Dr Jeff Jones

Biography

Jeff Jones is a post-doctoral research associate at Liverpool School of Tropical Medicine (LSTM) in the Vector Biology department. He has a biological science and computer science background and holds an undergraduate degree in Biology with Computer Science.

Jeff completed a PhD in Computer Science that focused on modelling the behaviour of true slime mould physarum polycephalum for the purposes of non-classical (unconventional) spatially-implemented computation.

He worked as a Leverhulme Trust Fellow on collective amorphous robotics. This involved using emergent behaviour to provide locomotion and sensory abilities for collective amoeboid robots. He also worked as a Senior Research Fellow on the EU FP7 e Physarum Chip Project to hybridise slime mould with classical and novel electronics devices. His role involved designing physically inspired collective computational methods and novel non-neural approaches to low-level vision and primitive learning.

Jeff has authored approximately 50 papers, 6 book chapters and one monograph. His main special interest is in non-classical, or unconventional, computation and novel computational approaches to image analysis problems. This involves exploiting physical and living systems to perform useful and spatially applied computation. He is also interested in dynamical pattern formation, cellular regeneration and modelling low-level vision.

Jeff received a SPUR award to research non-neural analogues within biological computing, Implementing common behaviours seen in neural systems (lateral inhibition, illusory brightness perception phenomena) using collectives of systems containing no neurons..

Research interests

Jeff’s research interests at LSTM include modelling the indoor flight behaviour of mosquitoes and their interaction with human hosts, their attractant profiles, and insecticide treated bed nets. The aim of this research is to develop computational approaches which may enhance the speed of development of vector control tools.

 

He is involved in development of novel bioassays to assess mosquito behaviour, particularly with respect to the presence of a human host and the assessment of how behaviour is affected by insicticidal compounds.

Jeff is also interested in in assessing how vision affects the behaviour of aedes aegypti mosquitoes and its potential exploitation for novel vector control tools.

 

He has also developed multiple software packages which have been used in research at LSTM, including InVecTS (multi agent 3D flight modelling in an indoor environment with simulation of activity about a human baited bednet), ViCTA (computational augmentation to the WHO cone bioassay), ViRSA (analysis of mosquito behaviour on IRS coated surfaces), computer assisted egg counting and labelling software, interactive wing measurement software, interactive melanisation assessment software (MelanIE), motion estimation software to estimate bulk movement of mosquitoes in cages (FlowMo), compositor software to aggregate video recordings into experimental summary snapshots, and visualisation of estimation of resting time and location of mosquitoes.

His recent work explores novel ways of assessing mosquito responses to spatially distributed vapour-phase insecticides using machine learning methods.

Jeff has succeeded in attaining external funding from Innovative Vector Control Consortium to explore computationally assisted assessment of novel insecticide nets and their effect on mosquito behaviour.

Selected research publications

Multimodal platform for ITN efficacy: Surface chemistry, bioavailability, and mosquito behavior – Journal: Science advances – Published: 8th April 2026

Behavioural responses of Anopheles gambiae to standard pyrethroid and PBO-treated bednets of different operational ages – Journal: Current Research in Parasitology and Vector-Borne Diseases – Published: 8th November 2024

Insecticidal roof barriers mounted on untreated bed nets can be as effective against Anopheles gambiae as regular insecticide-treated bed nets – Journal: Scientific Reports – Published: 12th December 2023

Video augmentation of the WHO cone assay to quantify mosquito behavioural responses to insecticide-treated nets – Journal: Parasites and Vectors – Published: 15th November 2023

A minimal 3D model of mosquito flight behaviour around the human baited bed net – Journal: Malaria Journal – Published: 7th January 2021

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