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Professor Weisi Guo 
Chair of Human Machine Intelligence

 Director of Smart Living Grand Challenge
Head of Applied AI Group
Visiting Fellow at the Alan Turing Institute

 

​Cambridge: MEng, MA, PhD 

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Address: 
Office: F411 (1st floor iMech, B83)
Lab: F104 (1st floor DARTeC B105)

Centre for Autonomous and Cyber-Physical Systems, SATM, 
Cranfield University, 

MK43 0AL, BEDS, UK

Tel: 01234 758304

 

Webpages: Cranfield, Turing

Google Scholar, UKRI
 

Email: weisi.guo@cranfield.ac.uk

wguo@turing.ac.uk

Vision & Expertise - As human society is becoming increasingly inter-connected, human and machine intelligence is more closely interfaced than ever. Our networked society faces serious challenges from the human and natural world. My expertise is in connected intelligence systems, comprised of: machine learning, communication networks, and engineering. I work in socio-cyber-physical ecosystems, with a particular interest in how AI and informatics can be designed for specific challenging environments or tasks, whilst integrating human domain knowledge. The application areas include:
 

  • Cyber - Autonomy and Networking in Extreme Environments:
    Domains: Trustworthy Autonomy, Social Data-Driven 5G & 6G, Secure Communication, Biological Communication
    Methods: Physical Layer Security, Federated Learning, Explainable AI, Molecular Encoding 
    Key Papers: "Random Sketch Learning for Deep Neural Networks in Edge Computing," Nature Computational Science, 2021 [Link]
    "On the Accuracy and Efficiency of Sensing and Localization for Robotics," IEEE Trans. Mobile Computing, 2020 [Link]
    "Explainable Artificial Intelligence for 6G: Improving Trust between Human and Machine," IEEE Comm. Mag., 2020 [Link]
    Key Grants: [UKRI-EPSRC] TAS-S Trustworthy Autonomous Systems-Security (EP/V026682/1, 2020-24, Co-PI)
    [H2020] DAWN4IoE - Data Aware Wireless Network for Internet-of-Everything (778305, 2017-21, PI & Coordinator)
    [H2020] Mo-IoNT - Molecular Internet-of-Nano-Things (792799, 2018-20, PI)
    [H2020] GreenML5G - Green Machine Learning for 5G & Beyond Resource 
    Optimisation (891221, 2020-22, PI)
    [InnovateUK] Future Flight: Fly2Plan – Enabling a new model aviation data system-of-systems (70883, 2020-22, CI)
    [InnovateUK] COCKPIT 5G - Crowd Blackspot Intelligence for 5G Rollout (29634, 2019-20, PI)

     

  • Physical - Sensing and Digital Twin of Critical Systems:
    Domains: Large-scale Networked Infrastructure, Networked Natural Ecosystems
    Methods: Sensing/Compression: Graph Signal Processing, Network Science,

    Key Papers: "Sampling and Inference of Networked Dynamics using Log-Koopman Nonlinear Graph Fourier Transform," IEEE Transactions on Signal Processing, 2020 [Link]
    "Network Community Structure of Substorms using SuperMAG Magnetometers," Nature Communications, 2021 [Link]

    Key Grants: [EPSRC] CoTRE - Complexity Twin for Resilient Ecosystems (EP/R041725/1, 2018-20, PI)
    [Turing LRF] CHANCE - Coupled Human and Natural Critical Ecosystems (2017-21, PI)
     

  • Social - Human Response to a Changing Environment:
    Domains: Social Networks, Conflict, Climate Induced Migration & Conflict
    Methods: Network Science, Inference, Natural Language Processing, Complexity, Social-Physics Models

    Key Papers: "Retool AI to Forecast and Limit Wars," Nature, 2018 [Link]
    "Gang Confrontation: The case of Medellin (Colombia)," PLOS ONE, vol.14(12), Dec 2019 [Link]
    "Common Statistical Patterns in Urban Terrorism," Royal Society Open Science, vol.6(9), Sep 2019 [Link]
    "Google Trends can Improve Surveillance of Type 2 Diabetes," Nature Scientific Reports, vol. 7(1), Jun 2017 [Link]

