Happening @ Michigan https://events.umich.edu/list/rss RSS Feed for Happening @ Michigan Events at the University of Michigan. Friday Night AI (October 2, 2020 7:00pm) https://events.umich.edu/event/77588 77588-19885834@events.umich.edu Event Begins: Friday, October 2, 2020 7:00pm
Location: Off Campus Location
Organized By: Michigan Artificial Intelligence Laboratory

Invited speakers:
Profs. Ceren Budak (School of Information) and Rada Mihalcea (Michigan AI)
Organizer: Michigan AI Lab in collaboration with the Ann Arbor District Library
Moderator: Prof. Benjamin Kuipers, Michigan AI

Pre-registration required by Oct. 1.

The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers has made it challenging to identify trustworthy news sources. Over the past few months, the amount of misinformation shared online has been exacerbated by the COVID-19 pandemic (which has sometime been also referred to as an infodemic) as well as by the ongoing political debates and the upcoming federal elections. Artificial Intelligence provides ways to identify misinformative content online, and to potentially curb its spread. Join us for a conversation with Michigan experts Prof. Ceren Budak and Prof. Rada Mihalcea, who will discuss how Artificial Intelligence can be used to address fake news and misinformation.

What are the ways that AI may be used to identify misinformation and fake news?
What are the challenges encountered when developing such AI systems?
What are the benefits and risks of using automated ways to fight misinformation?

About the panelists:
Ceren Budak is an Assistant Professor of Information, School of Information and Assistant Professor of Electrical Engineering and Computer Science, College of Engineering at the University of Michigan. Her research interests lie in the area of computational social science. She is particularly interested in the use of large scale data sets and computational techniques to study problems with policy, social and political implications.
Rada Mihalcea is a Professor of Computer Science and Engineering at the University of Michigan and the Director of the Michigan Artificial Intelligence Lab. Her research interests are in computational linguistics, with a focus on lexical semantics, multilingual natural language processing, and computational social sciences. Together with her research lab and collaborators, she has worked on the problem of automatic deception detection for more than ten years, addressing among others the detection of deception in language and multimodal streams, the identification of fake news, and identity deception. She is the recipient of a Presidential Early Career Award for Scientists and Engineers awarded by President Obama (2009) and a Fellow of the Association for Computing Machinery (2019).

]]>
Lecture / Discussion Tue, 22 Sep 2020 15:36:26 -0400 2020-10-02T19:00:00-04:00 2020-10-02T20:30:00-04:00 Off Campus Location Michigan Artificial Intelligence Laboratory Lecture / Discussion Friday Night AI
Tianle Yuan: Artificial Intelligence-based Cloud Distributor (AI-CD): Modeling Clouds at Different Scales (January 26, 2021 3:00pm) https://events.umich.edu/event/80989 80989-20830790@events.umich.edu Event Begins: Tuesday, January 26, 2021 3:00pm
Location: Off Campus Location
Organized By: Michigan Institute for Computational Discovery and Engineering

Abstract: Here we introduce the artificial intelligence-based cloud distributor (AI-CD) approach to generate cloud fields across different scales and cloud types. We show that generative adversarial nets (GANs) can not only generate realistic cloud fields with corresponding meteorological variables, but also capture known physical relationship between cloud fields and meteorological variables such as sea surface temperature, atmospheric stability, and relative humidity etc. We demonstrate that this approach works across a large range of spatial scales: from individual grid points (sub-grid process modeling), multiple grids, to global scale. In addition, the AI-CD approach is stochastic in nature. We suggest the AI-CD approach can be used as a data-drive framework for stochastic cloud parameterization.

Bio: Dr. Yuan got his B.S. in Geophysics and Computer Science in Peking Univ., PhD from University of Maryland, College Park, in 2008. After graduation, he is affiliated with Joint Center for Earth Systems Technologies (JCET) at the UMBC and NASA GSFC as an Associated Research Scientist. His research interest includes cloud and aerosol climate feedback, aerosol-cloud interactions, remote sensing, cloud physics, and application of ML/Deep Learning in Earth science. In deep learning applications, Dr. Yuan published a few papers in modeling sub-grid clouds, global scale clouds, hurricane prediction, finding ship-tracks, and supervised and unsupervised cloud morphology classifications.

]]>
Workshop / Seminar Wed, 20 Jan 2021 10:05:38 -0500 2021-01-26T15:00:00-05:00 2021-01-26T16:00:00-05:00 Off Campus Location Michigan Institute for Computational Discovery and Engineering Workshop / Seminar Tianle Yuan