Academic research
Ben-Gurion University
PhD research on how to evaluate feature-selection methods beyond predictive accuracy.
FROM RESEARCH TO REAL-WORLD IMPACT
I turn complex data problems into practical AI systems and measurable outcomes.
A concise visual journey through my PhD research, industrial analytics experience, and selected applied AI projects.
5visual case studies
4business domains
1research platform
THE PATH
Research, engineering, and business value on one line
This section gives the interviewer context before we open any project in detail.
Industrial analytics
Intel
Data-driven analysis of complex manufacturing systems, bottlenecks, capacity, and operational behavior.
Engineering depth + cross-functional problem solvingApplied AI & consulting
Client-facing AI projects
Machine learning, forecasting, anomaly detection, and GenAI systems translated into business workflows.
See selected projects →SELECTED WORK
Five examples I can walk through in the interview
Click any card to open the full visual. Use the arrow keys to move between case studies.
Click to open · ← → to navigate
PhD: Evaluating Feature Selection Beyond Accuracy
A research framework connecting predictive performance, stability, compactness, and generalization to real-world usefulness.
AI-Powered Product Anomaly Detection
Combining multiple weak signals to surface hidden product and operational issues earlier than single-metric monitoring.
From Scattered Data to Proactive Targeting
A context-aware model to identify customers most likely to be influenced by a campaign and support targeted activation.
Smarter Collections Forecasting
Machine-learning forecasting that combines operational data with business expertise to improve payment visibility and planning.
AI-Powered Invoice Approval System
An end-to-end document workflow for extracting information, checking business rules, identifying issues, and supporting human decisions.
HOW I USE THIS IN AN INTERVIEW
Start with the story. Open the visual only when the interviewer wants depth.
1Context — what business or research problem mattered?
2My role — what did I personally analyze, design, or build?
3Approach — what made the solution technically meaningful?
4Impact — what changed for the users or the business?
INTERVIEW PORTFOLIO
One page. Five stories. One consistent message.
I connect data, engineering, AI, and business context — and I care about making the result understandable enough to drive action.