Hello, I will be a Security Engineer at Facebook (starting in June 2020) and will work at the intersection of Machine Learning and Cyber Security to detect internal threats to Facebook. I am finishing my Masters in CS from University of Virginia (UVA) and am working as a graduate research assistant in the Mcintire School of Commerce under Dr. Ahmed Abbasi.
I am currently a Masters student at the University of Virginia (UVA) and work as a graduate research assistant at the Mcintire School of Commerce in UVA. I will be joining Facebook as a Security Engineer in June 2020. My interests lie at the intersection of cyber security, machine learning, and business analytics and I have done plenty of research and industrial projects on these topics. In my undergrad, I won over 20 bug bounties from companies like Google, Microsoft, and Apple. Outside of work, I love traveling to foreign countries.
Starting June 2020
Security Engineer, Abuse Team
May 2019 - August 2019
Security Engineer Intern, Abuse Team
Mcintire School of Commerce, UVA
August 2017 - May 2020
Graduate Research Assistant, Center for Business Analytics
Systems and Security Lab, LUMS
February 2016 - May 2018
Research Assistant, Cyber Security and Machine Learning
University of Illinois Urbana-Champaign
December 2016 - August 2017
Remote Research Assistant, Cyber Security
August 2015 - September 2017
Founder, Blog on the applications of Machine Learning in Cyber Security
A Deep Learning Architecture for Psychometric Natural Language Processing
Research project in collaboration with folks at UVA, University of Arizona, and Georgia Tech where we proposed a deep learning architecture encompassing multiple components for enhanced text classification performance in health analytics. Paper has been published in TOIS 2019(ACM Transactions on Information Systems) and I am the first author.
A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X
Research project in collaboration with folks at LUMS and UIowa where we designed a genetic algorithm capable of obfuscating text against stylometric authorship attribution classifiers. The paper has been published in PETS 2019 (Privacy Enhancing Technologies Symposium) and I am the second author.
Bringing the Kid back into YouTube Kids: Detecting Inappropriate Content on Video Streaming Platforms
Research project where we developed a deep learning based system encompassing Convolutional and Recurrent Neural Networks that was able to identify inappropriate and explicit videos on Youtube Kids and Youtube. The paper has been published in ASONAM 2019 (International Conference on Advances in Social Networks Analysis and Mining) and I am the second author.
The Browsers Strike Back: Countering Cryptojacking and Parasitic Miners on the Web
Developed a machine learning pipeline using random forests that was responsible for detecting mining activity in websites. Applied the system in real world and found numerous mining websites in the wild. The paper has been published in IEEE INFOCOM 2019 and I am the third author.
Finding Needles in a Haystack: Deep Learning for Rare Adverse Event Detection
Used deep auto encoders to build an anomaly detection system capable of finding rare adverse events caused by drugs. The paper has been published in INFOMS Data Science Workshop 2018 and I am the first author.
It's All in the Name: Why Some URLs are More Vulnerable to Typosquatting
Research project in collaboration with folks at LUMS and UIUC where I developed a machine learning pipeline to detect URLs vulnerable to typosquatting and suggested automated steps to rectify them. The paper was published in IEEE INFOCOM 2018 and I am the third author.