Algoma University · Canada

Engineering intelligencefor the physical world

We build intelligent, secure, and reusable embedded software for autonomous and cyber-physical systems.

Research visionFrom algorithms to systems that sense, decide, and act.
Autonomous systems Edge AI Secure systems
EXPLORE NEST
Our research

Software for systems
that cannot afford to fail

NEST connects software engineering, machine intelligence, and physical testbeds. Our work moves from foundational methods to deployable systems in aerospace, automotive, energy, and robotics.

Embedded AI at the Edge

Hardware-aware training, partitioning, and inference methods that bring capable machine learning to constrained edge platforms.

Secure & Safe Embedded Systems

Testing, calibration, and runtime assurance for systems where timing, safety, and reliability are non-negotiable.

Autonomous Systems

Perception, planning, health monitoring, and resilient software pipelines for UAVs, vehicles, and robotic platforms.

Smart Energy Systems

Embedded optimization and intelligent control for grid resilience, battery energy storage, and IoT monitoring.

Software Reuse & DevOps

AI-assisted feature discovery and certification-aware delivery pipelines for long-lived embedded software.

Cyber-Physical Systems & Digital Twins

Model-driven integration of sensing, computation, networking, and physical processes for observable and resilient intelligent systems.

Parallel Computing & Task Scheduling

Resource-aware scheduling, workload partitioning, and parallel execution across multicore, GPU, edge, and cloud platforms.

Selected publications

Ideas, tested and shared

Browse all publications →
2026
Conference · Embedded AI & Security

Constraint-Aware Exploitability Analysis and Anomaly Recovery for CPS CI/CD Pipelines

G. Camargo and M. A. Maruf

IEEE ICMLA 2026 · Accepted
2024
Conference · DevOps

Work-in-Progress: ESOps—An Agile Pipeline for Next-Generation Embedded Systems Development

M. A. Maruf and A. Azim

IEEE/ACM EMSOFT
2024
Conference · Software reuse

FeaMod: Enhancing Modularity, Adaptability and Code Reuse in Embedded Software Development

M. A. Maruf, A. Azim, N. Auluck, and M. Sahi

IEEE IRI — Best Paper Award
Open opportunities

Build the next generation
of embedded intelligence

We welcome ambitious graduate students, undergraduate researchers, visiting scholars, and industry collaborators.

Find your opportunity