Upcoming Events

 

ISE Seminar Series Fall 2020

Title: Multiscale Methods in Action: From Machine Learning and Graphs to Realistic Future for Quantum Computing

Speaker: Ilya Safro, University of Delaware 

Time: Tuesday, December 1, 11:00 AM to 12:00 PM

Join Zoom Meeting: https://lehigh.zoom.us/s/99527921266

 

Abstract:

In many real-world problems, a big scale gap can be observed between micro- and macroscopic scales of the problem because of the difference in mathematical (engineering, social, biological, physical, etc.) models and/or laws at different scales. The main objective of multiscale algorithms is to create a hierarchy of problems, each representing the original problem at different coarse scales with fewer degrees of freedom. We will discuss the multiscale frameworks for (nonlinear) support vector machines, and several optimization and mining problems on graphs along with their computational results on large-scale instances. Time permits, we will also discuss a use case of such frameworks combined with the near-term quantum devices (including quantum universal and annealing machines). We will present in details a scalable multilevel framework for support vector machines that is based on the elements of algebraic multigrid and demonstrate a substantial improvement of the model training computational time, and other advantages.. 

 

Bio:

Dr. Ilya Safro is an Associate Professor of Computer Science in the Department of Computer and Information Sciences at the University of Delaware. Dr. Safro received his PhD in Applied Mathematics and Computer Science in the Weizmann Institute of Science under the supervision of Achi Brandt and Dorit Ron. In 2008-2012, Dr. Safro was a postdoc and Argonne scholar at Argonne National Laboratory with a joint appointment in the University of Chicago. In 2012, he joined the School of Computing and the School of Health Research of Clemson University. His research interests include algorithms in machine learning, AI, NLP, network science, and quantum computing. Dr. Safro is an academic editor in the machine learning and network science areas of PLOS One and Algorithms. He is also a member of advisory boards of several companies. He has organized several week-long workshops on high-performance graph algorithms, tutorials and mini-symposia at SIAM and IEEE conferences. His research is funded by NSF, DARPA, DOE, and industry.

 

 

 

 

 

 


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