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Hire Dr. Aiman G.

Netherlands

Applied ML Researcher | Time-Series Forecasting, Digital Twins & Python Data Apps, Networks, and NLP

Profile Summary

I am an applied machine learning researcher and Python developer with strong experience in time-series forecasting, digital twins, interpretable AI, and custom data applications. I completed my PhD at USTC and later worked as a postdoctoral researcher at Eindhoven University of Technology, where I developed global forecasting and clustering methods for large LED degradation datasets as part of the ECSEL AI-Twilight digital twin project. I am the first author of an ACM TIST publication on global forecasting for LED lumen degradation. In addition to research, I build practical tools that help people use data effectively. I have developed Streamlit apps, Python analysis pipelines, and desktop GUI tools for exploratory analysis, explainable AI, and interactive visualization. My projects include end-to-end applications for semantic mapping, network analysis, and rule extraction, and I enjoy turning complex datasets into usable systems for researchers, engineers, and decision-makers. I can help with forecasting, data cleaning, exploratory analysis, interpretable ML, dashboard development, and research-oriented Python solutions. My background also includes network analytics and NLP model development, which gives me flexibility across scientific and technical projects.

Subject Matter Expertise

• ☆ Electrical Engineering
• ☆ Wireless Sensor Networks
• ☆ Network Engineering
• ☆ Data Analysis
• ☆ Machine Learning
• ☆ Neural Networks
• ☆ Telecommunications
• ☆ Computer Networks & Networking
• ☆ Multilingualism
• ☆ Computer Science Education

Services

• Writing
• Research
• Consulting
• Data & AI

Writing Technical Writing, Creative Writing

Research Scientific and Technical Research

Consulting Digital Strategy Consulting, Scientific and Technical Consulting

Data & AI Predictive Modeling, Image Analysis, Algorithm Design-Non ML, Algorithm Design-ML, Data Visualization, Text Mining & Analytics, Data Processing, Data Insights

Work Experience

Independent Researcher

Independent Researcher

January 2026 - Present

Eindhoven University of Technology

- May 2024

Visiting Scholar

University of Science and technology of China

July 2021 - July 2022

Network engineering

MIPc

November 2010 - August 2013

Education

doctor of engineering in computer science

University of Science and Technology of China

September 2017 - May 2021

Master of engineering in Computer Science

University of Science and Technology of China

September 2014 - June 2017

Certifications

• Ph.D

  University of Science and Technology of China

  January 2026 - Present

• Masters

  University of Science and Technology of China

  January 2026 - Present

Publications

JOURNAL ARTICLE

Aiman Ghannami (2026). Global Forecasting Model for LED Lumen Degradation: An Optimal Cluster Estimation Method . ACM Transactions on Intelligent Systems and Technology.

Potential of Satellite-Airborne Sensing Technologies for Agriculture 4.0 and Climate-Resilient @article{e9c20e02820442a2b6f9d6da5a1987ee, title = "Potential of Satellite-Airborne Sensing Technologies for Agriculture 4.0 and Climate-Resilient: A Review", abstract = "Agriculture 4.0 offers the potential to revolutionize the agriculture sector through improved productivity and efficiency. However, adopting Agriculture 4.0 requires a period of transition and effort. Satellite-airborne sensing technologies may become an opening enabler technology of this new paradigm due to its fast deployment process and flexible infrastructure. This article provides an overview of the technology, trends, challenges, and opportunities in agriculture and climate-resilient sensing technologies. The research covers critical enabling technologies such as low-altitude platforms (LAPs) (i.e., drones and tethered balloons), high-altitude platforms (HAPs) (i.e., airships, HAPs balloons, and aircraft), and satellites, as well as recent advancements in data processing and digital twins (DT), with some examples from agricultural research projects. Furthermore, this article explores some challenges in agriculture and the technological deployment of satellite-airborne sensing technologies. Finally, this article provides some potential opportunities for satellite-airborne sensing technologies for agricultural purposes. This article may become a guide for adopting Industry 4.0 by leveraging satellite-airborne network technologies.", keywords = "Agriculture 4.0, airborne network (AN), high-altitude platforms (HAPs), satellite, smart farming, unmanned aerial vehicles (UAVs)", author = "Hazmy, \{Asa Ibnu\} and Ammar Hawbani and Xingfu Wang and Ahmed Al-Dubai and Aiman Ghannami and Yahya, \{Ali Abdullah\} and Liang Zhao and Alsamhi, \{Saeed Hamood\}", year = "2024", month = feb, day = "15", doi = "10.1109/JSEN.2023.3343428", language = "English", volume = "24", pages = "4161--4180", journal = "IEEE Sensors Journal", issn = "1530-437X", publisher = "Institute of Electrical and Electronics Engineers", number = "4", } . IEEE Sensors Journal.

Ghannami, A., Li, J., Hawbani, A., Alhusaini, N.(2021). Diversity metrics for direct-coded variable-length chromosome shortest path problem evolutionary algorithms . Computing. 103. (2). p. 313-332.

Ghannami, A., Li, J., Hawbani, A., Al-Dubai, A.(2021). Stratified opposition-based initialization for variable-length chromosome shortest path problem evolutionary algorithms . Expert Systems with Applications. 170.

Hawbani, A., Wang, X., Kuhlani, H., Ghannami, A., Farooq, M.U., Al-sharabi, Y.(2019). Extracting the overlapped sub-regions in wireless sensor networks . Wireless Networks. 25. (8). p. 4705-4726.

Hawbani, A., Wang, X., Sharabi, Y., Ghannami, A., Kuhlani, H., Karmoshi, S.(2019). LORA: Load-Balanced Opportunistic Routing for Asynchronous Duty-Cycled WSN . IEEE Transactions on Mobile Computing. 18. (7). p. 1601-1615.

Hawbani, A., Wang, X., Abudukelimu, A., Kuhlani, H., Sharabi, Y., Qarariyah, A., Ghannami, A.(2019). Zone Probabilistic Routing for Wireless Sensor Networks . IEEE Transactions on Mobile Computing. 18. (3). p. 728-741.

Hawbani, A., Wang, X., Karmoshi, S., Kuhlani, H., Ghannami, A., Abudukelimu, A., Ghoul, R.(2017). GLT: Grouping based location tracking for object tracking sensor networks . Wireless Communications and Mobile Computing. 2017.

CONFERENCE PAPER

Ghannami, A., Shao, C.(2017). Efficient fast recovery mechanism in Software-Defined Networks: Multipath routing approach . 2016 11th International Conference for Internet Technology and Secured Transactions, ICITST 2016. p. 432-435.

Ghannami, A., Shao, C.(2017). Experience-based learning for identifying sub-regions in Wireless Sensor Networks . 2016 11th International Conference for Internet Technology and Secured Transactions, ICITST 2016. p. 326-327.

Karmoshi, S., Hawbani, A., Ghannami, A., Mohammed, S., Zhu, M.(2017). VNE-Greedy: Virtual Network Embedding Algorithm Based on OpenStack Cloud Computing Platform . Proceedings - 2016 International Conference on Digital Home, ICDH 2016. p. 143-149.