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cs.LG2026
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…
cs.LG2026
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…
cs.LG2025
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…