Eyhab Al-Masri

Associate Professor
Department of Computer Science and Systems
University of Washington (Tacoma)
Email:
Research Profile: Google Scholar Profile

My research at the Networked and Multi-Agent Intelligent Systems Lab (NMAIS) focuses on multi-agent orchestration, technical AI governance, privacy-preserving federated architectures, trustworthy edge intelligence, and secure distributed systems, including:

  1. Multi-Agent Orchestration & Agentic Systems
    • Automated reasoning and discovery for Model Context Protocol (MCP) tools, APIs, and intelligent services.
    • Inter-LLM communication, ranking divergence, synchronization, and workflow automation across distributed reasoning systems.
  2. Privacy-Preserving Federated Architectures
    • Federated foundation models, privacy-preserving AI, and collaborative learning across distributed scientific sites.
    • Correlation-aware differential privacy, MIC-DP, and user-centric local differential privacy for high-dimensional data.
  3. High-Performance Edge Computing & Remote Operations
    • Resource allocation, task scheduling, energy-efficient fog computing, and Edge AI workloads for constrained environments.
    • Lab-as-a-Service, remote scientific infrastructure, IoT protocols, sensing technologies, and smart-environment telemetry.
  4. Trustworthy AI-for-Science & System Governance
    • Structure-aware aggregation, poisoned-update filtering, anomaly detection, and real-time diagnostics for mission-critical systems.
    • Policy-based access control, IoT forensics, secure microservices, and end-to-end privacy and security risk detection.

I am an Associate Professor in the Department of Computer Science and Systems, School of Engineering and Technology at the University of Washington Tacoma. I direct the Networked and Multi-Agent Intelligent Systems Lab (NMAIS), where my research focuses on multi-agent and agentic AI systems, edge intelligence, privacy-preserving architectures, IoT systems, service computing, and secure distributed systems. I currently serve as an Associate Editor for IEEE Transactions on Services Computing. Previously, I worked in the David R. Cheriton School of Computer Science at the University of Waterloo. I completed my Ph.D. degree in Computer Science at the University of Guelph, where my doctoral dissertation received an honorable mention from the Association for Information Science and Technology (ASIS&T). I also hold an M.S. degree in Electrical Engineering and a B.Sc. degree in Computer Engineering from Florida International University.


Teaching

Autumn 2026

TCSS 559 Services Computing
TCSS 460 Client/Server Application Programming

Winter 2027

TCSS 573 Internet of Things

@ University of Washington Tacoma

TCSS 445 Database Systems Design
TCSS 360 Software Development And Quality Assurance Techniques
TCSS 460 Client/Server Programming For Internet Applications
TCSS 591 Research Seminar (Distributed Computing)
TCSS 559 Services Computing
TCSS 573 Internet Of Things
TEE 453 Digital Signal Processing

Research Publications

IEEE BigData 2026 | Seventh International Workshop on Scalable Agentic and Generative Edge Intelligence for IoT (SAGE 2026)

Organizer & Founder: 7th International Workshop on Scalable Agentic and Generative Edge Intelligence for IoT (SAGE 2026)


Mentoring

I actively work with students and collaborators. The list below highlights our collective achievements, including peer-reviewed publications authored by my students.

PhD Students
  • Wenjun Yang (2022 - Present)

 

MCSS Students (Capstone, Thesis & Independent Study)
  • Headley Brissett (2026 - Present)
  • Ruby Plangphatthanaphanit (2026 - Present)
  • Sopheanith Ny (2026 - Present)
  • Wiratmika (2026 - Present)
  • Yan Wu (2026 - Present)
  • Ari Yin (2026 - Present)
  • Ishwarya Narayana Subramanian (2025 - Present)
  • Preethika Pradeep (2025 - Present)

 

Alumni

MCSS (Thesis and Capstone) Alumni
Preethika Pradeep Energy-Optimized Scheduling for AIoT Workloads Using TOPSISa> (Best Presentation Award @ AIIoT 2025)
Sashank Kumar FLCMed-TAD: An Anomaly-Aware Federated Learning Approach for EV Load Forecasting
Aaron Chen Optimizing Computational Resource Efficiency with MCDM
De-Li Cheng EdgeWiseLLM: Efficient LLM Inference for Edge AI
Sri Vibhu Paruchuri FogScheduler: A resource optimization framework for energy-efficient computing in fog environments
David Orriss Observing Security in Microservices with Distributed Tracing
Habiba Mohamed A Multi-Objective Approach for Optimizing Edge-Based Resource Allocation Using TOPSIS
Joe Chou Detecting Security and Privacy Risks in Microservices End-to-End Communication Using Neural Networks
Yifeng Liu Evaluating the Reliability of MQTT with Comparative Analysis
Alina Saduova A Self-Adaptive IoT-based Approach for Improving the Decision Making of Active Surgical Robots in Hospitals
Ankit Singh A Hybrid Brain Computer Interface for People with Neurological Disorders
Deepika Patil Seamless Service Migration & Offloading across Mobile Edge Computing (MEC) Environments
James Olmsted FogWeaver: A Multi-Objective Optimization Strategy & Characterization of Hybrid Internet of Things Environment
Prashanti Pathak Using TOPSIS for Enhancing Service Provisioning Across Fog Environments
Harnidh Kaur An Observability Framework for Predicting the Behavior of IoT Systems
ShrustiShree Sumanth Package Theft Detection on Wyze Cameras
Deeksha Rao Gorige Investigating Privacy and Security using Distributed Tracing Tools
Vaishali Girdhar Enhancing Fault Tolerance of Fog-Based IoT Systems (Spring 2020, Autumn 2020)
Sreenavya Nrusimhadevara CSRF A Ranking Approach for Microservices in Service Composition
Ayush Bandil VTA-IH: A Fog-based Digital Forensics Framework
Richard Brun MQTT Performance Across Heterogeneous IoT Platforms
Surbhi Goya Real-Time Observability of Distributed Systems
Tejashri Joshi A User-Centric NFV Service Recommender System
Wenjun Yang Assessing Data Quality for Internet of Things (IoT) Systems
Hu Zhao A User-Centered Handoff Procedure in 5G Cellular Networks
Navyasree Petluri Web Traffic Prediction of Wikipedia Pages
Misba Momin Detecting Heart Rate Variability using Web Services
Lingwei Meng QoS-Based Cloud Service Recommender System
Amruthaa Rajan Enhancing Accessibility of Machine Learning using Service Oriented Architecture

