Afief Dias Pambudi
As one of the top three award winners of the 2020 IEEE Radar Conference Student Paper Competition, I am truly honored to share my experiences with the AESS community. I am fortunate to have received guidance from a number of dear and ingenious mentors, namely, Prof. Abdelhak M. Zoubir, an infinite source of inspiration and exemplary excellence in research, as well as my long-term collaborators Dr. Fauzia Ahmad and Dr. Michael Fauss, whom I thank for their creativity and invaluable expertise. The continued support of such exceptional people has paved the ground for my own work.
I am from the eastern part of the Indonesian island of Java. I like to spend time in nature. Going for a walk has always been a great source for me to gain creativity and inspiration. I grew up among hard-working rice farmers who strongly benefit from their tight relationships to one another, helping them to face uncertain weather conditions. Uncertainty also plays a big part in my research topic. In my research, I am focusing on developing robust detection techniques, insensitive to environmental changes, to identify buried targets such as landmines or unexploded ordnance. The core of this effort is the investigation of model uncertainty, robust optimization and finding statistical relations of multi-view radar images.
In our paper titled “Copula-Based Robust Landmine Detection in Multi-View Forward-Looking GPR Imagery”, we find such a relation and demonstrate its potential for the detection of landmines using a ground-penetrating radar system. It is a novel method to improve the accuracy of GPR target detectors, which have not yet been sufficiently explored for practical application. In particular, the question of how uncertainty sets can be constructed in a data-driven manner warrants our further research.