    Key Grants: [EPSRC] Mobility as a service: MAnaging Cybersecurity Risks across Consumers, Organisations and Sectors (EP/V039164/1, 2021-23, CI)
    [EPSRC] Centre for Doctoral Training in Urban Science and Progress (EP/L016400/1, 2014-22, CI)
    [EPSRC] Patterns of City Formation & Development (EP/N509796/1, 2018-22, PI)
    [USAF] Networked Social Influence and Acceptance in a New Age of Crises (FA8655-20-1-7031, 2020-23, PI)
    [Turing D&S] GUARD - Global Urban Analytics for Resilient Defence (2017-19, extension: 2019-21, PI)

Track-record - I obtained MEng, MA, and PhD degrees from the University of Cambridge. From 2012 to 2019, I built an award-winning team at the University of Warwick. Since 2019, I joined Cranfield University (Mech/Aero Engineering: QS world 45, ARWU world 33) at the Centre for Autonomous and Cyber-Physical Systems [Link]. Here, I lead the Human Machine Intelligence Research Group, where I work closely with the new £65m Digital Aviation Research and Technology Centre (DARTeC). 

Excellence - I have been PI on over £5.5m and an investigator on over £16.6m of research funding. ​I was a Turing Fellow at the Alan Turing Institute from 2017 and remain a researcher there. I have published over 100 journal papers (total IF 500+) and 60+ IEEE/ACM conference papers, with 4200+ citations (h-index 31). Key papers published include: a Nature commentary, Nature Comm., Nature Comp. Science, a top 10% cited PLOS One paper, and several cover issues in Royal Society and IEEE journals. I am also an IET Innovation Award winner 2015 and been a runner-up in the Bell Labs Prize three times. I currently serve as editor on 3 IEEE & 1 Royal Society journals and reviewer for EPSRC (full college), ESRC, MRC, UKRI FF, NSF, NSERC, H2020, Royal Society, Leverhulme, and other international grant awarding bodies.

Life - I have worked in an UNHCR refugee camp in Africa, been part of the victorious Cambridge Varsity archery team, solo climbed two of the highest sub-continent peaks (Toubkal-Atlas Mt, Jade Mt), served as badminton captains at both Cambridge (Fitz) and Sheffield Universities, and completed both the London (2007) and full Sahara (2010) full marathons.

Key Papers
Autonomy & AI
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"Random Sketch Learning for Deep Neural Networks in Edge Computing,"
B. Li, P. Chen, H. Liu, W. Guo, X. Cao, J. Du, C. Zhao, J. Zhang,
Nature Computational Science, vol.1(3), 2021 [Link] [Commentary]

"Edge implementation of DNNs require extraordinary computational power. Current compression post-training techniques are not sufficient. Here we present the first compress-whilst-training tiny AI saving up to 10x energy and 180x computation power."  

"Retool AI to Forecast and Limit Wars,"
W. Guo, K. Gleditsch, A. Wilson,
Nature, vol. 562, 331-333, Oct 2018 [Link]

"Conflict causes long term damage to the socioeconomic fabric of civilization and hinders the UN Development Goals. Here we show AI without domain knowledge has limited applicability and cross-disciplinary research is needed to improve forecasting in rural and sparse data areas."   [Funded by Alan Turing Institute D&S Program]

"Scalable Partial Explainability in Neural Networks via Flexible Activation Functions,"
S. Sun, C. Li, Z. Wei, A. Tsourdos, W. Guo,
AAAI Conference on Artificial Intelligence, Feb 2021 [Link]

"Explaining the role of activation functions via flexible GPs with control points is useful for interpreting features without an initial bias function. Here we demonstrate a scalable understanding and compression of neural networks for detecting fake currency." 

"Trustworthy Deep Learning in 6G Enabled Mass Autonomy: from Concept to Quality-of-Trust Key Performance Indicators,"
C. Li, W. Guo, S. Sun, S. Al-Rubaye, A. Tsourdos,
IEEE Vehicular Technology Magazine, Sep 2020 [
Link]
"As AI is increasingly used in future network management, the Quality-of-Trust (QoT) is a new indicator of importance in many applications. Along side established QoS metrics and emerging QoE metrics, QoT will be used to determined if the AI algorithms used in connected autonomy  is trustworthy. Here, we set out some foundations for trust and its architectural implementation in large-scale networks."  [Funded by H2020 DAWN4IoE]

