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Currently work as Applied Scientist at SpiNNcloud. Previously Postdoc at King's College London. Ph.D. from University of Manchester. Research interests include neuromorphic computing, spiking neural networks, analog in-memory computing, and large language models.

Last updated: April, 2026

General Information

Full Name Chen Li
Location London, UK
Email chenliatoz@gmail.com
LinkedIn linkedin.com/in/chen-li-727698157
Twitter @ArtwistLi
Google Scholar scholar.google.com

Education

  • 2018 - 2022
    Ph.D. in Computer Science
    University of Manchester, United Kingdom
    • Supervisor: Prof. Steve Furber
  • 2015 - 2018
    MSc in Spintronics and Quantum Optics
    Shanxi University, China
  • 2011 - 2015
    BSc in Applied Physics
    Nanchang University, China

Experience

  • 2025 - now
    Applied Scientist
    Algorithm Team, SpiNNcloud, Germany
  • 2022 - 2024
    Research Associate (Postdoc)
    King's Laboratory for Intelligent Computing (PI: Prof. Bipin Rajendran), King's College London, UK

Publications

  • Boybat I, Boesch T, Allegra M, et al. Heterogeneous Embedded Neural Processing Units Utilizing PCM-based Analog In-Memory Computing. IEEE International Electron Devices Meeting (Invited paper in IEDM).
    Built an analog chip to accelerate MobileBERT inference compared to running on GPUs.
  • Li C, Jones E, Furber S. Unleashing the Potential of Spiking Neural Networks by Dynamic Confidence. International Conference on Computer Vision 2023 (ICCV).
    Proposed a Neural Network decoding method based on temporal confidence.
  • Li C, Ferro E, Lammie C, et al. Efficient Transformer Adaptation for Analog In-Memory Computing via Low-Rank Adapters. Neuromorphic Computing and Engineering.
    Llama 3 8B adaptations on analog hardware via Reinforcement Learning and long Chain-of-Thoughts.
  • Nimbekar A, Katti P, Li C, et al. Hardware-Software Co-optimized Inference Accelerator for Deep Spiking Networks. IEEE International System-on-Chip Conference 2024 (SOCC).
    Proposing a hardware accelerator for Spiking Neural Networks.
  • Katti P, Nimbekar A, Li C, et al. Bayesian Inference Accelerator for Spiking Neural Networks. IEEE International Symposium on Circuits and Systems 2024 (ISCAS).
    Developed a Bayesian accelerator to enhance the performance of Bayesian Spiking Neural Networks.
  • Li C, Rajendran B. Noise Adaptor in Spiking Neural Network.
    Propose a plug-in-and-play module to improve both accuracy and scalability of Spiking Neural Networks.
  • Li C, Furber S, et al. Quantization Framework for Fast Spiking Neural Networks. Frontiers in Neuroscience, 2022, 16: 918793.
    The state-of-the-art method to build ultra-low-latency Spiking Neural Networks on ImageNet.
  • Li C, Furber S. Towards Biologically-Plausible Neuron Models and Firing Rates in High-Performance Deep Spiking Neural Networks. International Conference on Neuromorphic Systems 2021.
    Proposed a method to smooth spiking neurons by noise injections to achieve state-of-the-art accuracy.
  • Chen R, Li C, Li Y, et al. Nanoscale Room-Temperature Multilayer Skyrmionic Synapse for Deep Spiking Neural Networks. Physical Review Applied, 2020, 14(1): 014096.
    Proposed an Analog-in-memory computing (AIMC) architecture to accelerate Spiking Neural Networks.
  • Li C, Chen R, Moutafis C, et al. Robustness to Noisy Synaptic Weights in Spiking Neural Networks. International Joint Conference on Neural Networks 2020 (IJCNN).
    Demonstrated how noise in synapses affects neural network (both spiking and non-spiking) performance.

Professional Engagement

  • Conference Reviewer
    • AAAI2022, ICML2022, ICML2024, EDTM2024, NIPs2024 NeuroAI Workshop, ICLR2025 (Notable Reviewer), ISCAS2023, NIPs2025 Dataset and Benchmark Track, ICLR2026
  • Journal Reviewer
    • Neural Networks, Neurocomputing, TNNLS, TETCI, Transactions on Consumer Electronics, Frontiers in Neuroscience, Frontiers in Computational Neuroscience, Frontiers in Artificial Intelligence, Frontiers in Oncology
  • Editor
    • Frontiers in Computational Neuroscience (Guest Editor)
  • Mentorship
    • Postgrad Research Mentor, Department of Computer Science, University of Manchester
    • King's Undergraduate Research Mentor, Department of Engineering, King's College London

Presentations

  • 2025
    Workshop on Neuromorphic Computing, Egham — Invited Presentation
  • 2023
    Spiking Neural Networks as Universal Function Approximators — Poster Presentation
  • 2023
    International Conference on Computer Vision — Poster Presentation
  • 2022
    Neuromorphic Algorithms — Invited Poster Presentation
  • 2021
    International Joint Conference on Neural Networks — Presentation
  • 2021
    International Conference on Neuromorphic Systems — Presentation