Iraklis Giannakis received his bachelor and master degree in geophysics from the Aristotle University of Thessaloniki in 2009 and 2011 respectively, where his research focused in near surface geophysics including electrical resistivity tomography (ERT), potential methods, seismics and ground penetrating radar (GPR).

In 2015 he received his PhD from The University of Edinburgh under a project co-funded by the Defence Science and Technology Laboratory (DSTL) and the Engineering and Physical Sciences Research Council (EPSRC). His research focused on numerical modelling of GPR for landmine detection. During his PhD studies, as a member of COST (European Cooperation in Science and Technology) Action TU1208, he was a visiting researcher at Roma Tre University working on applications of GPR to civil engineering. Subsequently, he was employed as postdoctoral researcher at Delft University of Technology (TUDelft) in the Microwave Sensing, Signals and Systems (MS3) group.

There, he worked for D-Box, an industry oriented project aimed to deliver end-user tools for efficient demining. After finishing his national service in the Greek army he was employed by The University of Edinburgh under a project funded by Google fiber. Subsequently he became a research fellow at University of West London where he focused on the applications of non-destructive testing to forestry and arboriculture applications. In addition, he was a lecturer for the course of Mathematics and Statistics. Lastly, he is a frequent reviewer in journals associated with geophysics and he is part of the team behind gprMax (www.gprmax.com), an open-source FDTD solver tuned for GPR.

Throughout his academic career, he acquired a robust background to the whole spectrum of geophysics with a deep theoretical understanding combined with field experience. His research work is focused on near surface geophysics and its application to civil engineering and geosciences. In particular, he is interested in NonDestructive Testing (NDT), numerical modelling of GPR using FDTD, antenna modelling, Full-Waveform Inversion (FWI) and machine learning.