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Human Machine Intelligence Research Group

Welcome to our research group page. We are supported by a variety of UKRI, H2020, and Defence & Securities funding, totalling over £19m. Full list of grants is here. Access to our and wider university labs relevant to our group is here.















The team is diverse and consists of 20+ members

from many countries:

  • Lecturer (2 Dedicated):
    Yang Xing - AI in autonomous systems

    Adolfo Perrusquia (RAEng IC Fellow 2021-23) - reinforcement learning

  • Research Fellows/Assistants (6 Dedicated, 8 in 6G Hub): 
    Zhuangkun Wei (EPSRC UKRI TAS-S: RS2C 2021-24) - secure communications in autonomy

    Deepak Panda (RAEng IC Fellow 2023-25) - autonomy and AI
    Miguel Arana Catania (Group/Centre dedicated 2022-25) - verification & causal inference in AI
    Paraskevas (Paris) Chatzithanos (Leonardo CDAS 2023-25) - trustworthy learning for autonomous navigation
    Mariusz Winsinewski (Leonardo CDAS 2023-25) - XAI for sensor fusion 
    [Advert] (EPSRC TAS-S and EPSRC CHEDDAR 2023-26) - distributed autonomy across heterogeneous continuum 

  • Research Fellows/Assistants (6G Hub):
    [Adverts x4] (EPSRC CHEDDAR DSIT 2024-26) - various topics in AI/ML in telecommunications

  • Ph.D. Students (9 Primary Supervisor): 
    Mengbang Zou (2020-24) - resilience of dynamic networked systems
    Almina Jin (2020-24, US AFOSR) - social network influence analysis
    Danny Holt (2021-25, EPSRC DTP with Saab) - XAI based radar signal classification for UTM
    Jinsheng Yuan (2021-24) - adversarial machine learning in transport
    Robert Brown (2022-26, EPSRC DTP with Saab) - Drone intention prediction using a non-cooperative radar system

    Dimitris 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 
    Minqing Qiu (2024-27) - human-centric mapping in remote areas


  • Ph.D. Students (10 Co-/External-Supervisor): 
    Andra Sonea (2018-25 PT, EPSRC CDT in Urban Science) [Warwick] - urban digital financial service access
    Peter Strong (2020-23, EPSRC CDT in Complexity) [Warwick] - 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
    Tabitha Watson (2021-25, Exeter University) - Climate induced migration modeling
    Patrick Geragersian (2021-24, EPSRC DTP with Spirent) - AI based PNT solution for autonomous system

    Jiaming Pan (2023-24, Peking University) - Complexity science in transport networks 
    Sorin Negru (2023-26) - Hybrid PNT navigation
    Jamal Mazar (2023-26) - Space wireless networks
    Cillian Wang (2023-26) - Compressive sensing and control in aircraft wings



Research Directions


Details of these areas and their researchers are given below.

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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.


  • Bell Labs 2014 - Finalist

  • IEEE Best Paper 2014

  • IET Innovation Award 2015 - Winner

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.


  • Bell Labs 2019 - Semi-Finalist

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

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).


  • Bell Labs 2016 - Semi-Finalist

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

BBC Interviews

Nature: commentary

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