Aylin Tastan and Afief D. Pambudi first and second in Student Paper Competition


At the 2020 IEEE RadarConference, Aylin Tastan and Afief D. Pambudi both successfully participated in the 2020 IEEE Radar Conference Student Contest. Aylin Tastan’s work on robust clustering for human gait signatures was awarded the Best Student Paper Award, whereas the runner-up by Afief D. Pambudi focused on robust landmine detection.

Among the 143 papers submitted to the competition from across the world, the best five papers were selected as finalists through a rigorous review process by the AESS Radar Systems Panel Student Paper Competition Committee. In the final round, each participant presented their paper in a dedicated live on-line session to the jury.

In Aylin Tastan’s award-winning paper, she designed a parameter-free robust clustering algorithm to cluster highly contaminated human gait radar data. She extracted a new set of features from the data and deployed a graph-based outlier detection algorithm, using typical degree information of a sparse graph. This algorithm emphasizes the importance of degree information in a sparse graph, which can be valuable also for other purposes, such as weighting functions. In addition, it provides potential a priori information for designing robust sparse graphical models.

Afief D. Pambudi’s second-placed paper contributes to increase the performance and usage of ground-penetrating radar for detecting landmines. It proposes a copula-based robust test statistic for landmine detection in forward-looking ground-penetrating radar imagery. In this work, Pambudi successfully incorporated the dependence structure between different images into the test statistic for increased detection accuracy.

The work of Afief D. Pambudi is in close cooperation with Prof. Fauzia Ahmad from Temple University, PA, USA and the U.S. Army Research Laboratory.

Warm congratulations to Aylin and Afief!