I am a Doctor of Computer Science student at the University of the Potomac with a multidisciplinary background in artificial intelligence, data science, predictive analytics, cybersecurity, software quality assurance, and business technology.
My current research and development work focuses on using AI, machine learning, public data, and responsible analytics to study business growth, startup readiness, equitable access to capital, and economic opportunity in the United States.
I am particularly interested in building reproducible, explainable, and fairness-aware AI systems that can support research, entrepreneurship, and evidence-based decision-making.
- Artificial Intelligence and Machine Learning
- Predictive Analytics and Decision Support Systems
- Explainable AI and Responsible AI
- Fairness-Aware Modeling and Equity Analysis
- Entrepreneurship and Small-Business Analytics
- Capital Access and Economic Opportunity
- Data Science and Public-Data Engineering
- Software Quality Assurance and Test Automation
- Cybersecurity and Technology Risk
An open-source geospatial research platform for analyzing small-business activity, capital-access conditions, economic opportunity, and entrepreneurial ecosystems across U.S. geographies using authoritative public data.
Highlights:
- U.S. Census County Business Patterns integration
- State- and county-level geographic analysis
- Certified CDFI data integration
- Interactive Streamlit research application
- Reproducible validation workflows
- Published Python package on PyPI
- Versioned GitHub release and Zenodo DOI
🔗 View Repository
🌐 Live Application
📦 PyPI Package
📚 Zenodo DOI
A reproducible AI research platform for predictive capital-readiness analysis, model explainability, fairness auditing, and equity-aware capital-allocation simulation for underserved U.S. entrepreneurial ecosystems.
Highlights:
- Predictive machine-learning pipelines
- Explainable AI and local sensitivity analysis
- Fairness and structural-context auditing
- Capital-allocation simulation
- Interactive Streamlit dashboard
- Automated testing and GitHub Actions CI
- Published Python package and versioned release
🔗 View Repository
🌐 Live Application
📦 PyPI Package
A React-based application designed to help startups evaluate investor readiness across multiple business dimensions and explore structured recommendations for improvement.
- QA Selenium Smoke Tests — Selenium and Pytest-based browser automation examples.
- API Testing Automation — Automated REST API testing using Python, Requests, and Pytest.
- Sales Insights Dashboard — Data analysis and business-insight workflows using Python and visualization tools.
My academic and professional work sits at the intersection of computer science, artificial intelligence, business analytics, cybersecurity, and software quality. I am interested in research that connects technical innovation with practical economic and organizational problems.
Current priorities include:
- developing reproducible AI research software;
- integrating authoritative U.S. public datasets;
- improving explainability and fairness analysis;
- studying capital access and entrepreneurial ecosystems;
- expanding independent replication, review, and external-use evidence; and
- translating research ideas into usable open-source tools.
I welcome constructive feedback, independent replication, technical review, research collaboration, and responsible open-source contributions related to my projects.
📧 Email: sakerasiu23@gmail.com
🎓 Google Scholar: View Research Profile