Description

In recent times, rapid advances in machine learning through neural networks have enabled increasingly accurate and sophisticated detection of a wide range of verbal and non-verbal aspects of human behavior. This includes robust conversational speech recognition, detection of non-verbal vocalizations (e.g., laughter, sighing, backchannels) as well recognition of other individual behavioral patterns (e.g., gaze, facial expressions, geographical movement, etc). This has opened the door for many human-computer interaction based applications, many of which seek to augment human intelligence and to assist humans in their daily lives. At the same time, there remain significant behavioral signal processing challenges, like recognizing natural emotions in the same way that humans do, and designing systems and interfaces which combine these improved behavioral inferences in ways that solve real problems for people.

The event features the following expert speakers:
- Prof Sandy Pentland
- Prof Carlos Busso
- Prof Emily Mower Provost 

We will dig deep into the fundamental challenges in this exciting field and will report on some of the latest research which is solving some of these challenges using novel and cross-disciplinary approaches.

Event is limited to 100 registrants. Register now to save your spot.