Hi,
I'am Hyston
AI
Research
Engineer
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Professional Summary
AI Research Engineer with over 3 years of experience in discriminative and generative AI, including recommen dation systems, computer vision, large language models (LLMs), and Retrieval-Augmented Generation (RAG). Also skilled in full-stack development, with over 4 months of hands-on experience using React, Next.js, and the MERN stack (MongoDB, Express.js, React, Node.js). Passionate about leveraging AI in an industrial setting to tackle real-world challenges and promote business growth.
Skills
Profesional Skills
Solid foundation in machine learning, deep learning, and data analysis, spanning both theoretical concepts and hands-on implementation.
Projects
AI Personal Portfolio Chatbot
Developed a personal portfolio chatbot using LangChain, OpenAI, and Qdrant. The chatbot provides personalized responses to user queries about my portfolio, leveraging RAG techniques to retrieve relevant information from my documents and GitHub repositories. This project showcases my skills in AI-driven conversational agents and enhances user engagement with my portfolio.
Tools: Next.js, TypeScript, LangChain, OpenAI, Qdrant, Firebase, Vercel.
XR Twin-based Rehabilitation Training Content Technology Development project
Contributed to this project (IITP/MSIT-funded, Project No. 2022-0-00218), focusing on AI-driven rehabilitation technologies. Performed data preprocessing, analysis and visualization. Developed a hybrid model for heart rate prediction combining Dynamic Bayesian Networks (DBNs) and LSTMs. Achieved an avarage of 5.1 BPM MAE in prediction accuracy.This model is to assist in XR Twin project in AI custom coaching by providing personalized recommendations based on heart rate data.
Tools: Python, PyTorch, Jupyter, NumPy & Pandas, Matplotlib & Seaborn, scikit-learn.
Feature Selection Tool
Developed a Python-based Feature Selector Tool for automated data preprocessing, feature impor tance analysis, and visualization, supporting classification and regression tasks. This tool streamlines the feature selection process, enhancing model performance and interpretability. It includes functionalities for data loading, preprocessing, feature importance ranking, and visualization.
Tools: Python, Scikit-Learn, Feature-Engine, Seaborn, Matplotlib.
Mthandizi: Communication Tool for the Deaf
Mthandizi is a software solution designed to translate hand gestures into text or speech, and conversely, convert text or speech into hand gestures. This project aims to bridge communication barriers for deaf students in Malawi, enhancing accessibility in educational settings. Additionally, it aspires to expand into broadcasting stations, providing real-time sign language translation of news for the deaf community.
Tools: Python, CNN, OpenCV, Jupyter, PySide, PyQt5
Education
MSc in Computer Science and Engineering
Soongsil University, Seoul, South Korea (2022 – 2025)
Specialized in Machine Learning and Deep Learning and Research.
Thesis: “A Multi-Model Machine Learning Framework for Personalized Fitness Recommendations
Using DBNs and LSTMs"
BSc in Information Communication Technology
Daeyang University, Lilongwe, Malawi (2017 – 2021)
Focused on systems design and development.
Final Year Project: Designed and implemented an AI-powered tool to translate speech and text into sign language, enhancing communication for deaf students.
Publications
A Hybrid Approach to Modeling Heart Rate Response for Personalized Fitness Recommendations
📘 Journal: Electronics, Vol. 13, Issue 19 (2024)
Abstract: This paper proposes a hybrid model combining DBNs and LSTMs to personalize heart rate prediction for adaptive fitness guidance.
ProAdaFs: Probabilistic and Adaptive Feature Selection in Deep Recommendation Systems
📍 Conference: ICOIN 2024, Vietnam
Abstract: ProAdaFs introduces a probabilistic adaptive feature selection layer within deep recommendation models to improve both performance and interpretability.
Deep Adaptive Feature Selection in Deep Recommender Systems
📍 Conference: Korean Society of Information Science, Jeju (2023)
Abstract: This work explores a deep learning architecture embedded with adaptive feature selection for scalable and efficient recommendation systems.
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