# About Me

MB Jallow is an expereince Data Sceince and Machine learning engineer with a unique blend of public health. Currently pursuing a Master’s in Digital Transformation mainly focusing on conducting groundbreaking research in cardiovascular risk assessment and federated learning in cancer encology.

Skills & Technologies

  • Frontend: React, Vue, Astro, Angular, HTML, CSS, Tailwind, Bootstrap
  • Backend: Node.js, Python, Flask, FastAPI, Django, Express
  • Machine Learning: Scikit-learn, TensorFlow, PyTorch, OpenCV,
  • Data Analysis: Pandas, NumPy, Matplotlib, Seaborn, Plotly
  • Cloud: AWS, Azure, Google Cloud, Cloudflare, Vercel, Grafana
  • Databases: PostgreSQL, MongoDB, MySQL, SQLite, Redis
  • Mobile: React Native, Flutter
  • Blockchain: Ethereum, Solidity, Web3.js, Hardhat
  • IoT & Embedded Systems: Arduino, Raspberry Pi, ESP32, Expressive
  • Dev Tools: Git,Github Actions, Docker, Kubernetes, CI/CD, Jenkins

Education & Academic Background

  • MSc Digital Transformation (2023 - Present) - University of Applied Sciences and Arts Dortmund, Germany
  • BSc Computer Science & Informatics (2017 - 2021) - University of The Gambia, Banjul, The Gambia
  • Higher National Diploma in Public Health (2012 - 2014) - Gambia College, Banjul, TRhe Gambia

My Journey

MB Jallow’s career trajectory tells a compelling story of career transformation. He started as a healthcare practitioner then switch to computer science and informatics. Beginning as a Public Health Official with The Gambia’s Ministry of Health and Social Welfare (December 2012 - June 2017), he oversaw healthcare data and immunization programs (EPI) management at regional facility level, and disease surveillance initiatives. This foundational experience in healthcare systems provided him with real-world experience into medical challenges.

Recognizing the transformative potential of technology in healthcare, MB transitioned into software engineering and information systems. As a Software Developer, he developed intuitive data visualizations and collaborated on Industry 4.0 initiative projects at Guangzhou Panyu Zhifeng Microelectronics Co., Ltd (November 2018 - January 2020).

His expertise in machine learning emerged during his role as a Machine Learning Engineer with Omdena (January 2020 - March 2021), where he developed natural language processing models for the World Energy Council. This remote role with other international data scientists expanded his technical and collaborative expertise.

The entrepreneurial phase of his career began with product management roles at Marshub and SeedStudio in Shenzhen, China. As Product Manager and later Product Marketing Manager (March 2021 - October 2022), he led multinational development teams, develop products, and oversaw full-stack IoT product development. His ability to bridge technical complexity with market needs earned him recognition as Outstanding Employee of the Year in 2022.

Currently serving as Research Associate at Dortmund University of Applied Sciences and Arts (April 2024 - December 2024), MB leads development of barrier-free web platforms and conducts advanced research in cardiovascular risk prediction. Additionally, he works part-time as a Corporate Trainer with The Knowledge Academy, delivering specialized training in content marketing, SEO, project management, and data analysis.

Research & Technical Expertise

MB’s research portfolio demonstrates sophisticated understanding of healthcare technology applications. His current Master’s thesis, “Predictive and Causal Machine Learning for Cardiovascular Risk Stratification in the Heinz Nixdorf Recall Study” and research porject “Implementation of a Web-based Application for HNR Coronary Artery Calcification Progression Prediction and Integrated Cardiovascular Risk Assessment”, addresses critical gaps in clinical decision support systems. The project integrates HNR, MESA-ASCVD, and Framingham risk models into a unified, privacy-preserving platform.

Previously, his Bachelor’s thesis on “Optimized Genetic Algorithm for Influence Maximization in Social Networks using Random Reverse Reachable Sampling” (2021) demonstrated his ability to tackle complex algorithmic challenges with practical applications in social network analysis.

His publication record includes peer-reviewed papers such as “Examining the Impact of Team Dynamics in Agile Project Management Success in Software Development: A Systematic Literature Review” (IEEE International Conference, 2023) and “Blockchain for Healthcare: A Patient-Centric Data Management Framework for Smart City Ecosystems” (IEEE European Technology and Engineering Management Summit, 2025).