• Qualitative and quantitative data analyst with more than ten years’ experience conducting multi-sector research in cross-functional research teams.
• Experienced with all phases of qualitative and quantitative research projects and evaluations from conceptualization to reporting.
• Acquired, cleaned, synthesized, and analyzed complex and large amounts of confidential public health and community violence data (typically >2 million records) and communicated emerging conclusions and concepts to a diverse audience at national and international conferences, meetings, written formats, and state policy makers.
• Wrote successful grants on behalf of community organizations and city municipal departments.
• Extensive training and teaching experience on-line and off-line leading workshops and lectures on diverse topics ranging from clinical care to analytics in Tableau and Excel.
• More than seven-years’ experience using SQL queries to extract data, clean it using Excel and OpenRefine, and using Tableau or PHP/MySQL for dynamic dashboard use and analysis.
• Managed and supervised diverse teams including stakeholders, students, professionals from tech, criminal justice, and social services.
• Worked as data manager in a research institute and software/quality engineer in startups in Silicon Valley prior to work in higher education. Details can be provided on request.
• Experienced at working in diverse industries including private start-up companies, social services, and higher education.
• Conducted research in Kenya, Lebanon, and the United States.
• Extensive experience working with vulnerable populations: Refugees, substance mis-use user, victims of exploitation/trafficking, and reproductive health.
• English: Fluency in writing, reading, speaking • Arabic: Native speaker
|Project name||Organization name||Social impact area||Project summary||Task name||Task status|
|Cloud Enabled Social Media Analytics Framework for Crisis Management||Artificial Intelligence and Data Engineering (AIDE)Research Center||Education||This project aims to develop a cloud-based computing framework that will systematically monitor, collect and integrate disaster and crisis-related data streams from diverse social network channels, and turn them into actionable intelligence in a knowledge-base for intelligent inferencing, crisis handling, and decision making. The key components of the proposed framework are three: First, the collection of multilingual and multimodal data (text, audios, videos, images, and location information or other sensor-data) and their conversion into a unified machine-understandable dynamic knowledge-base. Second, the application of Natural Language Processing (NLP), Data Mining, Machine Learning, and Artificial Intelligence techniques for automatic identification, prediction, and detection of any type of crisis or disaster using the unified knowledge-base from the previous stage. 3) Third, the development of an interactive dashboard visualization to facilitate crisis monitoring and management.||Event detection||Started|