Title: Upskilling the Future Workforce Using AI and Affective Computing

Abstract: Many people fear automation. They may see it as a potential job killer. They may also be concerned about what can be automated. Could we train a computer to amplify human ability? Should we?

Our ability to be creative, use social learning and imitate sets us apart from other species. In this talk, I will provide some examples of how we can use these social theories to guide algorithms to 1) identify interpretable behavioral patterns; 2) confirm, elaborate and design interventions to better understand how the real-world works; 3) deploy the solutions to measure impact. By carefully designing appropriate applications of Human-Centered AI, we show that technology can improve important social and cognitive skills for many, including the disadvantaged, ill, disabled, and other individuals who struggle with socio-emotional communication, such as those with autism, severe anxiety, neurodegenerative disease, and terminal illness.

In this talk, I will offer insights gained from our exploration of several questions: How are humans able to improve important social and cognitive skills with an intelligent system? What aspect of the feedback helps the most? How can such systems be deployed to promote equity and access to healthcare?

Title: Multimodal AI: Understanding Human Behaviors

Abstract: Human face-to-face communication is a little like a dance, in that participants continuously adjust their behaviors based on verbal and nonverbal cues from the social context. Today's computers and interactive devices are still lacking many of these human-like abilities to hold fluid and natural interactions. Leveraging recent advances in machine learning, audio-visual signal processing and computational linguistic, my research focuses on creating computational technologies able to analyze, recognize and predict human subtle communicative behaviors in social context. Central to this research effort is the introduction of new probabilistic models able to learn the temporal and fine-grained latent dependencies across behaviors, modalities and interlocutors. In this talk, I will present some of our recent achievements in multimodal machine learning, addressing five core challenges: representation, alignment, fusion, translation and co-learning.