Fall 2024

10/05/2024 | Comics About Science, From Feynman to Einstein: Math is Hard & Leaving Stuff Out is Harder -- Jim Ottaviani (U-M Librarian Emeritus)

Whether you’re applying for funding or discussing your work with a friend or colleague, science always involves storytelling. Using comics to tell those stories may seem weird when you first hear about them, but they’re a natural fit for introducing people to the passion, beauty, and joy of discovery. Even with both words and images at your disposal, though, you never get to give the whole picture…

10/19/2024 | The Science of Election Security -- J. Alex Halderman (U-M Bredt Family Professor of Computer Science and Engineering and Director, Center for Computer Security and Society)

Modern high-tech elections must defend against both real cybersecurity threats and false hacking claims. Learn how scientists and public officials have been working to keep them trustworthy and what risks most concern the experts heading into November.

11/02/2024 | Climate Change in the Great Lakes: Effects on Algal Blooms and Human Health -- Gregory Dick (U-M Earth & Environmental Sciences and School for Environment and Sustainability)

Harmful algal blooms affect freshwaters worldwide, threatening drinking water, recreation, and the lakes we love. Climate change is making them worse, but we are only beginning to understand their effects on human health.

11/16/2024 | Galaxy Clusters: Special Fields in the Sky, Special Challenges for Our Science -- Camille Avestruz (U-M Physics)

Galaxy clusters are made of hundreds to thousands of galaxies. They can tell us about the history and contents of our universe--but we need to find them and weigh them. 

11/23/2024 | AI and the Business World -- Nigel Melville (U-M Ross Business School)

Modern AI brings new benefits, costs, and risks to business organizations. This creates challenges as they try to adopt AI to achieve goals while avoiding harm and damage. I'll discuss findings from my research proposing a new framing of AI as a set of machine capabilities, share what I'm learning from industry engagements, and describe an innovative interdisciplinary initiative here at U-M that's asking the Big Questions about AI.