Yacin Nadji
Lead Security Researcher at Corelight
Washington, District of Columbia, United States
Summary
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Senior🎓
Top SchoolYacin Nadji is a Lead Security Researcher with 17 years of experience blending data analysis, machine learning, and network security to detect and remediate advanced threats. Based in Washington, DC, he progressed through research and engineering roles at Corelight and SecurityScorecard, transitioning academic machine learning prototypes from Georgia Tech into production for DoD networks. He contributes to prominent open-source projects like Zeek, improving backend functionality, tests, and performance for a widely used network analysis framework. Comfortable at the intersection of research and product, he has hands-on expertise in attribution, telemetry, and building auditable detection pipelines. Notably, his background spans a Ph.D. in computer science and practical deployment of ML in high-assurance environments, reflecting both deep theory and operational rigor.
16 years of coding experience
9 years of employment as a software developer
Ph.D. Computer Science, Ph.D. Computer Science at Georgia Institute of TechnologyB.S. Computer Science, B.S. Computer Science at Illinois Institute of Technology
Chinese, French, Spanish, English
Stackoverflow
1reputation
0reached
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Github Skills (7)
zeek10
network-monitoring10
testing10
security9
c-language8
cprogramming-language8
python4
Programming languages (13)
Github contributions (5)
zeek/zeek
Sep 2020 - Aug 2022
Zeek is a powerful network analysis framework that is much different from the typical IDS you may know.
Role in this project:
Backend & QA Engineer
Backend & QA EngineerContributions:2 reviews, 16 commits, 8 PRs in 1 year 10 months
Contributions summary:Yacin contributed to the Zeek network analysis framework by implementing new functionality and improving existing features. Their work includes adding new bif functions, such as `count_to_double` and `int_to_double`, as well as adding tests to ensure the validity of functions and correct behavior. Furthermore, the user addressed code quality by fixing errors, improving tests, and refactoring portions of the codebase to address issues with formatting, specifically with tabs. They also added test cases and improved performance.
dfirnsmsecurityanalysisnetworking
ynadji/hands-on-adversarial-ml
Jan 2019 - Aug 2020
As seen at DEF CON and O'Reilly Live Trainings
Contributions:46 commits, 2 PRs, 17 pushes in 1 year 6 months
conmachine-learningtrainingsdefo-reilly
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