Bio

• Servas Adolph is a PhD student in the Department of Future Convergence Technology at Soonchunhyang University, South Korea. He received his Master's degree in Big Data Engineering from Soonchunhyang University in 2023, and a Bachelor's degree in Computer Engineering and Information Technology from the United African University of Tanzania. His research interests primarily focus on multimodal machine learning for healthcare, health data analytics, and artificial intelligence. With a strong academic background and experience in software engineering, Servas is dedicated to advancing technology and implementing innovative solutions in healthcare and related domains. 

Skills

• Programming Languages: Python, Java, C++

• Machine Learning & AI: Tensor Flow, Keras, Scikit-Learn, Yolo, GANs

• AI Automation & ChatBot: Lang chain, Natural Language Processing (NLP)

• Data Analysis: Healthcare Data Analytics, Statistical Modeling, Multimodal Data Analysis

• Tools: Jupyter Notebook, Vscode, GitHub

• Healthcare Expertise: HER Data Analysis, White Blood Cell Classification 

Education

• Ph.D. in Big Data Engineering at Soonchunhyang University (2023.09 - Present)

  • Advisor: Prof. Woo Ji-Young

  • Lab: Advanced Data Mining

• M.S. in Big Data Engineering at Soonchunhyang University (2021.09 - 2023.08)

  • Dissertation: WBC YOLO-ViT: 2 Way - 2 Stage White Blood Cell Detection and classification with a combination of YOLOv5 and Vision Transformer 

  • Advisor: Prof. Woo Ji-Young

• B.S. in Computer Engineering and Information Technology at The United African University of Tanzania (2017.01 - 2020.08) 

Research Experience

• 2021 - 2023)

  • Advisor: Prof. Woo Ji-Young

  • Lab: Advanced Data Mining

  • White Blood Cell Classification and Segmentation

    • Conducted research on the classification and segmentation of white blood cells using machine learning models. This work focused on improving the accuracy of hematological diagnostics through advanced image-processing techniques

  • Generative Adversarial Networks (GANs) for Medical Data

    • Developed and implemented GANs to generate realistic white blood cell images. This project aimed to augment limited medical datasets with synthetic yet high-quality data, contributing to enhanced machine-learning performance in medical diagnostics

• 2023 - Present

  • Advisor: Prof. Woo Ji-Young

  • Lab: Advanced Data Mining

  •  Emotion Detection Using Multimodal Modalities (Current)

    • Ongoing project focused on detecting emotions using multimodal data (text, audio, and images) across multiple languages. This work integrates diverse data types to enhance the accuracy of emotion recognition, contributing to the fields of emotional AI and human-computer interaction.

  •  Mental Health Analysis

    • Developing machine learning models for analyzing mental health conditions. This project focuses on detecting early signs of mental health issues using data-driven approaches, aimed at supporting early intervention and personalized care solutions. 

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