Research
My research focuses on the development and application of advanced mathematical and computational methods to solve complex problems across diverse fields such as mathematical biology, engineering, signal processing, and emerging technologies like quantum computing and blockchain. By combining mathematical modeling, data-driven techniques, and interdisciplinary collaboration, my work addresses real-world challenges while contributing to theoretical advancements.
Key Research Areas
1. Mathematical Modeling of the Alzheimer's Disease
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2. Mathematical Biology and Biomedical Applications
I have extensively studied the dynamic instability of microtubules, tumor growth, and Alzheimer's disease. Notably, my work on using compressed sensing and peak detection for estimating microtubule parameters was featured at IEEE Bioinformatics and Biomedicine (BIBM). My models aim to bridge the gap between biological theory and clinical application, such as optimizing cancer treatment strategies and analyzing stochastic microtubule behavior.
3. Smart Grids and Renewable Energy
My research includes modeling uncertainties in wind power for microgrid operations using the dichotomous Markov noise technique. This work, presented at the IEEE Great Lakes Symposium on Smart Grid, contributes to sustainable energy solutions by addressing reliability and efficiency challenges in renewable energy systems.
4. Signal Processing and Wavelet Applications
Leveraging wavelet-based compressed sensing and Kalman estimation methods, I have developed approaches to analyze complex signals in fields such as microtubule dynamics and ship power distribution models. My work has been presented at multiple IEEE conferences, demonstrating its utility in solving computational and engineering problems.
5. Quantum Computing and Blockchain
My recent work focuses on the development of quantum-resistant blockchain technologies to address security challenges in the rapidly evolving field of cryptography. This research was highlighted at the International Conference on Electrical, Computer, and Energy Technologies (ICECET), showcasing its potential impact on securing digital infrastructure in the post-quantum era.
6. Interdisciplinary Innovations
Collaborating with researchers across disciplines, I have contributed to mathematical models for tumor growth, bioinformatics, and engineering systems. My work has been published in prominent journals such as Nonlinearityand Mathematical Methods in the Applied Sciences and has been presented at leading conferences worldwide.
Future Directions
Moving forward, I aim to expand my research in the following areas:
- Integrating machine learning with mathematical modeling for predictive analytics in biomedical and engineering applications.
- Advancing the use of stochastic processes in renewable energy systems and climate modeling.
- Exploring the applications of quantum computing in optimization problems and secure communications.
Through my interdisciplinary approach, I strive to create impactful solutions that bridge theoretical research with practical applications, fostering advancements in both established and emerging fields.