Sulagna Mukherjee

Ph.D. Candidate, USC@NSL

  • (2017- ) Ph.D. in Computer Science, University of Southern California, USC.
  • (2015) B.Tech. in Computer Science and Engineering, Institute of Engineering and Management (IEM), Kolkata.

Biography

I am a Ph.D. candidate in the Networked System Lab (NSL) at University of Southern California. I am lucky to be advised by Prof. Barath Raghavan. My primary research interests have to do with Security, Human-Computer Interactions (HCI) and System Design with a focus on user centric design approaches. I am also interested in understanding how the information ecosystem is evolving with time and impacting us individually and societally.

Prior to joining USC, I completed my Bachelors (B.Tech) in Computer Science and Engineering at the Institute of Engineering and Management (IEM), Kolkata. I was advised by Dr. Himadri Nath Saha and worked on MANETs.

Ongoing Projects

The Ghost Trilemma: We look at the underlying properties that are necessary to distinguish a human-controlled account from a bot account across social media platforms. We demonstrate the impossibility of verifying these properties simultaneously online in a fully decentralized setting. We then propose a framework and suggest sketches of practical, incrementally deployable schemes to achieve an acceptable tradeoff working within the constraints.

Understanding and Shaping Usable Security as Rituals: Unintentional human error is the root cause for the majority of data breaches occurring globally. We apply the concept of rituals as seen in the human society to inculcate better security practices in individual users. Our focus is on people who are not formally trained in Computer Science principles but are required to regularly use machines for work.

Reviewing the Evolving Nature of Internet Shutdowns: An explorative study looking at the relationship between internet shutdowns and today’s global information ecosystem, within the context of evolving attitudes towards censorship.

Teaching Experience

Teaching Assistant at USC
Course: CS 570: Analysis of Algorithms - Spring 2019, Summer 2018-19 Instructor: Shawn Shamsian
Course: CS 270: Introduction to Algorithms and Theory of Computing - Fall 2018, Instructor: Michael Shindler

Work Experience

Graduate Research Assistant (August 2019 - present)
Department of Computer Science, University of Southern California.
Advisor: Barath Raghavan

Solution Integrator (Role: System Administrator in Linux) (September 2015 - July 2017)
Ericsson India Global Services PVT. Ltd, Kolkata.

Summer Research Intern (May 2014 - Aug 2014)
Indian Statistical Institute, Kolkata.
Mentor: Sasthi C. Ghosh

Awards

  • Graduate Student Annenberg Fellowship, 2017 - 2022.
  • Received USENIX Security diversity grant, 2021.
  • Grace Hopper Scholarship, 2018.
  • Indian Academy of Science Fellowship, 2014.

Publications

  1. Improving User Coverage Through Resource Aware Handoff Management in Heterogeneous Networks
    Singh, Rohit, Mukherjee, Sulagna, and Ghosh, Sasthi C.
    In Proceedings of the 13th International Conference on Advances in Mobile Computing and Multimedia 2015

    In heterogenous network, there are possibilities of horizontal handover between similar type of transmitters as well as vertical handover between different type of transmitters. Also, an user may be served by a single transmitter under the hard handover or may be served by multiple transmitters simultaneously under the soft handover. Considering all these, an user may have several available options to connect with. We have developed a greedy algorithm which allocates every user to their respective most beneficial link in terms of power requirement and traffic cost. We have compared our proposed algorithm with two existing handoff decision algorithms of which one is based on received signal strength indicator measurements and the other is based on selecting the least congested transmitter. Through simulation we have shown that the proposed algorithm outperforms both these algorithms in terms of user coverage and the average data rate provided to the users.