•   When: Friday, March 11, 2022 from 11:00 AM to 12:00 PM
•   Speakers: Xuhao Chen
•   Location: ZOOM only
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Abstract:
Numerous AI applications in social networks, e-commerce, biomedicine and security, are driven by graph algorithms.
The graph data is massive and sparse, which poses great challenges in computing system design.
In this talk, I will explore system design tradeoffs for an important class of graph algorithms — graph mining.
I will describe experiences creating abstractions, optimization techniques and automation methodologies for graph mining, 

across different layers of the system stack, including both software and hardware.
As I will demonstrate, cross-layer system design can fully unlock the potential of graph computing, and should be used for enabling pervasive AI.

Bio:
Xuhao Chen is a Research Scientist at MIT CSAIL, working with Prof. Arvind.
Dr. Chen is broadly interested in parallel systems and computer architectures for AI and big-data, with a focus on graph algorithms.
He has built multiple software and hardware systems for data mining and machine learning on graphs.
His work has been published in ISCA, MICRO, VLDB, ICS and DAC.
Before joining MIT, Dr. Chen was a Research Fellow at UT Austin.