I am a first year PhD student at the iSchool. My research interest is in data science and artificial intelligence. I am broadly interested in solving problems related to knowledge mining and building artificial intelligence with reasoning.
I completed an MS in Computer Science from Syracuse University with coursework focused in artificial intelligence, machine learning, and data mining.
As a graduate student, I have received opportunities to work in multiple interdisciplinary research. Currently, I am working as RA (Research Assistant) in a research project “Modeling Dynamic Contextualized Contagion”. Our research focuses on understanding and modeling how social pressure and coherence affects on how people adopt information.
Earlier, I was RA for “TRACE” (Trackable Reasoning and Analysis for Collaboration and Evaluation) project. I worked in a interdisciplinary team for behavioral data analysis to understand how user interaction with software may affect quality of reasoning in decision-making while solving problems.
In other research, I worked in a team to understand fake news. Our research focused on detection of fake news by assessing its credibility.
Previously, I was also involved in a research project to understand user behavior during TV advertisements. In this research project, we applied artificial neural network(ANN) models to predict drop off in viewership during commercial breaks. Our paper “TV Program-Ad Genre Congruence and Ad Avoidance: Applying Neural Networks to Assess Effects” won first place in the Broadcast Education Association’s (BEA 2019) Open Paper competition.