Professor Antonio Hurtado

Institute of Photonics

Personal statement

I am a Reader and Turing Artificial Intelligence (AI) Fellow with the Institute of Photonics, University of Strathclyde, Glasgow (UK). I completed my Ph.D. at the Universidad Politécnica de Madrid (UPM), Spain. I have more than 15 years of international research experience in photonics, having worked at the Universities of Essex and Strathclyde (UK), the University of New Mexico (USA) and UPM (Spain). I was the recipient of two Marie Curie Fellowships awarded by the European Commission, and a Chancellor’s Fellowship from the University of Strathclyde, following which I was appointed as a Lecturer at Strathclyde’s Institute of Photonics in 2014. At Strathclyde, I have established and lead the Neuromorphic Photonics research group and have been Principal Investigator in programmes funded by UK (e.g. EPSRC), US (e.g. ONRG) and EU (e.g. H2020 Programme) agencies. Notably, in 2020 I was awarded a Turing AI Acceleration Fellowship by the UK Research and Innovation (UKRI) Office and the UK Govt. Business, Energy and Industrial Strategy Dept., to develop a 5-year Programme on ‘Photonics for Ultrafast AI’.

Publications

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Publications

Resonate-and-fire photonic-electronic spiking neurons for fast and efficient light-enabled neuromorphic processing systems: Adair Andrew, Owen-Newns Dafydd, Donati Giovanni, Robertson Joshua, Figueiredo José, Wasige Edward, Al-Taai Qusay, Romeira Bruno, Hejda Matěj, Hurtado Antonio; Communications Physics Vol 9 (2026); https://doi.org/10.1038/s42005-026-02694-5
Neuromorphic photonic processing and memory with spiking resonant tunnelling diode neurons and neural networks: Owen-Newns Dafydd, Robertson Joshua, Donati Giovanni, Al-Taai Qusay, Figueiredo José, Wasige Edward, Lüdge Kathy, Romeira Bruno, Hurtado Antonio; Advanced Intelligent Systems Vol 8 (2026); https://doi.org/10.1002/aisy.202500800
Neuron Surface Emitting Laser (NeuronSEL) : spiking regimes and negative differential resistance in solitary multi-junction VCSELs: Duque Gijon Maria, Robertson Joshua, Owen-Newns Dafydd, Baker Jack, Allford Craig P, Porte Xavier, Shutts Samuel, Smowton Peter M, Hurtado Antonio; (2026); https://doi.org/10.48550/arXiv.2604.12893
Polarization-sensitive nanowire terahertz receivers : from laboratory research to industry: Peng Kun, Morgan Nicholas P, Jevtics Dimitars, Wagner Ford M, Siday Thomas, Xia Chelsea Q, Dede Didem, Boureau Victor, Piazza Valerio, Guilhabert Benoit, Hurtado Antonio, Strain Michael J, Dawson Martin D, Fu Lan, Tan Hark H, Jagadish Chennupati, Morral Anna Fontcuberta i, Johnston Michael B; 2025 IEEE Photonics Conference (IPC) IEEE Photonics Conference 2025 2025 IEEE Photonics Conference (IPC), pp. 1-2 (2025); https://doi.org/10.1109/ipc65510.2025.11282030
Photonic Spiking Neural Network with Coupled Passive Silicon Microring Resonators: Donati G, Biasi S, Pavesi L, Hurtado A; 2025 IEEE Photonics Conference (IPC) IEEE Photonics Conference 2025 2025 IEEE Photonics Conference (IPC), pp. 1-2 (2025); https://doi.org/10.1109/ipc65510.2025.11282384
All-optical spiking processing and reservoir computing with a passive silicon microring and wavelength-time division multiplexing: Donati Giovanni, Biasi Stefano, Pavesi Lorenzo, Hurtado Antonio; Photonics Research Vol 13, pp. 2641-2653 (2025); https://doi.org/10.1364/PRJ.558405

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Research

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Research Interests

My research interests lie within Photonics for Neuromorphic Computing and Artificial Intelligence (AI). This is a rapidly emerging scientific field combining concepts from diverse disciplines (e.g. photonics, neuroscience, computer science) seeking to emulate the brain’s powerful computational capabilities for new paradigms in light-enabled ultrafast brain-inspired computing and AI. I have established the Neuromorphic Photonics group at Strathclyde, which deliveres fundamental and impact-inspired research in this emerging field, with a main focus on novel ultrafast artificial photonic spking neurons, photonic spiking neural networks and optical spike-based processing platforms towards high-speed, energy-efficient, light-enabled, brain-inspired computing architectures.