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Appointment
- Research Fellow
- London Centre for Nanotechnology
- Faculty of Maths & Physical Sciences
Biography
I received an MEng in nuclear engineering at the University of Birmingham in 2018, during which I worked as an intern at Hitachi GE Nuclear Division in Hitachi, Japan. My Masters’ dissertation involved the investigation of emergent magnetic monopoles in EuB6.Following this, I undertook my PhD at Imperial College under the supervision of Will Branford. I made a number of notable discoveries including the world-first demonstration of neuromorphic computing in large nanomagnetic arrays, development of physical neural networks using nanomagnetic arrays as well as a method of controlling nanomagnetic states with low-power (mW) lasers – 100 X less power than the leading technology.
I am now a doctoral prize fellow at UCL where I am developing my neuromorphic computing technology towards device integration. In September 2023, I will start a 2-year fellowship at the new Imperial-X AI in Science initiative where I will be developing trainable magnetic neural networks, integrating my computing and laser writing technologies.
I have presented my work at a number of conferences including an invited talks at MEMRISYS 2022 and OASIS online seminars as well as contributed talks at MMM (2022, 2021), IOP Magnetism (2022, 2021), CHRIONSpin-wave computing workshop (2022).
Research Summary
My research involves controlling and engineering magnetic states for computing technologies with a particular emphasis on low-power data storage and neuromorphic computation.
Active areas: Nanoscale magnetism with a focus on strongly-interacting magnetic nanoarrays, spin waves, neuromorphic computation and data storage. Reconfigurable magnonics (GHz dynamics of microstate-writeable nanomagnetic arrays). Novel nanomagnetic writing methodologies – all-optical, surface probe and nanofabricated.
Recent research focus: Nanopatterned magnetic arrays hold huge promise as both model systems in which to observe exotic physics and as candidates for next-generation technologies such as data storage, neuromorphic computation and reconfigurable magnonics. However, work has so-far been impeded by a lack of means to control the magnetic states of arrays. To address this pressing need, I have developed and refined a range of systems for state control; a cutting-edge all-optical switching approach (under review, Arxiv 2021) and a surface probe approach (ACS Nano 2020) requiring no global magnetic field. I have also developed a technique for accessing global high-energy states via external field (Nature Nanotechnology, 2022, Nat. Comms. 2021) I am particularly interested in the controlling high-frequency dynamics via state control and harnessing this for computation. My research efforts have explored these effects across a range of nanomagnetic systems outlining reconfigurable magnonic transistors & filters (Comms. Phys., 2020), phase-shifters & logic gates (ACS Nano 2020) and reconfigurable magnon mode hybridisation in a nanopatterned array via microstate control (Nat. Comms, 2021). I have also implemented the first experimental demonstration of neuromorphic computation in strongly-interacting nanomagnetic arrays (Nature Nanotechnology,2022). Ongoing work is focussed on designing and implementing neuromorphic computational schemes, from non-trainable ‘reservoir computing’ for time-series forecasting to fully trainable deep neural networks for image recognition, as well as investigating and optimising all-optical magnetic switching for low-power data storage.
Academic Background
| MEng (Hons | Nuclear Engineering | University of Birmingham | |
| Imperial College London |
