I am a King’s India scholar and a Ph.D. candidate at King’s College London. I am working towards my Ph.D. in computer science at Department Informatics, King’s College London , under the guidance of Dr. Nishanth Sastry.
My primary interests are complex networks and quantifying their emergent behaviours using the up and coming representation learning methods. My current projects deal with affective analysis of images for urban informatics, quantificaiton of engagement metrics in social media and quantification of notions of support in online communities.
I have graduated as a Masters of Science (M.S.) from University of California, Santa Barbara with a double major in Signals Processing and Networks, and a Bachelors of Engineering (B.Eng) in Electronics from University of Pune, India.
Ph.D. in Computer Science, 2018-19 (expected)
King's College, London, U.K.
Master of Sciences (M.S) with emphasis on Signals Processing and Networks, 2012
University of California, Santa Barbara, C.A. , U.S.A
B.Eng, Electronics Engineering, 2008
University of Pune, India
The up and coming formats of micro videos and stories, have allowed a new form of expression for social media users. The format however competes with the other contemporary formats of simple images and videos. The aim was to understand what sets this new format apart, and predict engagement patters using computational methods.
We as a soceity have accepted the internet as a way to express the most intimate secrets about us. Sometimes the upside of this is the availibility of a wider audience who can help one another at times of mental or physical distress. In this project, I attempt to quantify behavioural and conversational motifs of the notion of support through the analysis of a wide range of data streams.
Buildings and neighbourhoods speak. They speak of egalitarianism or elitism, beauty or ugliness, acceptance or arrogance. The aim of this project is to celebrate egalitarianism, beauty, and acceptance by beautifying the entire world, one Google Street view at a time. All of this is done by designing state-of-the-art technologies that make it possible to smarten a street view and read inside the Deep Learning ‘black box’.
The project looks at how hyper-partisan news sources in the U.S. interact with each other and with their consumers. We work with a professionally curated seed dataset from Buzzfeed and analyse it using Bit.ly click throughs and Alexa traffic refers to understand how these hyper-partisan news sources create an echo-chamber of themselves.