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Human Machine Intelligence Research Group

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Welcome to our research group page. We are supported by a variety of UKRI, H2020, and Defence & Securities funding over the years, totalling over £4.8m in PI and £16.3m in CI.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The team is diverse and consists of 25+ members from 8 (13 historical) countries:
 

  • Lecturer (1 Dedicated):
    Yang Xing - AI in autonomous systems
     

  • University 75th Anniversary Fellow (1):
    Alexander Elliott - digital twin of dynamical systems
     

  • Research Fellows (8 Dedicated): 
    Adolfo Perrusquia (Group/Centre dedicated 2021-, RAEng IC Fellow 2021-23) - reinforcement learning
    Zhuangkun Wei (EPSRC UKRI TAS-S: RS2C 2021-24) - secure communications in autonomy
    Liang Wang (EPSRC PETRAS GraphSec 2021-23) - IoT security
    Kai Fung Chu (EPSRC MACRO 2022-23) - adversarial AI 
    Deepak Panda (Group/Centre dedicated 2022-25) - autonomy and AI
    Miguel Arana Catania (Group/Centre dedicated 2022-25) - verification & causal inference in AI
    2x New Positions in Autonomy: (1) Trust, and (2) Alternative Navigation [Link] - closes 27th Nov
     

  • Ph.D. Students (10 Primary Supervisor): 
    Chen Li (2019-22) - green and trustworthy AI for networked drones [Graduating]
    Schyler Sun (2019-22) - explainable AI for causal prediction [Graduating]
    Mengbang Zou (2020-23) - resilience of dynamic networked systems
    Almina Jin (2020-23, US AFOSR) - social network influence analysis
    Danny Holt (2021-24, EPSRC DTP with Saab) - XAI based radar signal classification for UTM
    Jinsheng Yuan (2021-24) - adversarial machine learning in transport
    Robert Brown (2022-25, EPSRC DTP with Saab) - Drone intention prediction using a non-cooperative radar system
    Dimitrios Panagopoulos (2023-26, EPSRC iCASE with Thales) - human machine teaming for search & rescue
    Yifan Yang (2023-26) - civil aviation safety for air mobility on demand
    Lakhepatil Ajinkya (2023-26, EPSRC TAS-S: RS2/3) - human adaptation informed secure networked learning
     

  • Ph.D. Students (6 Co-/External-Supervisor): 
    M. Mazzamurro (2018-22, EPSRC DTP) - scaling laws in urban science [Graduating]
    Andra Sonea (2018-25 PT, EPSRC CDT in Urban Science) - urban digital financial service access
    Peter Strong (2020-23, EPSRC CDT in Complexity) - migration modelling for refugees
    Jon Ricketts (2020-27 PT, QinetiQ) - NLP for aviation safety
    Janice Liu (2020-23) - AI prediction of digestate of anaerobic digestion
    Peter Geragersian (2021-24, EPSRC DTP with Spirent) - AI based PNT solution for autonomous systems
     

  • Visiting PhD and MRes Students (2 Co-Supervisor): 
    Jiaming Pan (2023-24, Peking University) - complexity science in transport networks
    James Shepherd (2022-23) - Deep learning for EV charging in renewable grids

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Research Directions

 

Details of these areas and their researchers are given below.

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Overview
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Cyber - Autonomy and Networking in Extreme Environments

Design a new generation of machine learning (federated, trustworthy, tiny) and networks (radio, molecular, DNA) for extreme environments: ultra-light, energy efficient, and nanoscale. Paving the way for new 6G Internet of NanoThings and swarm UAVs connectivity, with strong application in military, healthcare, and industry.

Awards:

  • Bell Labs 2014 - Finalist

  • IEEE Best Paper 2014

  • IET Innovation Award 2015 - Winner

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Key Papers:

  • "Random Sketch Learning for Deep Neural Networks in Edge Computing," Nature Computational Science, 2021

  • "Tabletop Molecular Communication: Text Messages Through Chemical Signals" PLOS ONE, 2013 

  • "On the Accuracy and Efficiency of Sensing and Localization for Robotics,"
    IEEE Trans. on Mobile Computing, 2020

 

Nature Computational Science

IEEE Transactions on NanoBioScience: cover

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Physical - Sensing and Digital Twin of Critical Systems

Analysing and sensing the resilience of networked infrastructure as a function of local dynamics and global network topology. Minimise data collection for future Digital Twins using graph signal processing and machine learning. Inform predictive maintenance and long-term design of engineering and engineered systems.

Awards:

  • Bell Labs 2019 - Semi-Finalist

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Key Papers:​

  • "Sampling and Inference of Networked Dynamics using Log-Koopman Nonlinear Graph Fourier Transform,"
    IEEE Trans. on Signal Processing, 2020

  • "Network community structure of substorms using SuperMAG magnetometers," Nature Communications, vol.12, 2021

  • "Node-Level Resilience Loss in Dynamic Complex Networks," Nature Scientific Reports, 2020

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Royal Society Open Science - Cover Issue​

Nature Communications

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Social - Human Response to a Changing Environment 

Develop generative models and AI solutions to predict emerging events and conflict, both in the physical and cyber world. Use models to inform political science development (causal discovery, peace negotiation), and stakeholders (government, NGOs, peacekeeping, and sustainable development).

Awards:

  • Bell Labs 2016 - Semi-Finalist

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Key Papers:

  • "Retool AI to Forecast and Limit Wars," Nature, 2018

  • "Common Statistical Patterns in Urban Terrorism," Royal Society OS, 2019 

  • "Simulating Imperial Dynamics and Conflict in the Ancient World," Cliodynamics, 2019

  • "Gang Confrontation: The case of Medellin (Colombia)," PLOS ONE, 2019

  • "Revealing the Excitation Causality between Climate and Political Violence via a Neural Forward-Intensity Poisson Process," IJCAI, 2022

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BBC Interviews

Nature: commentary

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