Dr. Guy Schumann is the CEO and Principal Scientist at RSS-Hydro Sarl-S and also a Principal Scientist at Remote Sensing Solutions, Inc. Dr. Schumann is currently also a Visiting Research Fellow at Geographical Sciences, University of Bristol and an affiliate at the Dartmouth Flood Observatory (DFO), INSTAAR at the University of Colorado Boulder, CO. He received both the MSc (Remote Sensing) and PhD (Geography) degrees from the University of Dundee (UK). Dr. Schumann has around 15 years of experience in the field of remote sensing data integration with hydrodynamic modeling and particularly radar remote sensing and its use in flood models. Most of his current work and projects are research projects focusing on simulating river hydrodynamics and floodplain inundation. Dr Schumann is also involved in the Interoperability Program activities of the Open Geospatial Consortium (OGC) and is spearheading an international initiative to create a global high-accuracy, open-access digital elevation model.
Research Interests: flood hydrology; remote sensing; radar; hydrodynamic modeling; flood disaster; digital elevation model.

Dr. Mohammad Zare is working as a research scientist, with focus on hydrological prediction models applying machine learning (ML) and artificial intelligence (AI) techniques, spatial data management and developing remote sensing models at the Research and Education Department (RED) of RSS-Hydro. He was an undergraduate student in applied math at Tabriz University in 2003 and in water engineering and received his bachelor in 2009. He then pursued a MSc in water resource engineering in his home town, Kermanshah, Iran. He started his PhD at Tehran University in 2011, and, at the same time, applied for a PhD position abroad. His PhD research idea won him two full governmental PhD scholarships from Australia and Germany, ergo, he did his PhD under the German Academic Exchange Service (DAAD) scholarship and graduated in July 2017 from the University of Kassel, faculty of Civil and Environmental Engineering, Germany. His thesis focused on the application of data driven approaches – which integrates machine learning algorithms with evolutionary optimization algorithms – in water resources management. Over the past years, Dr. Zare has been working at several universities and research institutes in different countries including Iran, Germany and Luxembourg. He also acts as supervisor/advisor and examiner of several MSc. and PhD theses.
Research Interests: signal (image) processing; statistical analysis; machine learning methods; optimization algorithms; spatial data management (RS & GIS) and hydrologic big data analysis.