I am a Graduate Student at Stony Brook University, doing my Masters in Computer Science. I am currently doing my research on Computer Vision under the guidance of Prof. Roy Shilkrot @The Human Interaction Lab. I am also interested in IoT Computer Networks, and have worked on the same for scaling IoT Pub/Sub Networks, here's a paper which I worked on last summer - A Highly Resilient and Scalable Broker architecture for IoT applications which was published in COMSNET 2018. I also have quite some experience as a Full Stack Developer and I generally work on backend technologies as well as love automation.Download Resume
I am an outgoing Software Engineer seeking Full Time Opportunities to leverage my technical and professional expertise. I have worked as a Full Stack Developer in Quintype & ThoughtWorks in the few years and have helped build scalable platforms for different products. At Quintype I have been working on a SaaS solution for Publishers which helped them get online at scale without the pain of building everything on their own. I was part of the team responsible for the core product and I invested my time working on different fascets from scalable APIs to Analytics, where I solved critical problems such as handling millions of users which involved huge amounts of read and write operations to various caching solutions. I was also part of handling one of the customer BloombergQuint which consume our APIs for their publishing solutions and helped them build the backend & front-end components necessary for the same. I was also part of building Ruby gems & Node.js modules for our core APIs to be consumed seamlessly. I am looking forward to challenging full time opportunities.
Stony Brook University
2017 - 2018
Masters in Computer Science
GPA - 3.88
Stony Brook University
2017 - Present
2016 - 2017
Full Stack Engineer
2014 - 2015
TCS Innovations Labs
2013 - 2014
This is a project I am working on at the Human Interaction Lab @Stony Brook University under the guidance of Prof. Roy Shilkrot. The primary idea is to detect certain dark patterns which may/maynot be able to skew user opinion as well as influence user actions by using different types of design techniques. This is being used almost everywhere and in almost all most heavily used websites. Our goal in this project is the detection of such dark patters for websites and show it to users for more awareness. Here's a glimpse of the existing dark patterns - Dark Patterns Hall of Shame
Deep Learning approach for Monocular Visual Odometry using Deep Learning; Inspired from the paper DeepVO.
3D printed an arm band for a raspberry pi with a gyroscope connected. Used DTW to detect quality of workouts based on the data from the sensors and send user feedback using an iOS app. Won the Most Innovate Idea Award at [email protected]
Developed an online real-time one-to-one tutoring platform to empower students for on-demand learning.
Analyzed and visualized NYPD Motor Vehicle Collision dataset for accidents in New York City(1 Million rows). Used stratiﬁed sampling using K-means for data reduction and Principal Component Analysis(PCA) for dimension reduction. MongoDB was used for storing data, D3 and deck.gl(WebGL) were used for creating visualizations