I'm Alemayehu Bekele. I help teams find the story inside complex programs, messy data, and high-stakes decisions.
Senior Researcher - Evaluation, applied research, measurement, analytics, and applied AI
Pronounced ah-leh-MY-yoo beh-KEH-leh
Programs are rarely simple. Some evaluations begin at the drawing board, alongside partners building a theory of change, logic model, measures, and learning plan before a program takes shape. Others begin midstream, when data is scattered, assumptions have shifted, and the question is how to connect the dots without flattening the realities behind the numbers.
When data is thin, the next step is to get closer to the work and listen. Across learning, wellbeing, and opportunity, from STEM access and multilingual learning to prevention, veterans' outreach, and workforce pathways, my focus is practical evidence that respects context, can be defended, and helps teams decide what to keep, what to change, and what to build next. Applied AI supports parts of that process, but the center is still people, judgment, and the shared story we build together.
Frame the work. Co-develop the theory of change, logic model, measures, and evaluation questions with partners so the evaluation grows from the program, not as a separate exercise.
Make the evidence usable. Bring together surveys, interviews, dashboards, administrative records, documents, and analytic tools to see patterns, gaps, and choices more clearly.
Stay close to context. When data is thin or incomplete, listen to practitioners, participants, families, and partners closest to implementation so the story reflects what is happening on the ground.
For evaluation design, quantitative data strategy, measurement systems, survey analytics, dashboards, mixed-methods research, or applied AI integration in research workflows, .
- Evaluation design
- Quantitative analysis
- Measurement systems
- Mixed-methods research
- Applied AI workflows
Waltham, Massachusetts - Research, evaluation, teaching, and applied AI