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Meet David Foo, Senior Machine Vision Engineer!

Graham Duncan

Everyone at Micropack has been thoroughly impressed and delighted to work with the talented David Foo since he joined us as our new Senior Machine Vision Engineer earlier in the year.
 
David brings over 18 years of experience in algorithm development, image processing and multi-stage product innovation. David also has a PhD in Electronic and Electrical Engineering from the University of Strathclyde and multiple patents to his name, with a proven track record of taking complex ideas from concept to deployment.
 
In this quick interview, we chat to David about how he will be applying his expertise in computer vision and machine learning to advance our intelligent flame detection technologies and drive new levels of safety innovation...
 

What first drew you to machine vision and image processing?

Many years back when I was doing my PhD in applied signal processing, my peers in my research group were working on image processing systems and solutions. I always found it interesting and cool, and aspire to work in this area.

Can you share a bit about your background before joining Micropack?

I have a wide range of experience in product research and development. I have experience in ceilometer development, high speed imaging systems for microscopy, optical sensing for chemical manufacturing processes, video based non-contact monitoring pulse oximetry systems, and my most recent role was in alcohol monitoring device for offender management systems. My expertise is in algorithm development using digital signal processing and image processing.

What attracted you to this role and Micropack?

I was drawn to the area of application, and the prospects of learning and applying advanced machine vision algorithms on real life, mission critical applications. When I first met the team and the office environment, I felt warm and was able to visualise this as my place of work. 

How has your experience been since joining the team?

Excellent. The team at Micropack have great personalities, and have helped me to settle in quickly into my role, enabling me to hit the ground running. I have already had opportunities to contribute to live issues and providing solutions. 

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What’s a typical day like for you so far?

Firstly, coffee. Then looking out of the window at the sea in the horizon, with the sunrise. Being still new to the team, there is a lot of information to absorb. I plan my day based on the tasks required to strategically solve my current tasks at hand. It could be conducting imaging experiments, to writing some algorithms, or researching subject matters. I always try to recap the positive outcomes from my day before heading home, and make a note of what I should do the next day.

What excites you most about working on flame detection and vision systems?

Knowing that my work will have direct impact on saving lives. I’m keen to see where I can apply my experience in this area and make improvements to existing systems. Also learning and applying advanced AI/Machine Learning techniques that could be incorporated into existing vision systems. 

Where do you see the biggest opportunity for innovation in our field?

The advancement in hardware in-conjunction with AI and Machine Vision techniques. There are opportunities to enhance algorithm performance and extend functional capabilities. 

What’s one technical challenge you’re keen to tackle here?

Mininimisation of false positive detections, and improving detection capabilities. 

How do you like to collaborate with others on projects?

I always enjoy contributing to providing effective solutions. My preferred choice of collaboration is via in-person communication wherever possible but voice and video calls work well for me too. 

Outside of work, what do you enjoy doing?

I love my food, both cooking and eating out. I also do enjoy hiking with my dog and salsa dancing. 

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