Zakeya Namrud

Analyzed over 10,000 CVEs from the National Vulnerability Database to develop predictive models that increased exploit detection accuracy by 35%, enhancing proactive cybersecurity measures. Collaborated with Secureworks to design and implement machine learning algorithms capable of identifying emerging vulnerabilities, resulting in a 40% reduction in false positives and faster threat mitigation. Integrated diverse threat intelligence sources into the research framework, improving vulnerability prediction coverage by 28%, thereby strengthening system defenses against potential exploits.

Honoured with a Certificate of Appreciation for driving innovation in AI and Generative AI research. Contributed to strategic planning that expanded conference participation by 40% over previous years. Facilitated dissemination of cutting-edge research by reviewing over 150 submissions, ensuring a peer review accuracy rate exceeding 98%, enhancing the conference’s reputation for scholarly rigour. Led organizational efforts as Session Chair and Workshop Presenter, coordinating 20+ sessions that increased attendee engagement by 35% and promoted cross-disciplinary collaboration among industry leaders and academic experts.

Led pioneering research on Large Language Models (LLMs) for Failure Management in Cloud Native Applications, resulting in the publication of 5 peer-reviewed papers and establishing a novel framework that improved failure detection accuracy by 30% across diverse cloud environments. Supervised PhD and Master's students, guiding experimental design and data analysis that contributed to 3 groundbreaking theses recognized for innovation, while increasing student publication output by 50%. Collaborated with industry partners to translate research findings into practical applications, contributing to a project that reduced system downtime related to failure management by 25%, thereby enhancing service reliability in enterprise cloud platforms.