Staff Data Scientist

Obsidian Security, USA  |  (2025 – Present)

As a Staff Data Scientist, I work on AI initiatives focusing on applied AI research, including generative AI, to enhance security solutions for SaaS environments.

Security Data Scientist

Obsidian Security, USA  |  (2022 – 2025)

As a Security Data Scientist at Obsidian Security, I focused on developing advanced threat detection models, alert monitoring systems, and collaborating on applied AI research for cybersecurity solutions in SaaS environments.

• Researched, designed, and implemented multiple threat detection models for SaaS (Software as a Service) environments.
• Developed and maintained monitoring solutions to assess the ongoing performance of threat detection models.
• Contributed to data-driven security posture management solutions, including data extraction, modeling, and orchestration tasks.

PhD Researcher

State University of New York at Binghamton, USA  |  (2018 – 2022)

Dissertation: "Empowering Artificial Intelligence for Cybersecurity Applications"

During my PhD, I focused on using AI and machine learning for improving cybersecurity. My research aimed to design real-time models for anomaly detection, improving explainable malware classification, and applying blockchain to enhance data integrity in scientific workflows.

• CFGExplainer: Developed a model that explains malware classification decisions made by Graph Neural Networks (GNNs).
• Log-Anomaly-Mask: Designed a real-time adversarial evasion attack against deep learning-based system log anomaly detection.
• RAMP: Built a real-time machine learning model for anomaly detection in streaming multivariate time series data.
• SciBlock: Utilized blockchain technology to enable tamper-proof storage and improved reproducibility in scientific workflows.

Graduate Research Assistant

State University of New York at Binghamton, USA  |  (2017 – 2018)

In this role, I conducted research in wireless networks, focusing on deep learning for wireless channel quality prediction and Markov models for routing in cache networks.

• DeepChannel: Developed an LSTM/GRU-based deep learning model for wireless channel quality prediction.
• Opportunistic Routing in Cached Wireless Networks: Designed a Markov model to analyze routing behavior in wireless cache networks.