Dimosthenis Pasadakis

I am a postdoctoral fellow at the Institute of Computing (CI) of Università della Svizzera italiana (USI), where I act as the principal investigator (PI) of the project Directed acyclic graph partitioning for scheduling tasks, financed by the Huawei Research Center Zürich for 2025. My work is also supported by the joint German Research Foundation (DFG) and Swiss National Science Foundation (SNSF) project Numerical algorithms, frameworks, and scalable technologies for extreme-scale computing.

In the private sector, I am the Chief Operating Officer (COO) of Panua Technologies Sagl, a company based in Lugano that creates customized high-end software solutions for large-scale prediction, simulation, optimization, and graph analytics.

Personal website: https://dmspas.github.io

The focus of my research is centered around the question “How can we accurately and efficiently estimate the graphical structure of high-dimensional data, and subsequently use it for exploratory data analysis tasks?” My work has addressed these problems by developing highly accurate large-scale algorithms for graph learning, and for the discovery of clusters, partitions, and anomalies on graphs. The introduced algorithmic solutions aim to bridge the gap between theoretical academic research and industrial software development.