Yasin Asadi

Professional Summary

Ph.D. applicant with 8+ years of experience in Computer Engineering, Research, Reviewing, and Lecturing. Expertise spans Machine Learning, Data Science, Artificial Intelligence, Network Engineering, and Network-on-Chip (NoC). Reviewed over 800 scientific papers for top-tier journals (Elsevier, IEEE, Springer, ACM). Active member of ACM, IEEE, and professional engineering societies. Currently engaged in interdisciplinary research with industry applications in ML-driven infrastructure monitoring, energy systems, and transportation. Committed to academic excellence, innovation, and knowledge dissemination.

Academic Education

• M.Sc. in Computer Engineering (Computer Architecture System Design)
  Islamic Azad University, Central Tehran Branch – 2018
• B.Sc. in Computer Engineering (Hardware Engineering)
  Islamic Azad University, Qazvin Branch – 2015

Working & Teaching Experience

• Lecturer – University of Applied Science and Technology, Tehran (2024–Present)
  Taught Windows 7/10/11, Linux; covered Operating Systems, ICDL, data science tools, research methods, and network engineering (ITF+, A+, Network+, MCSA, CCNA, MTCNA).
• Lecturer & Researcher – Islamic Azad University, Central Tehran Branch (2020–Present)
  Same curriculum as above.
• Honorary County Ambassador – I2OR, India (2022–Present)
  Represented and promoted county interests, supported local initiatives, and contributed to development and visibility.
• Lecturer – Tehran Institute of Technology (MFT), Karaj (2021–Present)
  Same curriculum as above.
• Teaching Assistant – Islamic Azad University, Qazvin Branch (2017–2018)
  AI Introduction, Windows 7/10/Server, Linux Fedora, OS Lab, technical computer English.
• Research Assistant – Islamic Azad University, Central Tehran Branch (2016–2018)
  Conducted research for thesis and projects in Computer Engineering, Electronics, and Technical English labs.
• Teaching Assistant – Islamic Azad University, Central Tehran Branch (2015–2018)
  Taught AI, ML, Windows, Linux, OS Lab, electronics, and technical computer English.

Projects & Industry Applications

• Leveraging ML for Water Pipeline Anomaly Detection – Water & Sewerage Co., Iran (2024)
  

  Applied ML for early anomaly detection; reduced maintenance costs by up to 80%, improved system reliability, and enabled data-driven environmental monitoring.

• ML Approach for Detecting Electricity Consumption Irregularities – Power Distribution Co., Iran (2023)
  

  Detected grid anomalies with ML; achieved up to 75% issue avoidance and accurate fault identification, improving resource allocation and sustainability.

• ML Methods for Bus Scheduling Optimization – Suburbs Bus Co., Iran (2017)
  

  Optimized public transport routes and schedules using ML, improving passenger flow prediction and operational efficiency.

• Digital Thermometer with ML on Atmel AVR – BSc Project (2015)
  

  Embedded ML model on microcontroller for real-time, precise temperature sensing—demonstrating edge AI in hardware.

• Solar Power Plant Fuzzy Mechanization (2020–2021)
  

  Designed fuzzy logic control systems for solar automation, improving energy efficiency and reducing operational costs.

• Khayyam International Inventions Festival (2018–2020)
  

  Represented as official liaison and translator; enhanced international collaboration and event quality through cross-cultural coordination.

Publications

• Optical network-on-chip (ONoC) architectures: a detailed analysis of optical router designs
  Journal of Semiconductors 46(3), 031401 (2025)
  DOI: 10.1088/1674-4926/24060006 Journal
  Abstract
  Optical network-on-chip (ONoC) systems have emerged as a promising solution to overcome limitations of traditional electronic interconnects. Efficient ONoC architectures rely on optical routers, enabling high-speed data transfer, efficient routing, and scalability. This paper presents a comprehensive survey analyzing optical router designs, specifically microring resonators (MRRs), Mach−Zehnder interferometers (MZIs), and hybrid architectures. Selected comparison criteria, chosen for their critical importance, significantly impact router functionality and performance. By emphasizing these criteria, valuable insights into the strengths and limitations of different designs are gained, facilitating informed decisions and advancements in optical networking. While other factors contribute to performance and efficiency, the chosen criteria consistently address fundamental elements, enabling meaningful evaluation. This work serves as a valuable resource for beginners, providing a solid foundation in understanding ONoC and optical routers. It also offers an in-depth survey for experts, laying the groundwork for further exploration. Additionally, the importance of considering design constraints and requirements when selecting an optimal router design is highlighted. Continued research and innovation will enable the development of efficient optical router solutions that meet the evolving needs of modern computing systems. This survey underscores the significance of ongoing advancements in the field and their potential impact on future technologies.