"Neural Network Approximation of Graph Fourier Transforms for Sparse Sampling of Networked Flow Dynamics,"
A. Pagani, Z. Wei, R. Silva, W. Guo,
ACM Transactions on Internet Technology, to appear, Jun 2021

"Compressing networked non-linear dynamical systems is crucial to sensing complex infrastructures. Here we train deep learning to both compress (using Graph Fourier Transform as a training basis) networked non-linear dynamics of a water distribution network, and then to perform sub-GFT inference with partial data. We show neural networks can achieve acceptable noisy inference with only a small set of sensors."  [Funded by EPSRC CoTRE and Alan Turing Institute DCE Program] 

"Network Community Structure of Substorms using SuperMAG Magnetometers,"
L. Orr, S. Chapman, J. Gjerloev, W. Guo,
Nature Communications, vol.12, 1842, 2021 [Link
"Geomagnetic storms can disrupt electronic and GNSS systems, but their formation is not well understood. Here we use network science to show that small sub-storms are in fact interconnected and form the early phase of larger geomagnetic storms."   [Funded by Alan Turing Institute & USAFOSR & EPSRC CDT in Complexity]

"Node-Level Resilience Loss in Dynamic Complex Networks,"
G. Moutsinas, W. Guo,
Nature Scientific Reports, vol.10, 2020 [Link]
"Complex engineering and ecological systems derive sophisticated capabilities by networking together simple components. Yet, the multi-scale resilience of networked non-linear systems is not well understood. Here, we show a sequential heterogeneous mean field approach that can rapidly converge on multi-scale resilience estimation as a function of both the component level non-linear dynamics and the graph properties of the network. In particular, we show how higher order effects can cascade along the network, ending in rupture at the end of a network, yet trace causes to the beginning of a health network."  [Funded by EPSRC CoTRE]

"Sampling and Inference of Networked Dynamics using Log-Koopman Nonlinear Graph Fourier Transform,"
Z. Wei, B. Li, SC. Sun, W. Guo,
IEEE Transactions on Signal Processing, Nov 2020 [Link

"Compressing networked non-linear dynamical systems is crucial to sensing complex infrastructures. Here we combine GFTs with a novel log-Koopman operator to efficiently lineaepize large-scale connected dynamics and compress to a spatially invariant sensing solution."  [Funded by EPSRC CoTRE and Alan Turing Institute DCE Program] 

 

 

"Molecular Physical Layer for 6G in Wave-Denied Environments," 
W. Guo, M. Abbaszadeh, L. Lin, J .Charmet, P. Thomas, Z. Wei, B. Li, C. Zhao,
IEEE Communications Magazine, vol.59(5), May 2021 [Link]

"Review of 8 years of funded research in molecular communications from theory to experimentation for future 6G bio-air-interface. Our work examines the information theory of fluid dynamic channels for a wide range of micro and macro scales. We use tracer molecules ignited by PLIF and other laser techniques to image the propagation of information molecules in different fluid dynamic channels with diverse obstacles and understand the impact on information capacity."  [Funded by H2020 Mo-IoNT, DSTL MEDE, USAF MolCom, Royal Society-NSFC]

 

"Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine,"
W. Guo,
IEEE Communications Magazine, vol.58(6), 2020 [Link]
"Review of the importance of Explainable AI for future communication systems that are vital to connected autonomy and safety critical areas. Our work examines both physical layer and MAC layer protocols and intelligence. Examples include explaining neural network power allocation in multiple access systems using meta-symbolic mapping."  [Funded by H2020 DAWN4IoE]

"Probabilistic Stability of Traffic Load Balancing on Wireless Complex Networks,"
G. Moutsinas, W. Guo,
IEEE Systems Journal, 2019 [Link]

"Seminal work to prove that load balancing stability in any network is independent of the dynamics and topology, but dependent on the accuracy of the load information. Here we use a wireless network as an example, but can be extended to other systems, and provided the self-dynamics of stations and balancing dynamics between stations are reasonable load balancing ODEs of any form, we show it is always stable for any graph topology using stochastic geometry. We further show through Gershgorin circles that the eigenvalues are bounded in stable regime only if the data accuracy that informs the load balancing has a variance that is smaller than the variance of noise."  [Funded by EPSRC CoTRE]

Complexity & Graph Analysis
Radio & Molecular Communications
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