KEY SKILLS
- Proven, production-grade AI application architecture design, including Python-based HTTP services such as Flask/Gunicorn and FastAPI/Uvicorn, plus event-driven systems such as Kafka and RabbitMQ for scalable, resilient deployment.
- Experience leading a small Agile R&D team, coordinating work through Jira- and GitHub-based workflows, planning development timelines, and reviewing technical work to maintain high standards.
- Deep understanding of AI models, both from first principles and in practice, with a focus on CNNs and LNNs.
- Infrastructure management using AWS and GCP, integrating LLM APIs from a variety of major providers, and using open-source tools such as Docker, Kubernetes, and TorchServe.
- Extensive Python experience, centred on deep learning frameworks and data science tools.
- Proficiency in SQL, SAS, and Fortran, as well as familiarity with C++, Java, HTML, R, and MATLAB.
EXPERIENCE
Head of Artificial Intelligence Research, Idenfo, London, England (Hybrid), July 2025 - Present
(previously Innovation Specialist - AI & Machine Learning, February 2023 - July 2025, and Innovation Associate - AI & Machine Learning, July 2022 - February 2023)
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Currently, I research and develop applications of AI for identity verification and due diligence checks, such as in facial comparison, anti-spoofing, forgery detection, and name screening. My primary responsibility is leading end-to-end development of new AI-enabled products and features across the organisation. I play a key role in each step of the process, from initial conception and architecture design through to management of data collection, model structure and training iterations, performance evaluation, and, finally, deployment to production.
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For example, I have personally developed a facial comparison model from scratch, which achieved 99.7% accuracy with a false-acceptance rate < 1 x 10-4% on a large, diverse, and representative dataset. Now integrated into Idenfo's IDV platform, this model is used to verify customer identities for a wide range of clients, including Pakistan's largest microfinance bank.
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I manage a small team, with two direct reports dedicated to R&D, and I also flexibly part-manage several other resources when a portion of their time is allocated to R&D projects.
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My team and I also work on improving name matching and public domain name screening systems through the integration of LLMs, text embeddings, entity extraction, sentiment analysis, and other NLP techniques. Over the past 12-18 months, I have guided the company through the advent of agentic LLM-powered systems and ensured that we were one of the first to market with a semi-automated agentic system for public domain adverse media and political exposure checks.
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I have also helped the company integrate AI-assisted workflows within the development team, evaluating solutions such as Cursor, Claude, and GitHub Copilot, engineering system prompts, and establishing strict governance rules for compliant use.
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In addition to my R&D responsibilities, I participate in advisory work through Idenfo. I have led several on-site system architecture and data-quality reviews, as well as anti-money-laundering and anti-fraud system-tuning projects, at various Dubai-based banks. This has involved analysing large financial datasets (>100M records), as well as developing transaction-monitoring rule-logic simulations and performance-visualisation tools. I have used above-the-line and below-the-line testing methods to fine-tune existing rules, and proposed system modifications after examining transaction distributions and typical customer behaviour. Initially, my role in these projects was limited to data analysis alone, but, as my experience and compliance knowledge have grown, it has expanded to include leading tuning exercises as a whole and aiding clients with the development of new rules and the training of junior staff.
Mathematics and Science Tutor, Freelance, Frome, England, 2019
Intern, Centre for the Analysis of Motion, Entertainment Research and Applications, Bath, England, 2017
- I processed data from optical motion capture systems.
Assistant Database Administrator, Kensington Domestic Appliances, Heathfield, England, 2015-16
- I managed the database supporting the online store.
PUBLICATIONS
Warsop, S.J.E.T., Caixeiro, S., Bischoff, M., Kursawe, J., Bruce, G.D., and Wijesinghe, P., Estimating full-field displacement in biological images using deep learning, npj Artif. Intell. 1, 6 (2025). https://doi.org/10.1038/s44387-025-00005-x:
The estimation of full-field displacement between biological image frames or in videos is important for quantitative analyses of motion, dynamics and biophysics. However, the often weak signals, poor biological contrast and many noise processes typical to microscopy make this a formidable challenge for many contemporary methods. Here, we present a deep-learning method, termed Displacement Estimation FOR Microscopy (DEFORM-Net), that outperforms traditional digital image correlation and optical flow methods, as well as recent learned approaches, offering simultaneous high accuracy, spatial sampling and speed. DEFORM-Net is experimentally unsupervised, relying on displacement simulation based on a random fractal Perlin-noise process and optimised training loss functions, without the need for experimental ground truth. We demonstrate its performance on real biological videos of beating neonatal mouse cardiomyocytes and pulsed contractions in Drosophila pupae, and in various microscopy modalities. We provide DEFORM-Net as open source, including inference in the ImageJ/FIJI platform, for rapid evaluation, which will empower new quantitative applications in biology and medicine.
EDUCATION
Master's in Physics (First-class, Integrated), University of St Andrews, Scotland, 2018-22:
- Completed a research-based Master's project, "Measuring biomechanics with deep learning", which became the basis for the publication, "Estimating full-field displacement in biological images using deep learning", above.
- I studied a wide range of topics across Physics, Astronomy, and Mathematics, including:
- quantum field theory,
- Monte Carlo simulation,
- magnetohydrodynamics,
- electronics, and
- computational astrophysics,
- signal processing.
- Named on the Dean's List (for averaging first-class results across all modules) in every academic year.
- Awarded the Astronomy and Astrophysics Medal for the highest academic performance during Junior Honours.
- Awarded the Physics Medal for the highest academic performance during my final year.
- Awarded the MPhys Project Prize for the year's best Master's project.
A Levels, Frome Community College, England, 2016-18:
- Physics (A*), Mathematics (A*), Chemistry (A), and Photography (A*): the highest results in the year group.
