including Dr. Jane Doe, a pioneer in machine learning resilience, and Mr. John Smith, known for his work on autonomous cyber-physical systems.
Who Should Attend
This conference is ideal for machine learning researchers, cybersecurity experts, software engineers, policy makers, and anyone interested in the field of system resilience.
Why Attend
Attendees will gain insights into cutting-edge research, network with peers and industry leaders, and learn about the latest machine learning technologies and methodologies being applied to improve the resilience of complex systems. Don’t miss the opportunity to contribute to the global conversation on building more resilient systems in the age of machine learning.
