Mohamed Mazloum Elshahat Mohamed Mohamed Salem

@mans.edu.eg

Faculty of Computer & Information Sciences
Mansoura University

Mohamed Mazloum Elshahat Mohamed Mohamed Salem


                  Download Compact PDF  
https://researchid.co/mosh2eb
I am an early-career researcher in Artificial Intelligence with focus on efficient generative models, multilingual healthcare applications, and adaptive learning frameworks. My work combines computer science and real-world problem solving, and I aim to develop research outputs that are impactful, open-source, and collaborative.

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence, Computer Engineering, Computational Theory and Mathematics

FUTURE PROJECTS

Ultra-Efficient Generative Models for On-Device Intelligence

This project aims to design lightweight generative AI architectures optimized for mobile and edge devices. The research will focus on reducing parameters and computation while preserving state of-the art performance in image, text, and multimodal tasks. Potential applications include healthcare diagnostics, low-resource language processing, and real-time creativity tools.


Applications Invited
Collaborators with expertise in model compression, quantization, and mobile AI deployment.

X-MedFuse: Multilingual and Efficient Multimodal Transformers for Healthcare

This project explores early mortality prediction and clinical decision support by fusing clinical notes (English, Arabic, French), structured EHR data, and medical images. The goal is to create efficient multimodal transformers suitable for low-resource environments.


Applications Invited
Students in NLP, multimodal learning, and healthcare data science.

NeuroLace: Sparse and Interpretable Continual-Learning Architecture for Edge AI

This project develops a new architecture for AI systems that can learn continuously without catastrophic forgetting, with built-in interpretability and fairness mechanisms. Target applications include robotics, autonomous agents, and sustainable AI deployment.


Applications Invited
Collaborators in reinforcement learning, continual learning, and neuroscience-inspired AI.

GRANT DETAILS

None yet.

Industry, Institute, or Organisation Collaboration

Not yet, open to collaborations.

SOCIAL, ECONOMIC, or ACADEMIC BENEFITS

My work focuses on healthcare and accessibility, aiming to reduce inequalities in low-resource environments by creating lightweight AI systems accessible on mobile devices.