Liliana Perez, PhD is an Associate Professor (Geography) at the Université de Montréal (UdeM). She has a broad research experience in geographical information science and geospatial analysis and complex modelling. She received her PhD in Geography with a specialization in modelling complex systems from Simon Fraser University (SFU) in 2011. Her dissertation examined the topic of spatial and temporal modelling of insect epidemics and their complex behaviors. Following her doctoral studies, she held two postdoctoral research positions at the University of British Columbia (2011) and the University of Victoria (2011-2013), where she worked on issues concerning the spatial and temporal relationships between changes in indirect indicators of biodiversity and climate change. Since she joined the UdeM in June 2013, she founded and heads the Laboratory of Environmental Geosimulation (LEDGE) that hosts a research group interested in the dynamics of complex geographic systems.  Her research interests center around the incorporation of artificial intelligence and machine learning techniques into the development of Agent-Based Models to solve complex socio-ecological problems, such as urban, national and transnational migration, climate change and the impacts on urban ecosystems, urban growth and urban segregation, with the aim to improve spatiotemporal modeling and simulation, which can be used to study and understand extremely complex movement patterns and behaviours. Under her direction, LEDGE creates new algorithms for spatial processes, develops tools for experimenting with geographical systems, proposes novel methodologies for conceptualizing spatial entities and the relationships between them, and applies simulation models to a variety of environmental issues. She collaborates with scientists from national (e.g. McGill, Simon Fraser University, Metro Vancouver and Ducks Unlimited) and international (e.g. CREAF, University of Magallanes and Instituto de la Patagonia) research institutions. Currently, she supervises two master and four PhD students.