With industry-leading immersive capabilities, the Magic Leap 2 device offers countless new opportunities for developers to create immersive applications that bring the metaverse to life, according to panelists at a pre-release developer event hosted by the University of Miami Institute for Data Science and Computing (IDSC). Read more “Catch the Replay of the Magic Leap 2 Developer Event”
The Magic Leap 2 is here and we will be hosting a developer event on September 20 at the University of Miami Lakeside Village Auditorium. This event will feature customer use case presentations from Magic Leap, Unity, PeakActivity, and others, followed by an AMA (Ask Me Anything Q&A Session) and a networking reception. See Agenda below . . .
The Magic Leap 2 is here and we will be hosting a developer event on September 20 at the University of Miami Lakeside Village Auditorium. This event will feature customer use case presentations from Magic Leap, Unity, PeakActivity, and others, followed by an AMA (Ask Me Anything Q&A Session) and a networking reception.
Leaders in academia, government, and industry shared ideas on how cities can use new technologies, from cloud computing and connected devices to smart materials and sustainable construction, to maximize opportunities for data-driven growth, in person, at the fifth annual Smart Cities MIAMI Conference, May 19 and 20, 2022. Read more “Catch the Replay: Smart Cities Miami 2022 Explored Digitally Connected Cities”
The University of Miami School of Communication XR Initiative recently held the launch of the Miami Chapter of the VR/AR Association (VRARA). Hosted by IDSC Director of Creative Computing, Kim Grinfeder, this interactive hybrid event drew a crowd online and at the Bill Cosford Cinema on campus.
Read more “Kim Grinfeder to Speak at VRARA Education Forum 5/19”
The University of Miami School of Architecture and Institute for Data Science and Computing present the 5th annual Smart Cities MIAMI Conference on Thursday and Friday, May 19-20, 2022. Read more “Join us at the in-person Smart Cities MIAMI Conference 5/19+20”
The University of Miami School of Architecture and Institute for Data Science and Computing present the 5th annual Smart Cities MIAMI Conference on Thursday and Friday, May 19-20, 2022. Read more “Smart Cities MIAMI 2022 5th annual Conference 5/19-20”
Join us for a special guest lecture (either in person or virtually) featuring Claus Ekstrøm, professor and vice-chair at the Section of Biostatistics, University of Copenhagen. His primary research interests are centered on developing methods for the analysis of high-dimensional data problems and causal discovery. He’s authored two books on statistics and is frequently used as an expert on statistics in Danish news media. Claus has been a grumpy old man from a young age.
Register Now | Wednesday 12/1/2021, 4:00-5:00 PM
This is a hybrid event: In person at Otto G. Richter Library, CR 343 | or online via Zoom.
Title of the lecture: “Validation of visual inference methods in statistics by use of deep learning”
When does inspecting a certain graphical plot allow for an investigator to reach the right statistical conclusion? Visual inference is commonly used for various tasks in statistics—including model diagnostics and exploratory data analysis – and though attractive due to its intuitive nature, the lack of available methods for validating plots is a major drawback.
We propose a new validation method for visual inference. Our method trains deep neural networks to distinguish between plots simulated under two different data-generating mechanisms (null or alternative), and we use the classification accuracy as a technical validation score (TVS). The TVS measures the information content in the plots, and TVS values can be used to compare different plots or different choices of data-generating mechanisms, thereby providing a meaningful scale that new visual inference procedures can be validated against.
We apply the method to three popular diagnostic plots for linear regression, namely the scatter plot, the quantile-quantile plot, and the residual plot. We consider various types and degrees of misspecification, as well as different within-plot sample sizes. Our method produces TVSs that increase with increasing sample size and decrease with increasing difficulty, and hence the TVS is a meaningful measure of validity.