Human Behavior Modeling with Machine Learning: Opportunities and Challenges
Nuria M Oliver · Albert Ali Salah
Mon Dec 9th 08:30 -- 10:30 AM
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.
Machine Learning for Computational Biology and Health
Anna Goldenberg · Barbara Engelhardt
Mon Dec 9th 11:15 AM -- 01:15 PM
Questions in biology and medicine pose big challenges to existing ML methods. The impact of creating ML methods to address these questions may positively impact all of us as patients, as scientists, and as human beings. In this tutorial, we will cover some of the major areas of current biomedical research, including genetics, the microbiome, clinical data, imaging, and drug design. We will focus on progress-to-date at the intersection of biology, health, and ML. We will also discuss challenges and open questions. We aim to leave you with thoughts on how to perform meaningful work in this area. It is assumed that participants have a good grasp of ML. Understanding of biology beyond high school level is not required.