4 papers
Longer Records, Broader Invariance: The Hidden Scaling Problem in Longitudinal Contrastive Learning
Rameen Mahmood, Xuhai "Orson" Xu, Zachary Beattie +2
Longitudinal data are valuable because people change. Yet the objectives used to learn from these data can inadvertently erase that change. In person-level contrastive learning, ob…
From Packets to Patterns: Interpreting Encrypted Network Traffic as Longitudinal Behavioral Signals
Rameen Mahmood, Omar El Shahawy, Souptik Barua +5
Human behavior is difficult to observe continuously at scale, yet it leaves measurable traces in everyday device use. We test whether encrypted smartphone network traffic---a ubiqu…
Network Traffic as a Scalable Ethnographic Lens for Understanding University Students' AI Tool Practices
Donghan Hu, Rameen Mahmood, Annabelle David +1
AI-driven applications have become woven into students' academic and creative workflows, influencing how they learn, write, and produce ideas. Gaining a nuanced understanding of th…
What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale
Rameen Mahmood, Tousif Ahmed, Sai Teja Peddinti +1
The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountability. This challenge is especially…