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servasadolph/README.md

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About Me

I am a Medical AI researcher and PhD student in the Department of Future Convergence Technology at Soonchunhyang University, South Korea. My work sits at the intersection of deep learning, multimodal sensing, and clinical AI — with a focus on systems that are high-performing, interpretable, and deployable in real-world healthcare environments.

I grew up in Tanzania, where access to specialist healthcare remains limited. That experience shapes everything I build. My goal is to develop AI that meets healthcare where it is — not just where infrastructure is ideal.


Research Areas

Area Focus
Domain Adaptation Cross-domain generalization for medical imaging and healthcare data
Multimodal Emotion Recognition Fusing EEG, audio, and facial expressions for mental health analysis
WBC Detection & Classification Automated hematology using YOLO and Vision Transformers
Medical Report Generation Structured clinical documentation via fine-tuned LLMs
RAG for Clinical Decision Support Retrieval-augmented systems grounded in verified medical knowledge

Research Vision

I believe the next generation of clinical AI should serve as an intelligent assistant — extending the capabilities of healthcare workers, not replacing them. My long-term mission is to build intelligent multimodal AI systems that improve clinical workflows and healthcare accessibility in low-resource settings, starting with Tanzania and sub-Saharan Africa.


Selected Projects

Multimodal Emotion Recognition A deep learning system fusing EEG, audio, and facial expression signals for mental health monitoring and early detection of emotional disturbances.

WBC Detection & Classification An automated pipeline for white blood cell detection, counting, and classification using YOLO and Vision Transformers — designed for resource-constrained clinical labs.

Medical Report Generation An LLM-based system producing structured clinical reports and doctor-style patient summaries from diagnostic inputs.

RAG for Clinical Decision Support A retrieval-augmented generation system grounding AI clinical responses in verified medical literature, targeting deployment in low-resource healthcare settings.


Tech Stack



 

Education

PhD, Future Convergence Technology — Soonchunhyang University, South Korea (in progress) MSc, Big Data Engineering — Soonchunhyang University, South Korea (2023) BSc, Computer Engineering & Information Technology — United African University of Tanzania


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