SUMS 2021

Math and Illusion

SUMS 2021: Math and Illusion is slated to cover 4 subtopics: Optical Illusions, Magic, Misinformation and Misleading Patterns. This event is free and open to anyone!


When: March 14th, 2021 (π-day) from 10 AM EST to 6 PM EST

Where: Virtual (links to be shared near event day, register to receive the links)

Registration: sign-up form (for attendance/student talk*/poster session*/artwork)


Events:

  • Four Superb Faculty Talks

  • Awesome Student Presentations and Poster Sessions

  • Artwork inspired by mathematics or illusion

  • Community-building Social and Games


Questions? Email us at sums@brown.edu!

*Note: deadline to register for student talks and poster sessions is Mar 1, 2021.


Faculty Talks

Photo credit: L.A. Cicero

THE (one and only) RANDOM GRAPH

Persi Diaconis, Stanford University

Abstract:

Imagine n people connected by random links (for each pair of people, connect them or not with probability 1/2). This makes a connection graph. If you make another (with the same people, but new connections) the chance that the two graphs are 'the same' is tiny (of course). Now think about n= infinity. Strange but true: the chance that two such graphs are 'the same' is certain. this object 'THE random graph and it has amazing properties. It's so strange, it's almost weird enough to get you doubting that there really is an infinity (is there?). I've been using such things (with the logician Maryanthe Milliarias) to show that certain things that we would like to 'get our hands on' are impossible to describe. Years of thinking make it seem so and this is one way of proving things. I will explain all this 'In English' for a general audience.


Other links:

The Art of Maths

Chris Budd, University of Bath

Abstract:

TBA


Other links:

  • Dr. Budd's homepage.

  • A fun and inspiring talk by Dr. Budd on creative and playful math.

Shiry Ginosar Portrait

The Art of Deception - Encountering Perception as a Creative Material

Shiry Ginosar, University of California, Berkeley

Abstract:

Computer Vision has made great strides forward in the last decade, and yet, the perception of vision systems is still impoverished. Current systems excel at tasks that are easy to define and evaluate, such as the classification of images and the detection of objects. However, they fail to capture the things that really matter to humans, such as the non-verbal, detailed information that is essential for the majority of everyday human behaviors. In my work, I am trying to push the limits of computer vision toward such a rich perception. I will cover several projects that take steps in this direction by mining for temporal changes in historical data, using multimodal data to learn about interpersonal communication, and modeling individual appearances. Through these examples, I will discuss some of the tools needed to learn rich representations directly from big data. In particular, I will focus on the human visual system and how we can utilize its capabilities and limitations for producing realistic-looking synthetic content.


Other links:

Peter Sarnak Portrait
Photo credit: Andrea Kane, Institute for Advanced Study

Patterns in Number Theory - Misleading or True?

Peter Sarnak, Princeton University

Abstract:

TBA


Other links:

Student Talks

details to these presentations will be available soon.

TBA

Student Poster Presentations

TBA