Dec 9, 2019
Human behavior is complex, multi-level, multimodal, culturally and contextually shaped. Computer analysis of human behavior in its multiple scales and settings leads to a steady influx of new applications in diverse domains including human-computer interaction, affective computing, social signal processing and computational social sciences, autonomous systems, smart healthcare, customer behavior analysis, urban computing and AI for social good. In this tutorial, we will share a proposed taxonomy to understand, model and predict both individual, dyadic and aggregate human behavior from a variety of data sources and using machine learning techniques. We will illustrate this taxonomy through relevant examples from the literature and will highlight existing open challenges and research directions that might inspire attendees to embark in the fascinating and promising area of computational human behavior modeling. The goal of this tutorial is to provide an introduction to this burgeoning area, describing tools for automatically interpreting complex behavioral patterns generated when humans interact with machines or with others. A second goal is to inspire a new generation of researchers to join forces into realizing the immense potential of machine learning to help build intelligent systems that understand and interact with humans, and contribute to our understanding of human individual and aggregate behavior while always having human interests and wellbeing at their core.
Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.
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