Current Projects:

• Systematic survey on Emotions in social decision-making

• Affective video classification

• Affective Interaction

Past Projects:

• User perspectives on emotionally aligned social robots for older adults and persons living with dementia
Modelled socially aligned pet robots using Affect Control Theory

• A Biologically-Inspired Neural Implementation of Affect Control Theory
Worked on Neural implementation of Affect Control Theory (ACT) using Nengo library for brain simulation, based on Neural Engineering Framework

• Virtual Human in Prisoner's Dilemma using Affect Control Theory
Modelled ACT in virtual human for interaction in prisoner's dilemma game setting

• AI agent in an Intelligent Tutoring System
Worked on an AI agent in an Intelligent Tutoring System

• Constrained POMDP Solver
In many situations, it is desirable to optimize a sequence of decisions by maximizing a primary objective while respecting some constraints with respect to secondary objec- tives. Such problems can be naturally modeled as constrained partially observable Markov decision processes (CPOMDPs) when the environment is partially observable. In this work, we describe a technique based on approximate linear pro- gramming to optimize policies in CPOMDPs. The optimiza- tion is performed offline and produces a finite state controller with desirable performance guarantees. The approach outper- forms a constrained version of point-based value iteration on a suite of benchmark problems.

• Emotionally-aware Cognitive Virtual Human Assistant
An exploratory study conducted to understand how audio-visual prompts are understood by people on an emotional level as a first step towards the more challenging task of designing emotionally aligned prompts for persons with cognitive disabilities such as Alzheimer's disease and related dementias (ADRD). Persons with ADRD often need assistance from a caregiver to complete daily living activities such as washing hands, making food, or getting dressed. Artificially intelligent systems have been developed that can assist in such situations. This paper presents a set of prompt videos of a virtual human 'Rachel', wherein she expressively communicates prompts at each step of a simple hand washing task, with various human-like emotions and behaviors. A user study was conducted for 30 such videos with respect to three basic and important dimensions of emotional experience: evaluation, potency, and activity. The results show that, while people generally agree on the evaluation (valence: good/bad) of a prompt, consensus about power and activity is not as socially homogeneous. Our long term aim is to enhance such systems by delivering automated prompts that are emotionally aligned with individuals in order to help with prompt adherence and with long-term adoption