William Paterson University · Department of Computer Science

ACBOS Virtual Research Laboratory

Investigating the convergence of Artificial Intelligence, Cognitive Systems, Blockchain Technology, and Open-Source Computing to build adaptive, decentralized, and intelligent systems for the future.

115+
Publications
1,758+
Citations
5
Books
15+
Years of Research
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Dr. Ali Mohammad Saghiri

Dr. Ali Mohammad Saghiri is an Assistant Professor of Computer Science at William Paterson University in New Jersey. With over 15 years of research experience, he works at the intersection of Artificial Intelligence, Cognitive Systems, Blockchain, and Distributed Computing. His work bridges theoretical foundations in reinforcement learning and formal methods with practical applications in decentralized systems, autonomous decision-making, and self-evolving software.

Before joining William Paterson in 2023, Dr. Saghiri held postdoctoral research positions at Stevens Institute of Technology, where he worked on cybersecurity challenges in AI, peer-to-peer energy trading markets, topology optimization using reinforcement learning, adaptive cruise control algorithms, Central Bank Digital Currencies, and blockchain-based budget management systems.

He earned his Ph.D. and M.Sc. in Computer Engineering from Amirkabir University of Technology (Tehran Polytechnic), studying under Professor Mohammad Reza Meybodi at the Soft Computing Laboratory. His doctoral research advanced the theory of Dynamic Cellular Learning Automata and their applications in peer-to-peer network optimization. His M.Sc. thesis focused on personalized information retrieval in peer-to-peer systems under the supervision of Dr. Alireza Bagheri.

He also served as a consultant at Rutgers School of Dental Medicine on research involving applications of Digital Twins and Artificial Intelligence in dentistry, and held visiting professor and researcher positions at multiple institutions and Mazandaran University of Science and Technology.

Dr. Saghiri is a strong advocate for open-source software and Linux-based systems in both research and education. He has authored 5 books, 7 book chapters, 20 journal papers, and 25+ conference papers, accumulating over 1,758 citations.

Current Position

Assistant Professor , Dept. of Computer Science, William Paterson University, Wayne, NJ (2023–Present)

Education

  • Ph.D. in Computer Engineering (AI) — Amirkabir University of Technology, 2010–2017. Advisor: Prof. M. R. Meybodi
  • M.Sc. in Computer Engineering (Software) — Amirkabir University of Technology, 2007–2010. GPA: 3.72. Advisor: Dr. A. Bagheri

Research Interests

  • Artificial General Intelligence & Cognitive Systems
  • Blockchain, Web 3.0 & Decentralized Systems
  • Reinforcement Learning & Learning Automata
  • Formal Methods & Software Verification
  • Internet of Things (IoT)
  • Linux & Open-Source Computing
  • Distributed Systems & Peer-to-Peer Networks
  • Software Engineering

Editorial Roles

  • Guest Editor — Applied Sciences (MDPI)
  • Guest Editor — Mathematical Biosciences & Engineering
  • Editorial Board — Int'l J. Advances in Applied Sciences

Selected Prior Positions

  • Postdoc — Stevens Institute of Technology, School of Business (2023)
  • Postdoc — Stevens Institute of Technology, School of Systems (2022)
  • Consultant — Rutgers School of Dental Medicine (2022–Present)
  • Researcher — IoT Center, Amirkabir University (2018–2020)
  • Visiting Prof — Mazandaran University of Science & Technology (2019–2022)

Awards

  • Ranked top 0.2% among 10,395 in national university exam (2007)
  • Full Ph.D. scholarship, Amirkabir University of Technology (2010–2017)
  • Best Paper Award, Int'l Conference on Web Research — ICWR (2018)

Where Our Work Lives

The ACBOS Lab operates at four intersecting frontiers. Our research philosophy is rooted in building systems that are adaptive, decentralized, verifiable, and open.

Artificial Intelligence & Cognitive Systems

We investigate how intelligent agents learn, adapt, and make decisions in complex, uncertain environments. Our work in reinforcement learning — particularly Learning Automata theory — forms the backbone of adaptive algorithms for distributed systems. We explore cognitive architectures that enable self-awareness, self-organization, and emergent intelligence, drawing from cellular automata, fungal growth patterns, and Schelling segregation models.

