Ritik Roongta

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Glacier canyon, Grindelwald

I am a Web Security Researcher at Palo Alto Networks, working on the Advanced URL Filtering team to detect malicious web content at scale. I recently completed my Ph.D. in the Computer Science Department at NYU Tandon School of Engineering, advised by Prof. Rachel Greenstadt and Prof. Brendan Dolan Gavitt. My research sits at the intersection of web privacy measurement and machine learning, with a particular focus on developing solutions to measure privacy in online systems and using AI to mitigate privacy risks.

I earned my B.Tech. in Computer Science and Engineering from the Indian Institute of Technology Bombay in 2021. For my undergraduate thesis, I worked on large-scale fuzzing of network programs to uncover vulnerabilities, under the supervision of Prof. Giovanni Vigna and Prof. Christopher Kruegel. My PhD work focused on deceptive online content, from how adblocker users are exposed to problematic ads to detecting malvertising at scale. I also studied how large language models can be used to generate propaganda, and how such content can be detected and mitigated.

I have had the support of various seniors and mentors who have guided me throughout my academic life to pursue a research career. To continue the legacy, I am always available to help ambitious students who could benefit from my experience. Please read these guidelines before sending me an email :)

Email: firstname.r [at] nyu.edu firstname+lastname [at] gmail.com

news

Jun 04, 2025 Presented a poster on problematic content in Acceptable Ads at Google Ad Privacy Day in San Francisco
Jun 01, 2025 Our paper on Understanding Problematic Content in ‘allowed’ Advertisements got accepted in PETS 2025.
Apr 04, 2025 Presented a poster on propaganda generation by LLMs at NYC Privacy Day at Columbia University.
Aug 15, 2024 Finished my summer intern at CISPA, Germany to work on differentual treatment of adblocker users, advised by Prof. Ben Stock.
May 04, 2024 Received fellowship to attend CISPA Summer School on Usable Security.