Susheel Suresh

I am a fourth year undergraduate student at P.E.S Institute of Technology, studying Computer Science. I also work as a student researcher at the Centre for Cloud Computing and Big Data (CCBD).

Recently, I worked as a research intern at INRIA, Paris with Prof. Catuscia Palamidessi and in the summer of 2015, I was a summer research fellow at IIT Bombay under Prof. R.K Shyamasundar. I have also spent time as a student intern at Nokia Developer Lab, Bangalore. I am fortunate to have had the guidance of Kostas Chatzikokolakis (CNRS) and Narendra Kumar (TIFR) during my research internships.

Email  /  CV  /  LinkedIn  /  Github  /  Google Scholar

News

  • 3D Image Analysis Paper accepted @ ICVGIP 2016. Checkout the program schedule.
    Quick intro slides.
  • Will be at INRIA Saclay for research internship this summer.
  • Presented the paper on Secure Data Sharing @ ICISS 2015, Kolkata India. The presentation is available here.
    A video demo of the attack and it's mitigation is here.

Research

My interests are primarily focused on three interleaved areas: Machine Learning, Computer Vision and Data Science. I have worked on segmentation, object detection, 3D reconstruction and application of these concepts to real world problems. In the security and privacy field, I have worked on Differential Privacy (it's application to location privacy) and Information Flow Control.
Apart from these, I like to explore other areas in computer science like Databases and Distributed Computing.

Towards Quantifying the Amount of Uncollected Garbage through Image Analysis

Susheel Suresh, Tarun Sharma, Prashanth T.K., Subramaniam V, Dinkar Sitaram & Nirupama M.

ICVGIP 2016, December 18-22, 2016

paper | abstract | poster | code

Civic authorities in many Indian cities have a tough time in garbage collection and as a result there is a pile up of garbage in the cities. In order to manage the situation, it is first required to be able to quantify the issue. In this paper, we address the problem of quantification of garbage in a dump using a two step approach. In the first step, we build a mobile application that allows citizens to capture images of garbage and upload them to a server. The second step, in the back-end performs analysis on these images to estimate the amount of garbage using computer vision techniques. Our approach to volume estimation uses multiple images of the same dump (provided by the mobile application) from different perspectives, segments the dump from the background, reconstructs a three dimensional view of the dump and then estimates its volume. Using our novel pipeline, our experiments indicate that with 8 different perspectives, we are able to achieve an accuracy of about 85 % for estimating the volume.

Enforcing Secure Data Sharing in Web Application Development Frameworks Like Django Through Information Flow Control

Susheel Suresh, N. V. Narendra Kumar, R. K. Shyamasundar.

Conference Version: ICISS 2015, December 16-20, 2015.
Published in LNCS Information Systems Security, Volume 9478

paper | abstract | code

The primary aim of web application development frameworks like Django is to provide a platform for developers to realize applications from concepts to launch as quickly as possible. While Django framework provides hooks that enable the developer to avoid the common security mistakes, there is no systematic way to assure compliance of a security policy while developing an application from various components. In this paper, we show the security flaws that arise by considering different versions of an application package and then show how, these mistakes that arise due to incorrect flow of information can be overcome using the Readers-Writers Flow Model that has the ability to manage the release and subsequent propagation of information.

Journal Version (Extended): Submitted to Software: Practice and Experience, Wiley

Key Projects

Real Time Video Content Based Contextual Advertisement

Susheel Suresh, Tarun Sharma

"What the Hack!" SAP Labs India Hackathon, 2015

tech report | abstract | code

With the advent of intermediary commercial ad-networks in charge of optimizing ad selection with a twin goal of increasing revenue and improving user experience, it is preferable to have ads relevant to the web content than generic ones. This method of advertising is popularly known as Contextual Advertising. Advertising has become ubiquitous in the internet community and more so in the ever-growing and popular online video delivery websites (e.g. YouTube, Vimeo). Video advertising is becoming increasingly popular on these websites as it has the most user engagement levels.
    Our novel method automatically associates ads from an advertisement database and seamlessly recommends them at the appropriate time within each individual video in real-time. If a context switch occurs in the video a different relevant ad is placed. Ads are in the form of banners (containing visuals and text) and are placed beside a playing video. Unlike most video sites which treat video advertising as general text advertising by displaying general, contextually irrelevant video ads at the beginning, end and beside a playing video, our approach aims to recommend contextually relevant ads for a video stream taking into account the audio/speech aspect. Specifically, given a Web page containing an online video, our method is able to extract keywords/text in the form of phrases from the video segment, perform entity extraction on the phrase, detect and resolve ambiguous entities and finally place relevant ads.

Applied Machine Learning Course Project

Advisor: Prof. Anantharaman P.N. , Jan-May 2016

Databases Course Project

Advisor: Prof. Srinivasa Murthy , Jan-May 2015

Operating Systems Course Project

Advisor: Prof. Subramaniam V , August-December, 2015

Algorithms Course Project

Advisor: Prof. Channa Bankapur , Jan-May 2015

Data Structures Course Project

Advisor: Prof. Srinivasa Murthy , August-December 2014


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