Blockchain & Decentralized Computing

From defending Proof-of-Work protocols against selfish mining attacks to designing decentralized platoon management, our blockchain research spans theory and application. We use AI-driven defense mechanisms (Q-learning, variable-depth learning automata) and study blockchain architectures for search engines, healthcare data, budget systems, energy trading, and CBDCs.

Linux & Open-Source Systems

We are committed to open-source as both a philosophy and a practical research framework. Our lab uses Linux-based environments for all development. We research adaptive shell scripting with LLMs, system administration automation, and formal verification of system-level code. Reproducible, open computing is essential for credible science.

Formal Methods & Self-Evolving Software

We develop formal verification frameworks using SMT solvers (Z3), temporal logic model checking (SPIN), and mathematical proof systems to verify LLM-generated code. Our self-evolving programs combine Large Language Models with Quine programs to create software that adapts and improves autonomously.

Complete Publications

All peer-reviewed publications. For real-time citation data visit Google Scholar, ResearchGate, or DBLP.

2025
Blockchain for Beginners: A Project-Based Approach
A. M. Saghiri, N. Wang
Cognella Academic Publishing
Textbook
2024
AI Millionaire: A Guide to Forecasting Future Jobs and Hunting Opportunities
A. M. Saghiri
Amazon
Self-Published
2024
How to Become an Expert In Job Hunting: Harnessing the Power of Generative Chatbots
A. M. Saghiri
Amazon
Self-Published
2023
Why GPT-Based Chatbots Will Be Vital: Applications, Challenges, and the Shaping of the Fragile Job Market
A. M. Saghiri
Amazon
Self-Published
2019
Intelligent Random Walk: An Approach Based on Learning Automata
A. M. Saghiri, M. D. Khomami, M. R. Meybodi
Springer
Monograph
2018
Recent Advances in Learning Automata
A. Rezvanian, A. M. Saghiri, S. M. Vahidipour, M. Esnaashari, M. R. Meybodi
Springer — Studies in Computational Intelligence, Vol. 754
Edited Volume
2023
Applications of Digital Twins to Migraine
A. M. Saghiri, K. G. HamlAbadi, M. Vahdati
Digital Twin for Healthcare, Elsevier
2022
Cognitive Internet of Things: Challenges and Solutions
A. M. Saghiri
Artificial Intelligence-Based Internet of Things Systems, Springer, pp. 335–362
2021
IoT-Based Healthcare Monitoring Using Blockchain
M. Vahdati, K. G. HamlAbadi, A. M. Saghiri
Applications of Blockchain in Healthcare, Springer, pp. 141–170
2020
Blockchain Architecture
A. M. Saghiri
Advanced Applications of Blockchain Technology, Springer, pp. 161–176
2020
Lurkers versus Posters: Investigation of the Participation Behaviors in Online Learning Communities
O. R. B. Speily, A. Rezvanian, A. Ghasemzadeh, A. M. Saghiri, S. M. Vahidipour
Educational Networking, Springer, pp. 269–298
2020
The Internet of Things, Artificial Intelligence, and Blockchain: Implementation Perspectives
A. M. Saghiri, K. G. HamlAbadi, M. Vahdati
Advanced Applications of Blockchain Technology, Springer, pp. 15–54
2014
A Learning Automata-Based Version of SG-1 Protocol for Super-Peer Selection in P2P Networks
S. Gholami, M. R. Meybodi, A. M. Saghiri
Recent Advances in Information and Communication Technology, Springer, pp. 189–201
2025
Asymmetric Variable Depth Learning Automaton and Its Application in Defending Against Selfish Mining Attacks on Bitcoin
A. Nikhalat-Jahromi, A. M. Saghiri, M. R. Meybodi
Applied Soft Computing, Vol. 170
2025
Cellular Goore Game with Multiple Learning Automata in Each Cell and Its Applications
M. M. Daliri Khomami, A. M. Saghiri, M. R. Meybodi
The Journal of Supercomputing, 81(7)
2024
A Bibliometric Analysis of Quantum Machine Learning Research
