Past Event: Oden Institute Seminar

Scientific computing paradigms in scaling data science and network science

David Gleich, Associate Professor, Computer Science Department, Purdue University

Abstract

Common paradigms to understand and scale computational data-driven analysis include
- scaling computational resources
- improving algorithms
- improving models
or a combination of these. I will present an overview of contributions from my research team in these areas in scenarios that span data science, network science, scientific computing, and combinatorial scientific computing. We will also take a deeper look into our recent and ongoing research in higher-order methods for networks and hypergraphs where there are deep interactions between the algorithms and models.

Helpful Overview of Higher-order Networks:
https://sinews.siam.org/Details-Page/higher-order-network-analysis-takes-off-fueled-by-old-ideas-and-new-data

Codes:
https://github.com/kfoynt/LocalGraphClustering
https://github.com/MengLiuPurdue/LHQD

 

Biography

David Gleich is the Jyoti and Aditya Mathur Associate Professor in the Computer Science Department at Purdue University whose research is on novel models and fast large-scale algorithms for data-driven scientific computing including scientific data analysis, bioinformatics, and network analysis. He is committed to making software available based on this research and has written software packages such as MatlabBGL with thousands of users worldwide. Gleich has received a number of awards for his research including a SIAM Outstanding Publication prize (2018), a Sloan Research Fellowship (2016), an NSF CAREER Award (2011), the John von Neumann post-doctoral fellowship at Sandia National Laboratories in Livermore CA (2009). His research is funded by the NSF, DOE, DARPA, and NASA. For more information, see his website: https://www.cs.purdue.edu/homes/dgleich/