Jonatha Anselmi
About Jonatha Anselmi
Jonatha Anselmi is a Research Scientist at Inria in Grenoble, France, where he has worked since 2014. He specializes in developing scalable algorithms for large-scale distributed systems and has a strong background in computer engineering with a Ph.D. from Politecnico di Milano.
Work at Inria
Jonatha Anselmi has been a Research Scientist at Inria since 2014, based in Grenoble, Auvergne-Rhône-Alpes, France. His role involves developing algorithms aimed at minimizing congestion and operational costs in large-scale distributed systems. Anselmi has been part of the POLARIS team, which focuses on decision making under uncertainty. He previously worked with the CQFD team at Inria in Bordeaux from 2014 to 2018, contributing to various projects related to cloud networks and operational efficiency.
Education and Expertise
Jonatha Anselmi studied at Politecnico di Milano, where he earned a Bachelor of Science (B.Sc.) in Computer Engineering from 2000 to 2003, followed by a Master of Science (M.Sc.) from 2003 to 2005. He completed his Doctor of Philosophy (Ph.D.) in Computer Engineering at the same institution from 2006 to 2008. His expertise lies in developing scalable algorithms and utilizing Markov processes to model cloud network dynamics, particularly concerning delays and energy consumption.
Background
Before joining Inria, Jonatha Anselmi held various research positions. He worked as a Postdoc at Inria from 2009 to 2010 in Grenoble, France, and held a research position at the Basque Center for Applied Mathematics (BCAM) in Bilbao from 2010 to 2013. He also served as a Visiting Researcher at Caltech for one month in 2011 and completed a research internship at IBM T.J. Watson in 2008.
Research Interests
Jonatha Anselmi's research interests encompass a range of topics including queueing theory, machine learning, Markov decision processes, load balancing, and power consumption minimization. His work is particularly focused on developing methodologies that enhance the efficiency of distributed systems, addressing challenges related to operational costs and system performance.