Events Calendar

PhD Defense - Sunghyun Park

Title: Computational Modeling of Human Behavior in Negotiation and Persuasion: The Challenges of Micro-Level Behavior Annotations and Multimodal Modeling
PhD Candidate: Sunghyun Park
Date / Time: Jan. 8th (Friday), 10 a.m. ~ noon
Place: SAL322

Committee:
Prof. Louis-Philippe Morency (Chair)
Prof. Jonathan Gratch
Prof. Aiichiro Nakano
Prof. Shrikanth Narayanan (external)

Abstract

Having a deeper understanding of human communication and modeling it computationally has substantial implications for our lives due to its potential synergistic impact with ever advancing technologies. It is an important step for a technology to be accepted as having effective artificial intelligence. However, human communication is a complicated phenomenon that can take an in-depth multimodal analysis of human behavior to understand, in all of the verbal, vocal, and visual channels. The challenge of multimodality is further complicated by many behavioral cues that are subtle and ambiguous.

The work described in this thesis primarily revolves around computational modeling of human behavior, approaching it largely from the affective and social perspectives. This thesis explores computational behavior analysis and modeling in terms of two important contexts of human communication, one in face-to-face interaction and the other in online telemediated interaction. Firstly, this thesis explores human communication in the context of face-to-face dyadic negotiation to better understand and model interpersonal dynamics that occur during close negotiation interaction. Secondly, this thesis explores human communication in the context of online persuasion, to obtain a deeper understanding of persuasive behavior and explore its computational models with online social multimedia content.

In studying human communication in these two contexts of face-to-face negotiation and online persuasion, this thesis addresses four significant research challenges: large-scale annotations, behavior representations, temporal modeling, and multimodal fusion. Firstly, this thesis addresses the challenge of obtaining annotations of human behavior on a large scale, which provide the basis from which computational models can be built. Secondly, this thesis addresses the challenge of making computational representations of multimodal human behavior, in terms of individual behavior and also interpersonal behavior for capturing the dynamics during face-to-face interaction. Thirdly, this thesis addresses the challenge of modeling human behavior with a temporal aspect, specifically for the purpose of making real-time analysis and prediction. Lastly, this thesis explores multimodal fusion techniques in building computational models of human behavior.

Location: Henry Salvatori Computer Science Center (SAL) - 322

Audiences: Everyone Is Invited

Contact: Lizsl De Leon