A. Ahmadikia, A. Shirzad, A. M. Saghiri
Science & Technology Libraries
2024
CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments
A. Rezvanian, S. M. Vahidipour, A. M. Saghiri
Algorithms, 17(1)
2023
A Literature Review of Energy Optimal Adaptive Cruise Control Algorithms
S. Vasebi, Y. M. Hayeri, A. M. Saghiri
IEEE Access, Vol. 11, pp. 13636–13646
2022
A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions
A. M. Saghiri, M. R. Jabbarpour, M. Vahidipour, M. Sookhak, A. Forestiero
Applied Sciences, 12, pp. 40–54
2022
HLA: A Novel Hybrid Model Based on Fixed Structure and Variable Structure Learning Automata
S. Gholami, A. M. Saghiri, S. M. Vahidipour, M. R. Meybodi
Journal of Experimental & Theoretical Artificial Intelligence
2021
A Framework for Component Selection Considering Dark Sides of AI: A Case Study on Autonomous Vehicle
M. R. Jabbarpour, A. M. Saghiri, M. Sookhak
Electronics, 10, 384
2021
A Self-Adaptive Algorithm for Super-Peer Selection Considering Mobility of Peers in Cognitive Mobile P2P Networks
N. Amirazodi, A. M. Saghiri, M. R. Meybodi
International Journal of Communication Systems, 34(1)
2021
A Survey on Blockchain-Based Search Engines
E. Rezaee, A. M. Saghiri, A. Forestiero
Applied Sciences, 11(15), 7063
2020
Extracting Strategies for Improving IoT-Based Home Industries in Iran: A SWOT Analysis
R. Mahmoudi, S. Roozi, A. M. Saghiri, A. Mahmoudi
IEEE Transactions on Engineering Management, 68(2), pp. 586–598
2018
Open Asynchronous Dynamic Cellular Learning Automata and Its Application to Allocation Hub Location Problem
A. M. Saghiri, M. R. Meybodi
Knowledge-Based Systems, 139, pp. 149–169
2018
An Adaptive Super-Peer Selection Algorithm Considering Peers Capacity Utilizing Asynchronous Dynamic Cellular Learning Automata
A. M. Saghiri, M. R. Meybodi
Applied Intelligence, 48(2), pp. 271–299
2018
On Expediency of Closed Asynchronous Dynamic Cellular Learning Automata
A. M. Saghiri, M. R. Meybodi
Journal of Computational Science, 24, pp. 371–378
2018
An Adaptive Algorithm for Super-Peer Selection Considering Peer's Capacity in Mobile P2P Networks Based on Learning Automata
N. Amirazodi, A. M. Saghiri, M. R. Meybodi
Peer-to-Peer Networking and Applications, 11, pp. 74–89
2017
A Distributed Adaptive Landmark Clustering Algorithm Based on mOverlay and Learning Automata for Topology Mismatch Problem
A. M. Saghiri, M. R. Meybodi
International Journal of Communication Systems, 30(3)
2017
A Delay Aware Super-Peer Selection Algorithm for Gradient Topology Utilizing Learning Automata
S. F. Deiman, A. M. Saghiri, M. R. Meybodi
Wireless Personal Communications, 95(3), pp. 2611–2624
2017
A Closed Asynchronous Dynamic Model of Cellular Learning Automata and Its Application to Peer-to-Peer Networks
A. M. Saghiri, M. R. Meybodi
Genetic Programming and Evolvable Machines, 18(3), pp. 313–349
2016
An Approach for Designing Cognitive Engines in Cognitive Peer-to-Peer Networks
A. M. Saghiri, M. R. Meybodi
Journal of Network and Computer Applications, 70, pp. 17–40
2016
A Self-Adaptive Algorithm for Topology Matching in Unstructured Peer-to-Peer Networks
A. M. Saghiri, M. R. Meybodi
Journal of Network and Systems Management, 24, pp. 393–426
2024
A Study on the Dark Sides of AI on the Future of Education
A. M. Saghiri, M. A. Saghiri
11th Annual NJBDA Symposium, Rutgers University
2024
Self-Evolving Programs: A Novel Approach Leveraging LLMs and Quine Programs
A. M. Saghiri, N. Wang
ICCIMS 2024 (IEEE)
2024
Decentralized Platoon Management Based on Blockchain: A SWOT Analysis Research Perspective
A. M. Saghiri, N. Wang
ICCIMS 2024 (IEEE)
2024
Q-Defense: When Q-Learning Comes to Help Proof-of-Work Against the Selfish Mining Attack
