1. SAP Labs, India

    I worked on the SAP BI Mobile application as an iOS Developer, where I implemented and intergrated features with Objective-C and Swift.

    Built a custom charting framework from scratch using Core Graphics
    • Built to handle millions of rows of data, custom lazy loading pagination and custom gestures

    Link to App Store

  2. Food Finder

    FoodFinder is an iOS app that helps you find food near your location easily. You get access to pictures, reviews, directions and more. All on a single page and easily accessable. Soon to come: Menus, walking directions and *reservations*

    Link to the App Store

  3. Space Commute

    Space Commute is a iOS game made using GameKit. It is your classic space shooter, with multiple levels. Feeder is a app I am currently wokring on, that will aggregate search results from multiple sources like, Twitter, Facebook, Quora, various news channels etc. Perform some basic analysis and give you overall consolidated information about a topic.

    Link to the App Store

  4. SAP Roambi

    I currently work at SAP in San Diego, on theri ROAMBI Analytics application. I work with C++ on the cross platform core of the application and also on the iOS application with Swift and Objective-C.

    Link to the App Store

  5. Facebook Search

    During my first semester at USC, I created a facebook search app for my Web Technologies class. For the front end, I used a combination of Bootstrap, Angular and Vanilla JS. For the Backend, it was PHP and hosted on AWS.

  6. Recommender Systems

    • Built a Model Based and Item Based Recommender system that used a Hybrid Algorithm, which gave recommendations to users based on their purchase history and features of each item.

    • Built on Apache Spark using Scala

    Link to the Github Repo

  7. Semantic Analysis

    For my Artificial Intelligence class, I built a Sentiment Analysis engine on Mathematica, using a 1/5th test to train ratio. The data was amazon reviews of various products and the goal was to classify the reviews as either positive or negative. The dataset was obtained from Kaggle. The preprocessing of the data included multiple tasks such as converting to lower case, removal of stop words, etc. Followed by the training phase, where we used Neural Nets, Decision trees and Naive Bayes. We compared the results of the various algorithms and produced various visualisations.

  8. Iris Recognition

    During my final year of college at BMSIT, I worked on a project where we looked at the existing methods used in Iris Recognition and looked to find a way to improve how effect it is under situations where there is lot of occlusion.

    Link to GitHub Repo

Hey, I'm Karan

Computer Science Graduate Student

University of Southern California


Various Projects I have worked on.

Recommender Systems

SAP BI Mobile


Food Finder


Space Commute

Iris Recognition

SAP Roambi

Sentiment Analysis

Facebook Search

About Me

I am studying at the University of Southern California, which is in Los Angeles, California. I am pursuing my Masters in Computer Science, and am currently looking for internship opportunities for Summer, 2018.

Contact me