Mohamed Seif

Mohamed Seif

Researcher in Wireless Systems and Machine Learning
Electrical and Computer Engineering
Princeton University
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About

I am a researcher in the Department of Electrical and Computer Engineering at Princeton University. I received my Ph.D. in Electrical and Computer Engineering from the University of Arizona.

My research lies at the intersection of information theory, machine learning, wireless communications, and signal processing. I develop mathematically grounded methods for privacy-preserving and communication-efficient AI systems, with interests spanning distributed learning, graph and network data analysis, edge intelligence, and trustworthy generative AI.

More recently, I have been interested in statistical learning problems for large language models, with a focus on designing fine-grained watermarking algorithms that enable edit detection and strengthen content integrity. I also work closely with industry collaborators to connect theory with practice, including projects on private wireless network analytics, Wi-Fi sensing privacy, and AI-native communication systems.

Research Interests

Differential Privacy
Wireless Systems
MIMO / OFDM / Wi-Fi
Distributed Inference
Graph Learning
Trustworthy AI
Multimodal AI
Statistical Learning

Industry Collaborations

Nokia Bell Labs

Artificial Intelligence Research Lab

I have collaborated with Nokia Bell Labs on privacy-preserving network analytics and scalable graph learning methods. This includes work related to differentially private spectral clustering and efficient algorithms for network data analysis, bridging statistical learning theory with practical telecom systems.

MediaTek

6G Wireless Research Center

I have been collaborating with MediaTek on privacy-preserving wireless sensing and next-generation AI over wireless systems. Recent efforts include protecting human activity signatures in Wi-Fi CSI feedback, studying privacy-utility tradeoffs in sensing pipelines, and developing practical mechanisms for trustworthy wireless intelligence.

News

Selected Recent Papers

Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management
IEEE Network, 2026
Protecting Human Activity Signatures in Compressed IEEE 802.11 CSI Feedback
IEEE DySPAN, 2026
On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models
IEEE Transactions on Network Science and Engineering, 2026
Deep Learning-based Image Compression for Wireless Communications: Impacts on Robustness, Throughput, and Latency
Nature (npj Wireless Technology), 2026
Collaborative Inference over Wireless Channels with Feature Differential Privacy
IEEE Journal on Selected Areas in Communications, 2025

Recent Talks

Contact

Email
mseif@princeton.edu