Deep Learning and Artificial Intelligence for High-Performance Networks
Our research explores machine learning, deep learning and AI techniques when applied to operational networking and distributed computing problems.
With advances in computing, data is being produced at exponential rates requiring highly flexible mobility across HPC computing and distributed facilities. Networks are the essential bloodline to science collaborations across the globe such as in high-energy physics, earth sciences and genomics. However, upgrading network hardware, with high-end routers and optic fibers, to cope with this data revolution can cost millions of dollars. We are exploring artificial intelligence to design and efficiently manage distributed network architectures to improve data transfers or routing, guarantee high-throughput and improve traffic engineering.
For more information, contact <email@example.com>.
SC20 news: Our group is taking part in INDIS workshop organization followed by a talk on AI for Networking: the Engineering Perspective.
Divneet and Shan will be presenting their posters at Supercomputing 2020 at Tuesday November 17 from 11:30 am to 1:00 pm Eastern time. Their posters are titled "Netgraf : A collaborative Network Monitoring Stack for Network Experimental Testbeds" and "Multi-agent meta reinforcement learning for packet routing in dynamic network environments" respectively.
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23 Sept 2020: Initial website updated.