Researcher & Student at SCH
+82 10 6443 9922
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.
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
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)
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.
International Journal
[2024]
Emmanuel Edward Ngasa, Mi-Ae Jang, Servas Adolph Tarimo, Jiyoung Woo, and Hee Bong Shin, " Diffusion-based Wasserstein generative adversarial network for blood cell image augmentation," Engineering Application of Artificial Intelligence, 2024.
[2023]
Servas Adolph Tarimo, Mi-Ae Jang, Emmanuel Edward Ngasa, Hee Bong Shin, HyoJin Shin, and Jiyoung Woo, " WBC YOLO-ViT: 2 Way - 2 Stage White Blood Cell Detection and Classification with a Combination of YOLOv5 and Vision Transformer," Computers in Biology and Medicine, 2023.
International Conference
[2022]
Servas Adolph Tarimo and Jiyoung Woo, "White Blood Cell Detection and Classification using YOLOv5 with Hybrid ResNet50-VGG-16-SVM," in Proceedings of the 6th International Conference on ICT for Smart Health & Home (ICT4sHealth & Home), Kota Kinabalu, Malaysia, December 18–22, 2022. (Presentation: December 19)