Open to opportunities

Muhammad Saqib

@muhammadsaqib8

Message

Cybersecurity researcher building AI-driven threat models and detection pipelines for critical infrastructure.

Canada

Message

What I'm looking for

I’m looking for a role where I can build detection engineering and threat hunting that’s measurable in MTTD and false-positive reduction—leveraging AI orchestration, SIEM/IDS rule generation, and intel-driven analytics for real-world critical infrastructure.

I’m a cybersecurity researcher focused on operational security—unifying threat modeling, detection, and hunting to protect critical infrastructure against advanced persistent threats (APTs). I’m currently building an agentic AI orchestration layer that fuses SIEM logs, threat feeds, and OSINT into adaptive, asset-aware threat models that represent the dynamic attack surface.

I also design intel-driven detection engineering pipelines that translate evolving threat models into SIEM/IDS rules, improving detection coverage while reducing false positives and alert noise. In parallel, I implement hypothesis-driven threat hunting workflows to surface early compromise signals and drive proactive adversary behavior discovery that targets meaningful MTTD reduction.

Experience

Work history, roles, and key accomplishments

Current

Cybersecurity Researcher

Current

TRIME Lab, Université du Québec à Montréal

Feb 2026 - Present (4 months)

Conducts research in operational security by unifying threat modeling, detection, and hunting to protect critical infrastructure, targeting 10x performance improvements against advanced persistent threats. Builds an agentic AI orchestration layer and designs intel-driven detection engineering pipelines to improve detection coverage, reduce false positives, and proactively reduce MTTD.

Operational Security Threat Modeling Detection Engineering Threat Hunting Agentic AI OSINT Threat Intelligence

Doctoral Researcher

TRIME Lab, Université du Québec à Montréal

Sep 2020 - Aug 2025 (4 years 11 months)

Designed large-scale feature engineering pipelines for memory/compute-constrained programmable data planes, improving resource utilization and reducing inference time. Developed in-network data-driven classifiers with ≈70% performance gains under evolving and adversarial traffic, and secured data processing pipelines using end-host profiling against spoofing-based resource exhaustion attacks.

Feature Engineering In Network Machine Learning Adversarial Traffic Modeling End Host Profiling Data Plane Security

Research Assistant

Jeju National University

Mar 2020 - Aug 2020 (5 months)

Designed a cloud-native architecture using OpenStack and OSM-MANO for vehicular networks, enabling continuous analytics and maintaining up to 95% data availability during intermittent disconnections. Improved connected vehicle reliability via robust edge–cloud integration and data management, reducing data loss and synchronization delays by 20–30%.

OpenStack OSM MANO Cloud Native Architecture Cloud Edge Integration Synchronization Reliability Engineering

Education

Degrees, certifications, and relevant coursework

University of Quebec Montreal (UQAM)

Doctor of Philosophy (PhD), Computer Science

2020 - 2025

Activities and societies: Supervisor: Prof. Halima Elbiaze; thesis work on intelligent and secure programmable network traffic management.

PhD research focused on intelligent and resilient traffic management mechanisms for programmable networks under a fully funded scholarship and research grants.