Hi, I’m Chen Hajaj, a faculty member of the Industrial Engineering and Management department at Ariel University. In addition, I serve as the head of the Data Science and Artificial Intelligence Research Center, and as a member of the Ariel Cyber Innovation Center.
My research activities are in the areas of Game Theory, Machine Learning, and Cybersecurity. Specifically, the focus of my work is on how to detect and robustify the weak-spots of AI methods (adversarial artificial intelligence), and the intersection of game theory and human decision-making (incentive design).
The methods I develop include theoretical, economical, and computational analysis, in parallel with an algorithm development for implementing such mechanisms in practice. On top of that, I conduct an experimental evaluation with human subjects to asses the applicability of my developed methods to real-life scenarios. For more information, please see my CV and Google Scholar page.
I am looking for excellent prospective M.Sc. and Ph.D. students, that are interested in working on fascinating and applicable research problems. Interest, challenge, and great benefits are promised.
Before joining Ariel University, I was a postdoc scholar at Vanderbilt University, hosted by Prof. Yevgeniy Vorobeychik. During this time, I was happy to serve as a Data Science fellow at the Data Science Institute of Vanderbilt University. I earned my Ph.D. in Computer Science at Bar-Ilan University where I was lucky to have Prof. David Sarne and Prof. Avinatan Hassidim as my advisors.
Chen Hajaj, Ph.D.
Department of Industrial Engineering and Management
Data Science and Artificial Intelligence Research Center
Ariel Cyber Innovation Center
Encrypted Video Traffic Clustering Demystified. In Computers & Security [Impact Factor: 3.062]
Evasion is not enough: A case study of Android malware. In the 4th International Symposium on Cyber Security Cryptology and Machine Learning (CSCML 2020)
Robust Malicious URL Detection. In the 4th International Symposium on Cyber Security Cryptology and Machine Learning (CSCML 2020)
The Different Path to Purchase of Mobile and Desktop Consumers: Analyzing Consumers’ Progress in the Conversion Funnel Using Hidden Markov Models. Statistical Conference in E-Commerce Research (SCECR 2020) [Video]
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features was accepted to the 28th USENIX Security Symposium (USENIX Security ’19 Winter)
Our paper on Adversarial Coordination on Social Networks has been accepted for publication in the proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019).