Abstract:
Modern sensing technologies have provided the possibility of sensing the world in a way that has not been possible before, generating massive spatio-temporal data sources. How can we use such data to understand and even change the complex world around us for the better? In this talk, I will discuss unique machine learning challenges in transforming such data into actionable decisions. These challenges call for automated solutions to address various problems, from filling the gaps in the data to filling the gaps in the knowledge acquired from data alone. I will present a few examples of such problems and automated solutions to address them.
Bio:
Mitra Baratchi is an associate professor of artificial intelligence at Leiden University, where she leads the Spatio-temporal data Analysis and Reasoning (STAR) and co-leads of the Automated Design of Algorithms research group. Her research interests lie in spatio-temporal, time-series, and mobility data modelling. She focuses on developing algorithms for wearable sensor data, Earth observations, and other open spatio-temporal data sources. Specifically, she explores the design of algorithms that can automatically handle all necessary data processing tasks from data collection through high-level modelling, information extraction, and effective decision-making. Her research targets applications in a broad range of urban, environmental, and industrial domains, for which she has collaborated, notably with the European Space Agency, Netherlands Institute for Space Research, Honda Research Institute, various municipalities, and researchers in other scientific disciplines.
