About
I am a Robotics and Physical AI Engineer focused on building intelligent robotic systems that operate reliably in real-world environments.
My work spans autonomy, perception, manipulation, robot learning, ROS 2, multimodal AI and VLA systems, with a systems-level focus from perception and planning through safety, recovery and deployment.
I bring several years of technical leadership and systems integration experience across architecture, deployment, troubleshooting, reliability and team coordination. This background positions me to lead technical direction across multidisciplinary robotics systems, from architecture and technology decisions through integration, validation and deployment.
I approach robotics as an end-to-end product and systems problem, with particular focus on Physical AI, manipulation, autonomous systems and service robotics.
Education
University of Toronto
Master of Engineering
Robotics, AI & Data Analytics
University of Ottawa
Bachelor of Applied Science
Mechanical Engineering
Independent Robotics Systems Consultant
Robotics systems, product definition and prototype strategy
I support early-stage robotics and automation initiatives by turning operational problems into executable technical plans. My work spans feasibility analysis, requirements definition, system architecture, platform selection, prototype planning and implementation strategy.
I translate real workflows into hardware and software requirements, subsystem boundaries, sensing and compute needs, cost and risk trade-offs, build-versus-buy decisions, and phased development roadmaps. I also define integration and prototype-validation strategies that surface technical unknowns early and establish measurable gates for further investment.
Focus areas
- Feasibility and requirements definition
- Robotics system and subsystem architecture
- Hardware, sensing and compute selection
- Build-versus-buy and cost/risk analysis
- Prototype and integration planning
- Validation criteria and phased product roadmaps
From Prototype to Reliable System
I approach robotics as an end-to-end product and systems problem rather than a collection of isolated algorithms. That means defining interfaces clearly, integrating software with physical hardware, instrumenting system behaviour, testing failure modes, maintaining bounded recovery and fallback behaviour, and expanding autonomy only when measured performance supports it.
- 01
Define
- 02
Architect
- 03
Build
- 04
Integrate
- 05
Instrument
- 06
Validate
- 07
Deploy
- 08
Improve
Measured capability
A command being accepted is not the same as the physical outcome being verified.
Bounded autonomy
Intelligent behaviour should operate inside explicit capability, safety, recovery and fallback boundaries.
Progressive complexity
Increase task and environmental complexity only after the underlying capability is sufficiently reliable.