I am a fourth year PhD student at the Internet Research Lab at University of Iowa. My advisor is Prof. Zubair Shafiq.

I conduct research on web security and privacy with an emphasis on privacy enhancing technologies. Specifically, my research focuses on improving the effectiveness of ad and tracker blocking technologies. Online advertisements have become an attractive target for various type of abuses, such as online tracking. Adblockers serve as a tool to protect user privacy by blocking these advertisements. A vast majority of adblockers rely on filter lists to block advertisements. However, filter lists suffer from two major problems. First, they are manually curated with informal crowdsourced feedback and thus lack precision and accuracy. Second, manual curation adds an update overhead and make filter lists susceptible to evasion attacks.

In my research, I address these problems using machine learning approaches.

As an intern at Brave. I instrumented Chromium web browser to capture the rendering of a webpage. Webpages are parsed and represented as DOM trees in modern browsers. The DOM tree captures relationships among HTML elements (e.g. parent-child, sibling-sibling). In my instrumentation, we enrich this existing tree-representation with additional information about the execution and communication of the page, such as edges to capture JavaScript's interactions with HTML elements, or which code unit triggered a given network request. These edge additions transform the DOM tree to a graph.
The graph representation of page execution tracks changes in the website's HTML structure, network requests, and JavaScript behavior. Because the graph contains information about the cause and content of every network request and DOM modification during the page's life cycle, the graph allows for tracing the provenance of any change or behavior back to either the responsible JavaScript code unit, or, in the case of initial HTML text, the browser's HTML parser. The contextual information captured by the instrumentation far exceeds what is available in the existing literature.

As an intern at Microsoft, I wrote a technical report describing the current landscape of ad and tracker blockers. I evaluated the state-of-the-art ad and tracker blocking solutions proposed in research, deployed ad and tracker blocking browser extensions, and current state of ad and tracker blocking in mainstream browsers. In addition, the report also discussed policies and consortiums around ad and tracker blocking solutions.
In light of developments around ad and tracker blocking and the current landscape of these privacy enhancing technnologies, the report outlined how and what an adblocker for Microsoft Edge would look like.

As a solution analyst at LMKT Corporation., I worked on a number of projects. The most prominent projects were:
(1) PTCL Smartlink: A mobile app for calling and instant messaging. It was packaged for PTCL (Pakistan Telecomunication Company Limited).
(2) V-Govern: An e-governance solution. I added search functionality to the product with configurable similarity models.
(3) RAFM (Revenue Assurance and Fraud management): A reporting dashboard. It provided near real time data analytics to monitor revenue and fraud critical situations by processing over one billion CDRs on daily basis.