Nikolaos Freris
Nikolaos Freris is affiliated with computer science and researches large-scale networked systems and distributed optimization.
At a glance
Nikolaos Freris
Nikolaos Freris is affiliated with computer science and researches large-scale networked systems and distributed optimization.
Research interests include AIoT, cyber-physical systems, distributed optimization and machine learning.
Research contributions and academic biography
Nikolaos Freris | Research contributions and academic biography
Nikolaos Freris works at the intersection of distributed algorithms, cyber-physical systems, the Internet of Things and data-driven optimization. These fields ask how networks of devices or computing agents can coordinate when information is incomplete or decentralized. The mathematical tools extend to sensor systems and machine learning. His background includes a doctorate from the University of Illinois Urbana-Champaign, and the university has identified a West Campus office in the Dian San Building.
Distributed optimization and networked computation
His research on the Internet of Things and cyber-physical systems examines how distributed devices coordinate information and decisions. Optimization becomes difficult when computation and observations are spread across many network nodes.
Randomized algorithms and network synchronization help address efficiency and reliability in such systems. His academic trajectory includes doctoral study at UIUC and research appointments at IBM Research, EPFL and New York University.
Academic profile and research
Large-scale connected systems and distributed optimization
Research focus
Professor Nikolaos Freris works at the meeting point of networked systems, machine learning and computational optimization. His research addresses how devices and computing systems can coordinate decisions when data and computation are distributed rather than collected at one central location.
Academic background
He holds a doctorate in electrical and computer engineering from the University of Illinois Urbana-Champaign. Before joining USTC he held positions at IBM Research, EPFL and New York University. His work covers large-scale Internet of Things systems, cyber-physical systems and mathematical optimization.
Research contributions
The university faculty listings identify representative contributions to randomized methods for distributed optimization, stochastic gradient algorithms and network synchronization. These methods have applications in complex sensor networks, transportation systems and intelligent infrastructure.