Passy Miano

• Spearheaded the development of the Citizen Engagement Platform, a digital tool to enhance civic participation across Africa.
• Led survey operations by working with African Union departments, Regional Economic Communities (RECs), and member states to design, customize, and structure surveys tailored to diverse country contexts.
• Authored and maintained survey instruments using internal tools; tested surveys for logic and functionality, ensuring a seamless user experience and technical soundness.
• Uploaded surveys to the platform, monitored live survey performance, and responded quickly to technical issues or feedback from field teams.
• Standardized data collection through the creation of a dynamic survey framework and related SOPs, improving quality control and enabling scalable deployment across multiple pilot countries.
• Collaborated closely with stakeholders to iterate on survey content and design, integrating real-time feedback into successive versions.
• Facilitated capacity-building workshops for partner institutions and field staff to ensure proper survey administration and data integrity.

Key Achievements:

• Successfully piloted the platform in three AU member states, with high survey completion and engagement rates.
• Developed and implemented a comprehensive survey management workflow, significantly reducing errors and turnaround time.
• Produced performance dashboards and monitoring reports using identified KPIs, enabling evidence-based decision-making across departments.

• Developed interactive dashboards using Power BI to enhance project monitoring.
• Managed data lifecycle processes to facilitate informed decision-making.

Key Achievements:

• Designed and implemented user-friendly dashboards, improving monitoring and evaluation capabilities.
• Streamlined data management processes, ensuring accuracy and efficiency in reporting and decision-making.
• Played a key role in fostering a culture of digital innovation within the organization, contributing to its long-term success.

• Conducted in-depth research on time-frequency analysis/Machine learning and its application to carbon market forecasting.
• Designed and developed a hybrid forecasting model that combined multiscale analysis techniques with machine learning models.

Key Achievements

• Achieved significant improvement in the prediction accuracy of carbon prices through the implementation of the hybrid model with Mean Squared Error of 0.049.
• Published a research paper in the prestigious Annals of Operations Research, contributing to the field's knowledge and showcasing expertise in the domain.