From rigorous research to practical AI strategy.
I'm the Founder of Nightingale Research, and Head of Research at Alpha Level. With a PhD in Statistics from Imperial College London, I specialise in turning cutting-edge AI research into real-world solutions, be it algorithms that cut cyber security false positives by up to 90%, to leading electricity price forecasts for energy trading.
I've designed and taught AI content to a range of professionals from apprentices to top executives at FTSE 100 companies, and I'm an elected committee member for the Royal Statistical Society Business and Industrial Section. Whether it's building models or demystifying AI for decision-makers, I love making the complex accessible and impactful.
I partner with organisations to bridge the gap between cutting-edge AI research and real-world implementation. Whether you need strategic guidance, hands-on technical expertise, or help navigating the rapidly evolving AI landscape, I bring academic rigour combined with practical industry experience.
Focused sessions to discuss your AI challenges, explore solutions, and get expert advice.
Bespoke machine learning models and AI systems tailored to your specific business needs.
Strategic guidance on AI adoption, implementation roadmaps, and building internal capabilities.
Cutting-edge R&D projects applying the latest statistical and ML methodologies.
Upskill your team with engaging workshops and talks that demystify AI and inspire innovation.
Academic-grade review of existing code with summary report, improvement suggestions, and advice on boosting performance and ROI.
Research on parameter estimation methods for point processes with binned observations, published in JCGS.
Read Paper →Generalising parameter estimation to the multivariate case using MC-EM, published in Statistics and Computing.
Read Paper →Ready to explore how AI can transform your work? I'd love to hear about your challenges and discuss how we might work together.
Or connect with me and drop a message on LinkedIn
Connect on LinkedIn