Aurelio Vivas is an adjunct professor and researcher with 11 years of experience specializing in high-performance and data-intensive scientific computing, satellite image processing, and cloud-based workflow deployments. He holds an M.Sc. in Systems and Computing Engineering and is completing a PhD focused on scheduling strategies for efficient data movement in large scientific workflows. Aurelio has combined academic teaching (computer architecture, OS, Python, cloud foundations) with hands-on research and engineering at institutions like Universidad de los Andes and Argonne National Laboratory, contributing to tracing/profiling frameworks for exascale applications and multiple peer-reviewed publications. He has led DevOps and platform deployments for national-scale satellite data cubes and trained teams at IDEAM on operationalizing large-scale geospatial processing. Comfortable moving between low-level HPC concerns and applied machine-learning workflows, he often bridges research prototypes into production-ready platforms. An interesting thread across his career is repeated delivery of scalable, workflow-driven solutions that span CPUs, GPUs, and distributed cloud environments.
11 years of coding experience
6 years of employment as a software developer
Exchange student, Exchange student at Universidad Nacional de Colombia
Ingeniero de Sistemas, Ingeniero de Sistemas at Universidad del Valle (CO)
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Aurelio Vivas - Adjunct Professor at Universidad de los Andes