Mohamed Seif

Mohamed Seif

Assistant Professor
Department of Computer Science and Engineering
Oakland University
Visting Fellow
Department of Electrical and Computer Engineering
Princeton University
Scholar LinkedIn GitHub Open Positions

About

I am a Tenure-Track Assistant Professor of Computer Science and Engineering at Oakland University. I also am a Visiting Fellow 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. I am originally from Alexandria, Egypt, a Mediterranean city with a rich Egyptian and Greek heritage where Euclid once developed the foundations of geometry.

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
Edge AI
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.

NILE Research Lab

Ph.D. Opening: One funded Ph.D. position is available immediately in the Networked Intelligence, Learning, and Edge Systems (NILE) Research Lab. Students with interests in trustworthy AI, wireless systems, privacy, cybersecurity, edge intelligence, semantic communications, and cyber-physical systems are encouraged to apply. Prospective master's students, undergraduate interns, visiting students, and research interns are also welcome to reach out regarding potential opportunities.

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
NILE Research Lab
Oakland University
Princeton University
Nokia Bell Labs