I am a researcher and developer specializing in deep learning, biometric systems, and TinyML applications. My work bridges edge AI algorithms with practical, real-world implementations, particularly in healthcare and embedded systems.

• Biometric Identification: Leveraging ECG, voice, and online handwritten signatures for secure and accurate authentication.
• Deep Learning Architectures: Exploring CNNs, LSTMs, and GRUs for complex signal processing tasks.
• TinyML and Edge Computing: Building and optimizing AI models for resource-constrained environments like the ESP32 and the STM32 microcontrollers.
• Signal Processing: Advanced techniques such as EMD, Hilbert-Huang Transform, and spectrogram analysis for feature extraction.

I have authored several peer-reviewed journal articles and conference papers on topics including ECG-based biometric systems and the integration of deep learning into embedded systems. View my publications here: ResearchGate | Google Scholar.

• Programming: Python, C, C++, Assembly
• Tools & Frameworks: TensorFlow, Edge Impulse, MATLAB, TinyML, FreeRTOS, IoT
• Embedded Platforms: ESP32, STM32, Arduino, Raspberry Pi

I am actively working on healthcare solutions optimized for embedded platforms, integrating TinyML and efficient deep learning architectures to enhance the performance of the proposed system.

I’m always excited to collaborate on projects related to Deep Learning, TinyML, and AI-powered healthcare solutions.