 

MCSS Independent Study Alumni
Ruslan Nurimbetov Dariya Abdrakhmanova Sanjay Vuppugandla Ashwin Meiyappan
Sri Vibhu Paruchuri Nazim Zerrouki Yanliu Wang Shori Yu
Tsung-Jui Wang Dhruviben Kaswala Deepthi Edakunni Sumitha Ravindran
Yiming Gan Rashad Hatchett Simerpreet Kumar Jyoti Shankar
Moran Wang Varik Hoang Pradnya Bhumkar Vaishnavi Goteti
Poornima Dixith Reshma Geetha Rashmi Ramachandra Himani Singh
Rajeev Suri Danyang Xu Xiaola Ye Feng Gao
Jiaqi Wang Zac Lu Shubham Kabu Ramil Zagidultdn
Richa Jain Manish KC Karan Kalyanam Sujanasree Ratakonda
Bhavana Gudi Suganya Jeyaraman Ramya Kumar Pooja Shrivastava
Bharathi Manoharan Swetha Reddy Nathala Ibrahim Diabate Ming Hoi Lam

 

Undergraduate Research Alumni
Josh Lee Abdulqadir Ibrahim Cordel Hampshire Samuel Hart
Dino Jazvin Daniel Jiang Reuben Keller John Batts
Jonathan Kim Sharanjit Singh Tammy Vo Greg Gertsen
Diesawi Mohammodnur Armoni Atherton David Chau Vecheka Chhourn
Charlotte Yan Craig Robertson Allen Whitemarsh Minh-Huy Tran
Andrea Maria Moncada Eduard Ktdmenko Daniel Carns Benen Adsitt
Christopher Marisco Katatdna Biondi Emmett Kang Kyle Beveridge
Zhou Lu Richard Yang Michael Quandt Keith Stellyes
Daryan Hanshee Dino Hadzic Marshall Freed Haylee Ryan

Prospective Students

Interested in research?

Networked and Multi-Agent Intelligent Systems Lab (NMAIS)

NMAIS Lab The Networked and Multi-Agent Intelligent Systems Lab (NMAIS) focuses on multi-agent orchestration, agentic AI systems, privacy-preserving federated architectures, trustworthy edge intelligence, IoT and cyber-physical systems, AI-for-science, and secure distributed infrastructure.

We are currently recruiting highly motivated students interested in:
  • PhD Students interested in Multi-Agent Systems, Agentic AI, MCP Tool Orchestration, Large Language Models (LLMs), AI Governance, Federated Learning, Differential Privacy, Edge Intelligence, or Trustworthy AI.
  • Master's Thesis / Capstone Students interested in AI agents, service discovery and composition, privacy-preserving AI, distributed systems, edge computing, IoT systems, anomaly detection, or AI-for-Science applications.
  • Undergraduate Researchers interested in Generative AI, multi-agent systems, intelligent web services, edge AI, cybersecurity, IoT systems, or applied machine learning.
  • Collaborators and Visiting Researchers interested in large-scale distributed AI, federated foundation models, technical AI governance, trustworthy autonomous systems, and next-generation intelligent infrastructure.
Current research opportunities include:
  • Multi-Agent Orchestration and Agentic AI Systems
  • Inter-LLM Communication and Collective Reasoning
  • Federated Foundation Models and Privacy-Preserving AI
  • Differential Privacy and Trustworthy Machine Learning
  • Edge AI, Resource Allocation, and Distributed Intelligence
  • IoT Security, Forensics, and Cyber-Physical Systems
  • Lab-as-a-Service (LaaS) and Remote Scientific Infrastructure
  • AI for Science and Autonomous Research Systems
If you are interested, please take a few minutes to fill out this form .

My students have achieved remarkable success: publishing first-author papers, receiving global awards, earning campus-wide recognition securing internships and full-time positions at top technology companies like Microsoft, Google, SAP, and IBM, and gaining admission to prestigious Ph.D. programs at leading universities. I am always proud of their accomplishments and committed to helping them develop their future careers. To learn more about the publications by my current and former students, please visit the Research section.