A. Nikhalat-Jahromi, A. M. Saghiri, M. R. Meybodi
ICAART 2024
2024
A Framework for Cognitive Defense in Blockchain: AI-Based Protection Against Selfish Mining
A. Nikhalat-Jahromi, A. M. Saghiri, M. R. Meybodi
ICAART 2024 — LNCS Vol. 15591, Springer
2023
A Generic Parametric Framework for Peer-to-Peer Electricity Market Design
A. M. Saghiri, S. Moazeni
IEEE EEEIC / I&CPS Europe 2023
2022
Digital Twins in Cancer: State-of-the-Art and Open Research
K. G. HamlAbadi, M. Vahdati, A. M. Saghiri, A. Forestiero
IEEE/ACM CHASE 2021, pp. 199–204
2021
Solving Minimum Dominating Set in Multiplex Networks Using Learning Automata
M. M. D. Khomami, A. Rezvanian, A. M. Saghiri, M. R. Meybodi
26th CSICC (IEEE), pp. 1–6
2020
SIG-CLA: Significant Community Detection Based on Cellular Learning Automata
M. M. D. Khomami, A. Rezvanian, A. M. Saghiri, M. R. Meybodi
8th CFIS (IEEE), pp. 039–044
2020
Utilizing Cellular Learning Automata for Finding Communities in Weighted Networks
M. M. D. Khomami, A. Rezvanian, A. M. Saghiri, M. R. Meybodi
6th ICWR (IEEE), pp. 325–329
2020
Overlapping Community Detection in Social Networks Using Cellular Learning Automata
M. M. D. Khomami, A. Rezvanian, A. M. Saghiri, M. R. Meybodi
28th ICEE (IEEE), pp. 1–6
2020
A Survey on Challenges in Designing Cognitive Engines
A. M. Saghiri
6th ICWR (IEEE), pp. 165–171
2020
Distributed Learning Automata-Based Algorithm for Finding k-Clique in Complex Social Networks
M. M. D. Khomami, A. Rezvanian, A. M. Saghiri, M. R. Meybodi
11th IKT (IEEE), pp. 139–143
2019
An Adaptive Topology Management Algorithm in P2P Networks Based on Learning Automata
M. Ghorbani, M. R. Meybodi, A. M. Saghiri
7th CFIS (IEEE), pp. 1–4
2019
An Architecture for Managing IoT Based on Cognitive Peer-to-Peer Networks
M. Ghorbani, M. R. Meybodi, A. M. Saghiri
5th ICWR (IEEE), pp. 111–116
2018
A Framework for Cognitive Internet of Things Based on Blockchain
A. M. Saghiri, M. Vahdati, K. Gholizadeh, M. R. Meybodi, M. Dehghan, H. Rashidi
4th ICWR (IEEE), pp. 138–143
Best Paper
2018
A Self-Organized Framework for Insurance Based on IoT and Blockchain
M. Vahdati, K. G. HamlAbadi, A. M. Saghiri, H. Rashidi
IEEE FiCloud 2018, pp. 169–175
2017
An Algorithm for Weighted Positive Influence Dominating Set Based on Learning Automata
M. M. D. Khomami, M. A. Haeri, M. R. Meybodi, A. M. Saghiri
IEEE KBEI 2017, pp. 0734–0740
2017
A Framework for Cognitive Recommender Systems in the IoT
K. G. HamlAbadi, A. M. Saghiri, M. Vahdati, M. D. TakhtFooladi, M. R. Meybodi
IEEE KBEI 2017, pp. 0971–0976
2016
Adaptive Search in Unstructured P2P Networks Based on Ant Colony and Learning Automata
A. Ahmadi, M. R. Meybodi, A. M. Saghiri
AI and Robotics, IranOpen (IEEE), pp. 133–139
2016
An Adaptive Algorithm for Managing Gradient Topology in Peer-to-Peer Networks
S. F. Deiman, A. M. Saghiri, M. R. Meybodi
8th IKT (IEEE), pp. 91–97
2015
A Bandwidth-Aware Algorithm for Solving Topology Mismatch in P2P Networks Using Learning Automata and X-Bot
M. H. G. Dolabi, M. R. Meybodi, A. M. Saghiri
23rd ICEE (IEEE), pp. 607–612
2013
A New Version of k-Random Walks Algorithm in P2P Networks Utilizing Learning Automata
M. Ghorbani, M. R. Meybodi, A. M. Saghiri
5th IKT (IEEE), pp. 1–6
2013
A Novel Self-Adaptive Search Algorithm for Unstructured P2P Networks Utilizing Learning Automata
M. Ghorbani, M. R. Meybodi, A. M. Saghiri
3rd Joint Conf. AI & Robotics / 5th RoboCup Iran Open (IEEE)
2010
Enhance Your Search Engine Functionality with Peer-to-Peer Systems
A. Saghiri, A. Bagheri
2nd ICCAE (IEEE), Vol. 1, pp. 583–586
2009
An Adaptive Architecture for Personalized Search Engine in Ubiquitous Environment with P2P Systems
A. Saghiri, A. Bagheri
Int'l Conf. Information and Multimedia Technology (IEEE), pp. 107–111

