Skip to main content

“Urgent Analytics Across the Computing Continuum” short talk at SC20 UrgentHPC Workshop

In this talk I explore how the computing continuum, spanning resources at the edges, in the core and in-between, can be harnessed to support urgent science, Early Earthquake Warning (EEW) in particular, and present a system stack that can enable the fluid integration of distributed analytics across a dynamic infrastructure. Specifically, I will describe programming … Read More

Vision talk on “Harnessing the Computing Continuum for Urgent Science” at DCC2020

Prof. Parashar presented a Vision Talk titled “Harnessing the Computing Continuum for Urgent Science” at the International Workshop on Distributed Cloud Computing (DCC 2020) held in conjunction with ACM SIGMETRICS 2020, May 2020. Abstract: Urgent science describes time-critical, data-driven scientific workflows that can leverage distributed data sources in a timely way to facilitate important decision … Read More

Outstanding Paper Award in Artificial Intelligence for Social Impact (AAAI-20)

Our paper entitled “A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning” was selected in the Artificial Intelligence for Social Impact track at AAAI-20. This work addresses the challenges of characterizing earthquake and issuing broadcast alerts in a matter of seconds by relying on an effective use of scientific instruments and large-scale detection. As … Read More

Two papers accepted at CCGrid2020

Two papers accepted at the 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid2020, rank A) next May in Melbourne. “Enhancing microservices architectures using data-driven discovery and QoS guarantees”. This work proposes a service discovery framework using a data-centric approach while ensuring performance guarantees for microservices. First publication of Zeina Houmani, my very … Read More

Article accepted at AAAI20 on Distributed Machine learning at the Edge

Our article “A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning” has been accepted at AAAI20, the 34th Conference on Artificial Intelligence. This paper introduces the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system, a novel machine learning-based approach that combines data from GPS stations and seismometers to detect medium and large earthquakes. Preprint is … Read More