PHD at Guangzhou University, China
Mehdi Gheisari is a cybersecurity researcher specializing in privacy-preserving solutions for IoT at Harbin Institute of Technology, Shenzhen. With a PhD in Cyber Space Security from Guangzhou University, China, and a background in Software Engineering from Iran, he brings expertise in SDN, WSN, and Semantic Web. As a lecturer for eight years and experienced project manager, he’s contributed to international and national research projects. Notable for his extensive publication record and awards, Mehdi is known for his active role in conferences and as an associate editor. He’s committed to advancing cybersecurity and fostering cross-cultural collaboration in technology.
Professional Profiles:
Education
Faculty member, Harbin Institute of Technology, Shenzhen, China (Aug 2021 – Present) Researcher, SUSTECH, Shenzhen, China (July 2020 – Aug 2021) Visiting Researcher, ITMO, St. Petersburg, Russia (Sep 2020 – Aug 2021) PhD in Cyber Space Security, Guangzhou University, China (Sep 2016 – Dec 2019) MSc in Software Engineering, IAU, and Amirkabir University of Technology, Iran (Sept 2007 – June 2010)
Work Experiences
- Programmer with .NET, IGS, Iran (Sept 2009 – Dec 2009)
- Software Engineer, ICDL, Iran (JUN 2013 – Dec 2013)
- Project Manager, Fanava, Iran (Sept 2013 – Jan 2016)
- Tech Consultant at Bull Run Consulting Company, bullrun.biz/leadership-team
Teaching Experience:
Lecturer, IAU, Shamsipour, UAST, Iran (Sept 2008 – Aug 2015)
Achievements/Awards:
Guangzhou University award (2016-2019) Winner of Guangdong Government Outstanding Student Award (2017) Best Researcher Award, Islamic Azad University (2015)
Research Interests
Security and Privacy in IoT and Autonomous Vehicles Software Defined Networking (SDN) Wireless Sensor Networks Semantic Web Big Data
Research Focus:
Mehdi Gheisari’s research primarily focuses on privacy-preserving solutions within IoT-based smart cities. He has made significant contributions to this field, as evidenced by his publications in reputable journals and conferences. His work encompasses the development of frameworks, architectures, and methodologies that address the complex challenges of privacy and security in IoT environments. Gheisari’s research extends to areas such as edge computing, deep learning, semantic web, and software-defined networking, aiming to create robust and efficient systems for protecting user data and ensuring the integrity of IoT infrastructure.
Publications
- Sentiment Analysis of Short Texts Using SVMs and VSMs-Based Multiclass Semantic Classification, Publication: 2024.
- Spatio-temporal Data Analytics for e-Waste Management System Using Hybrid Deep Belief Networks, Publication: 2024.
- Correction: Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review, Publication: 2024.
- A novel model for efficient cluster head selection in mobile WSNs using residual energy and neural networks, Publication: 2024.
- Accident reduction through a privacy-preserving method on top of a novel ontology for autonomous vehicles with the support of modular arithmetic, Publication: 2024.
- Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques, Publication: 2024.
- AI in Nuclear Medical Applications: Challenges and Opportunities, Publication: 2024.
- CAPPAD: a privacy-preservation solution for autonomous vehicles using SDN, differential privacy and data aggregation, Publication: 2024.
- Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review, Publication: 2024.
- An efficient computer-aided diagnosis model for classifying melanoma cancer using fuzzy-ID3-pvalue decision tree algorithm, Publication: 2024.
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