Alberto García Robledo

Alberto García Robledo is a Mexican Computer Scientist and Research Engineer.

He holds an M.Sc. and a Ph.D. in Computer Science from the Center for Research and Advanced Studies of the National Polytechnic Institute (Mexico). He is a full-time CONACYT researcher at the Center for Research in Geography and Geomatics (Mexico) and a researcher of the Observatorio Metropolitano CentroGeo group.

His areas of specialty include HPC, Big Data, Graph Computing, Reinforcement Learning, and Visual Analytics. Currently, his research is focused on developing novel platforms for Big Connected Data processing and visualization with geospatial applications in mind.

He has conducted research and developed technological projects in the fields of HPC, Graph Analytics, Applied Graph Theory, Visual Analytics, and Big Data at Cinvestav (Mexico), CentroGeo (Mexico), and MIT (US).

In HPC, Dr. García Robledo has studied and developed parallel algorithms for measuring compute intensive properties on large complex networks by leveraging GPU+multicore heterogeneous computing.

In Graph Analytics, Dr. García Robledo proposed and led the development of a novel situational awareness framework for risk ranking with applications in financial fraud detection for the MIT, in collaboration with Accenture.

In Applied Graph Theory, Dr. García Robledo has studied the correlations of complex network metrics when measured on the macroscopic topology of the Internet. He is also conducting research on an approach for the efficient ranking of Autonomous Systems in large Autonomous System Networks.

In Visual Analytics and Big Data, Dr. García Robledo is conducting research on hardware-accelerated platforms for the visualization of large connected data, as well as distributed platforms for processing large vehicle traffic data on the Cloud.

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