Gateway to Translation Webinar: Deepgate
The Gateway to Translation (G2T) series is a collaborative initiative led by the University of Cambridge’s Cambridge Academy of Therapeutic Sciences (CATS) and the University of Manchester’s Translation Manchester.
These monthly sessions explore ways to accelerate real-world impact by translating research into clinical practice. Each webinar showcases emerging trends, technologies, and commercially focused research, helping to connect academia with medtech, biotech, and pharma industries.
Upcoming Webinar
Wearables Are Getting Smarter: Deploying AI on Microcontrollers
Date and time: Tuesday 7 July 2026, 1:00–2:00 pm
Speaker: Luke Taylor, Founder of DeepGate.ai
Chair: Dr Garreth Prendergast, Lecturer in Audiology and Hearing Sciences, University of Manchester
The July session, “Wearables Are Getting Smarter: Deploying AI on Microcontrollers”, will be delivered by Luke Taylor (Founder, DeepGate.ai), on Tuesday 7th July 2026, at 1pm. The webinar will explore how AI can be deployed on resource constrained microcontrollers, enabling intelligent functions on small, low power devices. Using audio as a running example, the session will unpack the technical challenges, trade offs, and practical solutions involved in bringing machine learning models onto embedded systems in real world applications.
This webinar is aimed at:
• Academic researchers (PIs, postdocs, and PhD students)
• Engineers and data scientists working on AI, wearables, or embedded systems
• Researchers interested in translational and industry‑facing AI technologies
• Anyone curious about deploying AI beyond the cloud and onto real‑world devices
About the speaker
Luke Taylor is the founder of DeepGate, a company building ultra-efficient machine learning tooling for microcontrollers and embedded systems. He completed his DPhil at the University of Oxford, where he conducted computational neuroscience research in the Auditory Neuroscience Group with a focus on spiking neural network models. Inspired by the energy efficiency of the brain, his work today focuses on making machine learning smaller, faster, and more energy-efficient for real-world edge devices.
About the Company
DeepGate builds efficient tooling for developing, optimizing, and deploying machine learning models on microcontrollers. The company focuses on reducing latency, memory usage, and power consumption for edge AI applications, while simplifying development workflows for developers. Alongside its tooling platform, DeepGate develops licensable embedded AI models including object detection, wake-word, and audio enhancement systems.
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