About
Applied Data Scientist and Consultant with 7+ years of experience delivering production ML/GenAI solutions across finance, healthcare and the public sector. Drove £8M+ in projected savings across a £10M programme portfolio. PhD in Engineering from University of Cambridge with a track record of leading cross-functional teams from concept to production and driving executive adoption.
My research focuses on applied machine learning that is reliable at scale. On the scientific side, I develop computer-vision pipelines for high-throughput microscopy to quantify drug-induced cellular changes and accelerate cancer research. On the enterprise side, I design and deploy GenAI systems on cloud platforms with an emphasis on safety, robustness, and cost-aware MLOps. A core thread is AI cybersecurity—mapping threat vectors across data, model, and deployment layers—and translating them into governance patterns, evaluation harnesses, and guardrails. Broadly, I’m interested in human-centred, interpretable AI that delivers measurable value in healthcare, finance, and the public sector.
Publications
Rittick Barua, Kevin McCay, Mohammed Al-Khalidi, Yonghong Peng, Jamie Crossman-Smith
Cyber security risks to artificial intelligence (Whitepaper)
Department of Science, Innovation and Technology - 2024
Adam Colbourne, Thomas Blythe, Rittick Barua, Sean Lovett, Jonathan Mitchell, Andrew Sederman, Lynn F Gladden
Validation of a low field Rheo-NMR instrument and application to shear-induced migration of suspended non-colloidal particles in Couette flow
Journal of Magnetic Resonance - 2018
Barua, R., Lee, K., Mills, N., Ouki, S., Thorpe, R.
The effects of steam explosion and hydrolysing time on the digestibility of sewage sludge in anaerobic digestion
AcquEnviro - 2014