Deep thoughts on geometric learning & exploration of non-Euclidean data

Guy Wolf and I are organizing an online SIAM Mathematics of Data Science (MDS) mini-symposium on “Deep thoughts on geometric learning & exploration of non-Euclidean data.” We have a great list of speakers and everyone is encouraged to join via Zoom. More details are below!

Session 1

Date: June 17, 2020
Time: 12:00pm – 2:30pm (eastern)
Zoom link: here
List of talks:

1. 12:00pm – 12:30pm: Jure Leskovec (Stanford)
   Title: Recent Advancements in Graph Neural Networks
   Abstract
   Slides
2. 12:30pm – 1:00pm: Siheng Chen (Carnegie Mellon)
   Title: Data science with graphs: From social network analysis to autonomous driving
   Abstract
   Slides
3. 1:00pm – 1:30pm: Alexander Cloninger (UC San Diego)
   Title: Variational Diffusion Autoencoders with Random Walk Sampling
   Abstract
   Slides
4. 1:30pm – 2:00pm: Dongmian Zou (Minnesota)
   Title: Graph Convolutional Neural Network via Scattering
   Abstract
   Slides
5. 2:00pm – 2:30pm: Michael Perlmutter (Michigan State)
   Title: The Graph Scattering Transform(s)
   Abstract
   Slides

Session 2

Date: June 24, 2020
Time: 2:00pm – 4:00pm (eastern)
Zoom link: here
List of talks:

1. 2:00pm – 2:30pm: Will Hamilton (McGill)
   Title: Latent Variable Modelling with Hyperbolic Normalizing Flows
   Abstract
2. 2:30pm – 3:00pm: Zhengdao Chen (NYU)
   Title: The Expressive Power of Graph Neural Networks from Three Perspectives
   Abstract
   Slides
3. 3:00pm – 3:30pm: Rongjie Lai (Rensselaer)
   Title: Geometry Inspired DNNs on Manifold-structured Data
   Abstract
   Slides
4. 3:30pm – 4:00pm: Rex Ying (Stanford)
   Title: Graph Neural Networks and Embedding Geometry
   Abstract
   Slides