CV
Georgeos Hardo
Summary
Freshly graduated PhD from University of Cambridge, and incoming assistant professor at UAE University in August 2025!
Education
- PhD in Systems/quantitative microbiology2025Control Group, Department of Engineering, University of Cambridge
- MPhil in Biotechnology2019Department of Chemical Engineering and Biotechnology, University of Cambridge
- B.Eng in Chemical Engineering2018Department of Chemical and Biological Engineering, University of Sheffield
Work Experience
- Researcher in Systems Biology and Single-Cell MicrobiologyJanuary 2020 - Present (PhD completed in April 2025)Department of Engineering - Cambridge University
- I used smart microscopy combined with advanced deep-learning based bioimage analysis to do ultra-high-throughput single-cell imaging of bacteria in microfluidic devices.
- I use these techniques to investigate heterogeneity in bacteria at the single-cell level, with applications pertaining to antibiotic resistance, persistence, and phage therapy, but also to enhance the throughput and efficacy of genetic engineering and synthetic biology techniques.
- Built deep learning based systems that can image over ~10e6 cell lineages across time, building datasets of millions division events in a single experiment.
- I built and maintain custom software packages that use machine-learning based methods to handle, analyse, and track cells in these huge datasets
- PhD Industrial Research PlacementJanuary 2022 - April 2022Nemesis Bioscience - Cambridge, UK
- I took a 4 month break during my PhD to work in a phage therapy startup.
- Developed deep learning based models to classify phage proteins.
- Used bioinformatic techniques to optimise the design of custom-built phages.
- As of 2024, continued collaboration to bring Nemesis’ technology to help tackle the antibiotic resistance crisis.
- Graduate ResearcherJune 2019 - September 2019AstraZeneca/MedImmune - Cambridge, UK
- Engineered 5’ and 3’ mRNA UTRs to enhance in vivo stability and protein expression, using deep learning to predict and optimise mRNA expression/stability from UTR sequences.
- Developed generative AI models to design high-expression 5’ UTRs, identifying key regulatory motifs, and validating through successful in vivo transfection in human cell lines.
- ResearcherSeptember 2018 - June 2019Dept. of Chemical Engineering and Biotechnology, Sustaintable Reaction Engineering Group - University of Cambridge
- Developed graph-theory based tools and algorithms to model and search the entire organic chemistry literature as a network.
- Created an all-in-one toolkit for efficient selection of reaction synthesis pathways, optimizing for yield, conditions, feasibility, and other parameters..
Publications
- Electrochemical coupling of porin permeability and metabolism controls antibiotic resistance in bacteria2022bioRxiv - currently in second review in Nature MicroJoined this work as an author in 2024-25. This paper showes that the permeability of bacterial porin is dynamically regulated by periplasmic pH and potassium levels, altering antibiotic resistance.
- Single-cell imaging of the lytic phage life cycle in bacteria2024bioRxiv - currently in review in Cell SystemsSingle-cell imaging reveals heterogeneous kinetics and physiological coupling in bacteriophage T7 infection dynamics.
- Quantitative microbiology with widefield microscopy: navigating optical artefacts for accurate interpretations2024npj ImagingAddresses systematic errors in quantitative widefield microscopy of microbes (size, intensity, single-molecule counting) due to projection and diffraction, proposing synthetic data and machine learning for accurate interpretation.
- Synthetic Micrographs of Bacteria (SyMBac) allows accurate segmentation of bacterial cells using deep neural networks2022BMC BiologyDeveloped SyMBac, a novel method for accurate bacterial cell segmentation using deep neural networks trained with synthetically generated micrographs, outperforming human-annotated data.
- Challenges of analysing stochastic gene expression in bacteria using single-cell time-lapse experiments2021Essays in BiochemistryWe outline a rigorous framework for single-cell time-lapse experiments to analyze stochastic gene expression in bacteria.
Presentations
- Optical illusions in quantitative microbiology with widefield microscopy2024Warwick-Cambridge Quantitative Cell Biology SymposiumWarwick, UK
- Optical illusions and artefacts in the use of widefield microscopy for microbiology2023Physics of Living Matter Early Career Researcher ConferenceCambridge, UK
- Quantitative Microbiology with Microscopy: Effects of Projection and Diffraction2023Physics of Life ConferenceHarrogate, UK
- Accurate Segmentation of Bacterial Cells Using Synthetic Training Data Reveals Novel Size Regulation Behaviour2022ASM Microbe ConferenceWashington DC, USA
- SyMBac: Synthetic Training Data for Accurate and Precise Segmentation of Bacteria2021Physics of Living Matter ConferenceCambridge, UK
Teaching
- Cambridge University Synthetic Biology SocietyUniversity of Cambridge and iGEMRole: InstructorI mentor undergraduates and graduate members of the Cambridge University Synthetic Biology Society in the fields of synthetic biology and molecular biology year round, giving talks on mathematical modelling of gene expression systems, and analysis of bioimage data. I also mentor students during the iGEM (International Genetically Engineered Machine) competition each summer, taking the lead on the mathematical and computational side of the project.
- BioDesign - MPhil Computational BiologyDepartment of Applied Mathematics and Theoretical Physics - University of CambridgeRole: Supervisor and demonstratorTaught introductory mathematics for biologists, general biology for engineers and mathematicians, synthetic biology, genetic circuit design, modelling genetic circuits. Ran labs covering the theoretical (computational) design of COVID-19 mRNA vaccines, and the mathematics behind and implementation of modern DNA computing.
- Part III Systems BiologyCambridge Systems Biology Centre - University of CambridgeRole: SupervisorAn advanced and mathematics focussed 4th year undergraduate course for the top performers of the Cambridge Natural Sciences Tripos. Taught evolutionary modelling, stochastic modelling of gene expression, synthetic biology principles, bioinformatics, game theory.
- Mathematical Biology - Natural Sciences TriposCorpus Christi, Pembroke, and St Catherine's Colleges - University of CambridgeRole: SupervisorA year-long mathematics course for undergraduates, encompassing a quarter of their teaching time in the first year. Taught probability theory, linear algebra, probability theory, statistics, linear models, calculus, differential equations, biological systems modelling.
Interests
- Bioimage analysisDeep Learning, Segmentation, Tracking
- Systems BiologyAntibiotic persistence, Antibiotic resistance, Modelling biophysics of cell growth
- MicroscopyWidefield microscopy, Virtual microscopy, High throughput single-cell imaging
- MicrofluidicsThe mother machine, Single cell chemostats