I develop robotic and signal processing solutions; both hardware and software, specialising in learning-based robotic perception.
I enjoy solving deeply technical problems and am driven by those which have potential for commercial impact.
I am compelled to use my passion for engineering to create a positive technological impact on the world.Contact
I provide consulting and advisory services on the topics of robotics and machine learning to industrial businesses. Pairing industrial equipment and robotic hardware with sensors and perception algorithms to enable machines to perceive the world visually.
I help industrial companies set and reach their automation goals, facilitating the implementation of advanced robotics and machine learning technologies. This requires bridging the gap between authentic customer needs, emerging research, mathematical models, industry trends, and deployment of scalable automation solutions.
I enjoy building collaborative relationships with clients and partners in the manufacturing, logistics, and mining sectors. I help clients achieve operational excellence through the deployment of data-driven industrial automation solutions.
What am I passionate about?
The following are my areas of competency.
Enabling machines to learn by uncovering distinctive patterns and features in data.
Applying perception algorithms to robots so that they become more adaptive.
Applying computer vision techniques to automatically extract information in imagery and video.
Bridging the divide between the virtual world and the physical world.
In my Honours Thesis (2021) I developed a novel machine learning solution inferring scene semantics from video, this work was awarded 100/100 and has “publishable" elements.
Prior to this, I developed my research experience through 3 years of employment in the Machine Learning and Data Analytics Lab at the UTS Global Big Data Technologies Centre (GBDTC). I was working alongside 30 PhD students, research students, and postdocs.
At GBDTC I undertook theoretical training on the mathematics of deep learning, and I co-authored a deep learning paper. This theoretical work was complemented by machine learning practitioner work for multinational clients and government, ultimately yielding a strong link between praxis and theory.
As further indication of my research competency I provide this external comment from my honours thesis supervisor:
"This report represents a very high standard of work that I have not previously encountered at the undergraduate level. It is not an exaggeration to state that many postgraduate reports and journal articles do not reach the standard of this work. Alex has implemented a novel system for scene classification from scratch and merged together existing techniques as well as developing new, original algorithms and showing an improvement of the work." ~ Associate Professor Stuart Perry
I have a demonstrated history of leading teams in various professional contexts.
I strive to delegate where possible to ensure that deliverables are completed on time and on budget with a high standard of quality. Maximising the alignment between the tasks and individual competencies, interests.
I strive to lead with radical truth and transparency, illuminating issues as they arise rather than waiting idly for underlying problems to eventuate as serious issues. Addressing concerns can also require fronting with tough love, and conflict resolution.
I typically lead teams ranging in size of 3 to 10 people. Notable examples include, leading a cross-functional team of Data Scientists & Business Analytsts at my GBDTC role, and weekly reporting team progress to the client CTO. Leading a team of 6 engineers and creative innovation students at my first company. I have also lead educational workshops on robotics and machine learning technologies, asking questions to students to gain their perspective and improve delivery.