Courses & Talks

William Paterson University (Current)

  • Computer and Information Technology
  • System Administration
  • Computer Networks II
  • Discrete Mathematics

Previous Institutions

  • Operating Systems — MUST
  • Computer Networks — MUST
  • Artificial Intelligence — Azad University
  • Database — Azad University
  • Software Engineering — Azad University
  • Theory of Automata — Azad University
  • Algorithms — Univ. of Science and Culture
  • Programming Fundamentals — Amirkabir UT

Invited Talks & Workshops

  • Why Chatbots Will Be Vital in the Future of Education? — Cengage (2024)
  • Central Bank Digital Currencies — Stevens Institute (2022)
  • Internet of Things — Sharif University (2021)

Virtual Lab Space for Students

The ACBOS Virtual Lab is an open, collaborative research environment for motivated students — undergraduate and graduate — who want real experience working on cutting-edge problems in AI, cognitive systems, blockchain, and open-source software. Whether at William Paterson University or externally seeking research mentorship, our lab welcomes you.

Participation is project-driven. Students are matched to active research projects based on interests and skill level, and receive direct mentorship from Dr. Saghiri. All work uses open-source tools and Linux-based environments. Students are encouraged to publish findings and contribute to open-source repositories. The lab operates virtually — participate from anywhere.

Lab members build a portfolio of tangible research outputs — conference papers, journal articles, open-source code, and technical reports — preparing them for industry careers or graduate study.

01

AI & Reinforcement Learning

Adaptive learning algorithms, neural network optimization, cognitive architectures, learning automata, LLM-based code generation with formal verification.

PythonPyTorchZ3SPINLinux
02

Blockchain & Decentralized Systems

Consensus mechanism defense, smart contracts, blockchain search engines, healthcare data, DeFi analysis, CBDC research.

SolidityEthereumGoRustLinux
03

Open-Source & Systems

System administration automation, adaptive shell scripting, CI/CD pipelines, reproducible research infrastructure, open-source contributions.

BashGitDockerCI/CDLinux
04

IoT & Cognitive Networks

Intelligent algorithms for IoT, peer-to-peer networks, cognitive mobile environments, digital twin architectures, adaptive resource discovery.

C/C++MQTTPythonArduinoLinux

How to Join the Lab

  1. Review the research tracks above and identify which area aligns with your interests.
  2. Prepare a brief statement describing your research interests, relevant coursework, and what you hope to gain.
  3. Attach your resume/CV and any relevant work samples (GitHub, class projects, papers).
  4. Send materials to saghiria@wpunj.edu with subject: "ACBOS Lab — Student Application".
  5. Dr. Saghiri will respond within two weeks. If there is a good fit, an introductory meeting will be scheduled.

Open-Source First

Every experiment in our lab is built on open-source foundations.

Operating System

Ubuntu & Debian-based Linux for all development, simulation, and deployment.

Languages

Python, Bash/Shell, Solidity, C/C++, Go, Rust, Java, C#.

AI & ML

PyTorch, TensorFlow, scikit-learn, Z3 SMT Solver, SPIN model checker, MATLAB.

Blockchain

Ethereum (Geth, Hardhat), Nethereum, Hyperledger, The Graph Protocol.

Version Control

Git and GitHub for all code, papers (LaTeX), and collaborative research.

IDEs & Tools

PyCharm, Visual Studio, NetBeans, Docker, and CI/CD pipelines.

Writing

LaTeX, Overleaf, Pandoc. IEEE and Springer templates maintained in-lab.

Hardware

Arduino for IoT prototyping, sensor arrays, and embedded systems research.

Contact

Whether you are a student interested in joining the lab, a researcher seeking collaboration, or an industry partner exploring applied research — we would like to hear from you. All communication is handled through email.

Dr. Saghiri welcomes inquiries about research projects, guest lectures, peer review collaborations, thesis supervision, and joint publications.

Location

Department of Computer Science
William Paterson University
300 Pompton Road, Wayne, NJ 07470