  Keywords: optical network-on-chip (ONoC), optical routers, microring resonators (MRRs), Mach−Zehnder interferometers (MZIs), optical networking scalability

• Employing machine learning in water infrastructure management: predicting pipeline failures for improved maintenance and sustainable operations
  Industrial Artificial Intelligence 2(1), 8 (2024)
  DOI: 10.1007/s44244-024-00022-w Journal
  Abstract
  This study explores techniques for managing class imbalance in predictive modeling to forecast water pipe failures using XGBoost and logistic regression. Given the significant challenges posed by water pipeline failures—such as service disruptions, costly repairs, and environmental hazards—there is a pressing need for effective predictive models. Using a dataset from 2015 to 2022 that includes features like pipe age, material, diameter, and maintenance history, the study applies methods such as random oversampling and undersampling to improve model performance. Results show that XGBoost outperforms logistic regression in recall (0.795 vs. 0.683), a critical metric for managing water infrastructure. Although logistic regression has slightly better precision (0.695), XGBoost demonstrates superior overall performance with higher Matthews correlation coefficient (MCC) and F1 score, effectively balancing precision and recall.

  Keywords: Machine learning, Water pipeline, Water management system, Logistic regression, XGBoost

• A comprehensive study and holistic review of empowering network-on-chip application mapping through machine learning techniques
  Discover Electronics 1(1), 22 (2024)
  DOI: 10.1007/s44291-024-00027-w Journal
  Abstract
  This study investigates machine learning (ML) techniques for optimizing Network-on-Chip (NoC) application mapping, focusing on supervised learning, unsupervised learning, reinforcement learning, and neural networks. Through a comparative analysis of recent research, the study reveals that supervised learning methods, like artificial neural networks (ANNs), enhance core vulnerability prediction and runtime mapping. Unsupervised learning techniques improve NoC mapping via multi-label models, while reinforcement learning approaches, including actor-critic frameworks, reduce communication costs and power consumption. Scenario-aware strategies adapt mapping processes to varying operational contexts. Despite these advancements, challenges such as computational overhead, data quality, and model interpretability persist. Future research should focus on scalable ML algorithms, improving data quality, and enhancing model transparency. This study underscores the significant potential of ML to advance NoC application mapping and highlights the need for ongoing innovation to address existing challenges.

  Keywords: Network-on-Chip (NoC), Machine Learning (ML), Application Mapping, On-chip communication

• Artificial intelligence introduction to gaming systems
  Iran National Conference on Information Technology (2015) Conference
• Introduction to Artificial Intelligence, Expert and Fuzzy Systems
  Domestic National Conference on Information Technology, Iran (2017) Conference
• A Novel Approach in Application Fuzzy Mapping for Two-Dimensional Wireless Network-On-Chip in Power Reduction for IoT based systems
  M.Sc. Thesis, Islamic Azad University, Central Tehran Branch (2018) Thesis
• Mind-Shift: A Method for Power and Energy Reduction in Application Mapping onto Network-Chip Architectures
  13th International Conference on Information Technology, Computer and Telecommunication, Tbilisi, Georgia (2021) Conference
• An overview of the types of network operating systems
  14th International Conference on Information Technology, Computer and Telecommunication, Tbilisi, Georgia (2022) Conference
• The Art of Managing Complexity: A Book Review of Digital Design and Computer Architecture RISC-V Edition
  Authorea Preprints (2022) Review

Peer Review & Academic Leadership

Reviewed 800+ papers for leading scientific publishers:

• Elsevier
• Springer Nature
• The Institute of Electrical and Electronics Engineers (IEEE)
• Association for Computing Machinery (ACM)
• Wiley
• Taylor & Francis
• IGI Global

• Mendeley, Elsevier – Scientific Webinars Lead (2022–Present)
  Led global webinars on research tools and citation management.
• Young Researchers and Elite Club – Member (2015–Present)
  Organized webinars and workshops on emerging technologies and research methods.

Professional Memberships

• Association for Computing Machinery (ACM) – Professional Member (2020–Present)
• The Institute of Electrical and Electronics Engineers (IEEE) – Computer Society Member (2022–Present)
• Computer Science Teachers Association (CSTA) – Member (2020–Present)
• International Association of Engineers (IAENG) – Member (2023–Present)
• National Society of Professional Engineers (NSPE) – Member (2024–Present)
• Young Researchers and Elite Club – Member (2015–Present)

Awards & Honors

• Top Graduate Award – M.Sc. in Computer Engineering, Islamic Azad University, 2018
• Best Reviewer of the Year – Springer-Nature, Journal of Electrical Engineering & Technology, 2023

Specializations & Certifications