Veljko Pejovic, PhD of Faculty of Computer and Information Science, University of Ljubljana, Slovenia

The centralization of data and intelligence poses several risks, including potential privacy breaches, single points of failure, and stifling innovation. Decentralization, on the other hand, remains challenging as edge devices often have to work with limited computing capabilities and battery energy budgets. In this talk, I will present a line of research conducted at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia, where we aim to reduce the computational and energy burden of deep learning, thus enabling AI on the edge. The basis of our approach is dynamically approximable computing, which allows us to adapt the amount of computation to the actual situation in which deep learning occurs. The talk will demonstrate this philosophy applied to different domains, from physical activity recognition on smartphones to weed identification on a camera-equipped unmanned aerial vehicle (UAV). 

Biography:

Veljko Pejovic received his PhD and MSc in computer science from the University of California Santa Barbara and BSc from School of Electrical Engineering, University of Belgrade, Serbia. He is an associate professor at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. Prior to this, he was a Research Fellow at the Computer Science Department, University of Birmingham, UK. In Ljubljana, he is leading research on mobile computing, focusing on resource-efficient mobile systems, human-computer interaction, and cybersecurity in ubiquitous systems. His awards include the best paper nomination at ACM UbiComp and the first prize at Orange D4D challenge for his work on epidemics modeling. More about his research can be found at http://lrss.fri.uni-lj.si/Veljko/ 

Email: