At Oracle, we're building AI Apps, transforming enterprise applications with connected intelligence. I contribute to development of core machine learning modules as well as their deployment on Kubernetes.
I've previously led an internal search service development initiative, trying to solve some challenging information retrieval problems.
Before this, I've led development of command line tools for managing Oracle Engagement Cloud applications as portable metadata. I've earlier created tools to automate generation of Oracle JET applications.
This gave me exposure to some of the most important technologies & services in distributed systems like Hadoop, Spark, HBase, Hive, Kylin, Ambari, HDP, Zookeeper, Tomcat, etc.
This is also where I met one of my best friends, Docker, who was a 6 month old kid back then. :)
We also predicted engagement scores for websites by analyzing features like visibility of call to action, contrast ratios and link densities using OpenCV library in Python.
This gave me exposure to the power of MEAN stack and also to an interesting domain of image processing.
I've worked with Dr. Sourangshu Bhattacharya on Large Scale visual recognition (precisely dense image captioning) using Deep Learning on GPUs and Apache Spark.
Along with this, I worked with Spark community for efficient implementation of CNN and RNN in Apache Spark.
While working on this project, I also came across using convolution neural network to mix the content and style from different images. So, I implemented that and the results are very encouraging.
Predicting gender and age of the author using semantic features.
This project involves predicting personal information of authors like gender, age etc by training classifiers using content based and semantic features extracted from a KB like Wikipedia.
There is a lot of contextual difference between blogs written by different people. In this project with Prof. Pawan Goyal, we explored those contextual differences to predict age and gender of the author of a text.
In Twitter, mentioning (or tagging) users can be considered as an effective way of spreading an information beyond the reach of the followers.
The objective we had is to model retweeting patterns based on historical data of user interactions, inherent topical similarity between tweets reaching current user and tweets of his top K friends, and the nature of interactions of a user with his neighbours in the social network.
Point Processes have recently received significant attention from researchers in social media analytics. We have modelled Hawkes Process for online social networks to investigate retweeting patterns.
Implemented a complete online book store from registration to checkout including search, reviews and recommendations.
Implemented an information retrieval system for Bosch power tools using Apache Nutch and Apache Solr. Did sentiment analysis on the reviews to give better search results than those using standard techniques.
Have you ever found yourself googling about the new term your friend just tossed in a chat? Or searching for a video comparison to win a iPad vs Surface battle with your buddy. Don't need to do that any more.
Kibitz is an interactive chat application, I built along with my friend Arkanath, which automatically finds out the context of the chat and gives real time feeds of the related content from the web, including videos, definitions, news, etc.
Moreover, it also personalises your chat experience by changing the background based on the chat content.
It is a utility software for graph plotting, where a user enters the equation and variour other parameters, to get the corresponding graph, that can be exported to various formats.
An old school game, played by connecting the dots to make squares. Made it for Windows Phone, with many new extensions.
A tool for Computer Aided Software Engineering. It can be used to create(draw) data flow diagrams, modules, hierarchies etc. for automating various activities associated with structured software analysis and design.
I believe that everyone should learn coding because it teaches you how to think. In my leisure time, I oftenn end up solving some problem which consumes more of my time than required or solving problems which I have seen others facing.
Research is fun. I'm interested in Machine Learning, Computer Vision, Deep Learning and other areas of Artificial Intelligence.
You can often find me reading something in these fields. Find more about my research work here.
I love music. I have a huge collection of songs. I try to keep it organised in best quality possible. I like rock and metal and driven by the solos of Mark Trimonti and the likes.
I have always loved taking photos. I learn something new everyday. I believe in the power of visuals. It's a language we use to see into each other's lives, one frame at a time.
Yes, you may call me a geek. I always talk technical. People come to me for buying advice whenever they wanna buy any new gadget.
B.Tech. in Computer Science and Engineering
I completed various graduate level courses on Information Retrieval, Machine Learning and AI as electives.
I was also actively involved in research on Artificial Neural Network, specifically CNNs and RNNs.