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Antonia Chalka
Bioinformatician & Machine Learning Engineer at the Roslin Institute
Machine Learning (ML) Engineer and Bioinformatician with 6 years of experience in predictive modelling, analysis of high-throughput data, and bacterial genomics. Skilled in Python, PyTorch, scikit-learn, and cloud/HPC workflows. Proven track record of deploying scalable ML solutions to solve biological challenges. Seeking to leverage academic expertise and technical skills to contribute to innovative machine learning solutions in the industry as an ML Engineer.
Edinburgh, Scotland, Great Britain Orcid
Email: [email protected]
Tel: +44 7783867600
Skills
Programming & ML
Python (scikit-learn, PyTorch, Pandas, NumPy, Jupyter), R, Bash, Git, SQL, ML Development & Deployment
Tools & Platforms
Docker/Singularity, Nextflow, HPC, CLIMB, Google Cloud Platform, Server Management
Bioinformatics
Genomics, Bacteriology, Long & Short Read Sequencing
Languages
English (Fluent), Greek (Native), French (Beginner)
Work Experience
Postdoctoral Researcher • University of Edinburgh
ML Development: Designed Python (scikit-learn, PyTorch) models for bacteria-phage interaction prediction and E. coli host assignment which achieved accuracies of over 90% and revealing insights into AMR and phylogeny.
Pipeline Engineering: Automated genomic data processing via containerized pipelines, streamlining model training/testing and outputting a comprehensive isolate report from raw data in 2 hours.
Tool Deployment: Built an internal Flask web app for sequence analysis and ML predictions.
Scalability: Leveraged HPC/cloud platforms (Google Cloud, CLIMB) to process over 8TB of genomic data, enabling large-scale ML model training, validation and testing.
Communication: Presented at international conferences & led ML workshops bridging research and industry. Authored technical reports for Food Standards Scotland, informing on Scottish E. coli epidemiology and attribution.
Collaboration: Collaborated with 10+ government & research groups across Britain to create a novel E. coli dataset of 4000+ isolates.
April 2023–Present
Doctoral Researcher • The University of Edinburgh
Pathogen Prediction: Engineered ML models to trace S. enterica origins, achieving 95%+ accuracy and outperforming traditional epidemiological and genomic methods by 20%.
Framework Design: Benchmarked ML strategies on diverse biological data types (algorithm selection, feature engineering, etc) to identify optimal predictive strategies.
Generalizable Solutions: Created a reusable host-prediction framework & pipeline, later adapted to E. coli/phage projects.
October 2019–December 2023
Education
2019 - 2023
The University of Edinburgh
PhD: Using machine learning to examine S. enterica host specificity.
2018 - 2019
University of Glasgow
MSc: Bioinformatics, Polyomics and Systems Biology
Antonia Chalka
Bioinformatician & Machine Learning Engineer at the Roslin Institute
Machine Learning (ML) Engineer and Bioinformatician with 6 years of experience in predictive modelling, analysis of high-throughput data, and bacterial genomics. Skilled in Python, PyTorch, scikit-learn, and cloud/HPC workflows. Proven track record of deploying scalable ML solutions to solve biological challenges. Seeking to leverage academic expertise and technical skills to contribute to innovative machine learning solutions in the industry as an ML Engineer.
Edinburgh, Scotland, Great Britain Orcid
Email: [email protected]
Tel: +44 7783867600
Skills
Programming & ML
Python (scikit-learn, PyTorch, Pandas, NumPy, Jupyter), R, Bash, Git, SQL, ML Development & Deployment
Tools & Platforms
Docker/Singularity, Nextflow, HPC, CLIMB, Google Cloud Platform, Server Management
Bioinformatics
Genomics, Bacteriology, Long & Short Read Sequencing
Languages
English (Fluent), Greek (Native), French (Beginner)
Work Experience
Postdoctoral Researcher • University of Edinburgh
ML Development: Designed Python (scikit-learn, PyTorch) models for bacteria-phage interaction prediction and E. coli host assignment which achieved accuracies of over 90% and revealing insights into AMR and phylogeny.
Pipeline Engineering: Automated genomic data processing via containerized pipelines, streamlining model training/testing and outputting a comprehensive isolate report from raw data in 2 hours.
Tool Deployment: Built an internal Flask web app for sequence analysis and ML predictions.
Scalability: Leveraged HPC/cloud platforms (Google Cloud, CLIMB) to process over 8TB of genomic data, enabling large-scale ML model training, validation and testing.
Communication: Presented at international conferences & led ML workshops bridging research and industry. Authored technical reports for Food Standards Scotland, informing on Scottish E. coli epidemiology and attribution.
Collaboration: Collaborated with 10+ government & research groups across Britain to create a novel E. coli dataset of 4000+ isolates.
April 2023–Present
Doctoral Researcher • The University of Edinburgh
Pathogen Prediction: Engineered ML models to trace S. enterica origins, achieving 95%+ accuracy and outperforming traditional epidemiological and genomic methods by 20%.
Framework Design: Benchmarked ML strategies on diverse biological data types (algorithm selection, feature engineering, etc) to identify optimal predictive strategies.
Generalizable Solutions: Created a reusable host-prediction framework & pipeline, later adapted to E. coli/phage projects.
October 2019–December 2023
Education
2019 - 2023
The University of Edinburgh
PhD: Using machine learning to examine S. enterica host specificity.
2018 - 2019