I am a Ph.D. Computer Science student in the Principles of Expressive Machines (POEM) Lab under the advisement of Dr. Chris Martens, at NC State University. I model and study large populations of humans by simulating small-scale social interactions that take into account real-world geographic constraints, logistics, lifestyles, and social influence dynamics. With my research I aim to support a variety of applications, such as examining the implications of social science theories and developing playable, explorable story worlds. I believe my research would afford increased entertainment, immersion and replayability for users.
Most recently, I was a PhD Summer Research Intern at IBM Research. I designed and developed a simuluation that modelled the spread of COVID-19 amongst employees in a workplace using artificial intelligence and agent-based simulation techniques with the goal to mitigate the viral spread amongst employees. Before that, I worked as a Research Lab Associate at Disney Research Pittsburgh in the Narrative Intelligence Lab. Before that I completed my M.S. in Computer Science from Georgia Institute of Technology under the advisement of Dr. Mark Riedl in the Entertainment Intelligence Lab.
PhD in Computer Science (Specializing in Narrative Intelligence), 2017 - present
North Carolina State University
MS Computer Science (Specializing in Interactive Intelligence), 2014 - 2016
Georgia Institute of Technology
B.Engg. Computer Engineering, 2007 - 2011
University of Mumbai
Bolstered by a growing interest in simulating believable non-player characters (NPCs), work on NPC models has spanned topics such as planning, procedural storytelling, decision-making, and social dynamics. However, research groups work in isolation, designing and discussing their character models with disparate approaches, often using project-specific terminology. This makes it challenging to identify, classify, and accumulate existing knowledge. It is our position that since modelling of virtual characters has become an integral part of the scientific practice in our field, we must develop a common taxonomy to discuss these models. With this goal in mind, we conduct an in-depth analysis of a selection of projects, categorizing existing agent social interactions, and comparing results from research-based and commercial social simulation works in the entertainment domain. We conceptualize a taxonomy that classifies agent interactions by their social behaviours, inter-agent communication, knowledge flow, and the change in their relationships. We posit such a taxonomy would allow scientists to reproduce and evaluate existing models, collaborate on standards, share advances with other researchers and practitioners, allow for better communication and methodologies developed for new techniques, and allow for a more rigorous model-to-model analysis.
The concept of audience interactivity has been rediscovered across many domains of storytelling and entertainment—e.g. digital games, in-person role-playing, film, theater performance, music, and theme parks—that enrich the form with new idioms, language, and practices. In this paper, we introduce a Spectrum of Audience Interactivity that establishes a common vocabulary for the design space across entertainment domains. Our spectrum expands on an early vocabulary conceptualized through co-design sessions for interactive musical performances. We conduct a cross-disciplinary literature review to evaluate and iterate upon this vocabulary, using our findings to develop our validated spectrum.