{"schemaVersion":"1.0","provenance":"Owner-supplied professional and project descriptions; earlier non-conflicting portfolio material retained. Not independent verification.","profile":{"name":"Owen Cheung","role":"AI Engineer","tagline":"AI systems, built with judgement.","intro":"AI Engineer at LexisNexis Risk Solutions, building human-governed workflows for fraud-model optimisation. Final-year Computer Science & AI student at the University of Bath.","heroHeadline":"Human-reviewed AI workflows.\nApplied research, made useful.","heroContext":"LexisNexis Risk Solutions / CS & AI at Bath","location":"Bath, UK","email":"oc608@bath.ac.uk","github":"https://github.com/OwenC05","linkedin":"https://www.linkedin.com/in/owen-cheung-472998225/","bio":["I'm Owen, an AI Engineer at LexisNexis Risk Solutions and a final-year Computer Science & AI student at the University of Bath. I stayed on as a contractor after my industrial placement to develop a self-initiated prototype into a funded internal pilot.","I enjoy the freedom to investigate a problem, explore emerging methods and put promising ideas to the test. My work connects representation-learning research with practical AI engineering: from graph and contrastive methods to recoverable, human-governed systems.","Away from the screen I snowboard with Bath Snowsports, pull a lot of espresso, and build mechanical keyboards — which is how I ended up typing at 193 wpm (and designing TypeForge)."]},"experience":[{"org":"LexisNexis Risk Solutions","role":"AI Engineer (Agentic AI)","period":"Aug 2026 — present · Contract","location":"London","note":"Architected a solution designed for organisation-wide use, built an end-to-end demo, and presented the concept, value and scaling approach to senior stakeholders. Now developing the funded internal pilot toward production."},{"org":"LexisNexis Risk Solutions","role":"Data Scientist","period":"Jul 2025 — Aug 2026 · Industrial placement","note":"Large-scale financial data analysis, two fraud-model optimisations and self-directed representation-learning research. Gained experience in GPU compute and batch training, and initiated an agentic working demo."},{"org":"Dish'D","role":"Full-Stack Developer","period":"Jan 2024 — Feb 2025","location":"London","note":"Built a social cooking app end-to-end with Django + Flutter; led technical decisions."},{"org":"Kinetix","role":"System Tester","period":"Jul 2024 — Aug 2024","location":"Hong Kong","note":"QA for Census & Statistics Department systems — validated functionality against spec."},{"org":"DXC Technology","role":"AI Project Intern","period":"Jul 2022 — Aug 2022","location":"Hong Kong","note":"Drafted technical proposals for enterprise bids (AI-assisted mapping, OpenBIM.AI)."}],"education":[{"org":"University of Bath","detail":"BSc (Hons) Computer Science & Artificial Intelligence, with placement","period":"Sept 2023 — 2027 (expected)","note":"Expected 2:1"},{"org":"Wellington College","detail":"A-Levels — Maths, Further Maths, Physics, Computer Science","period":"Aug 2021 — Aug 2023"}],"skills":{"AI / ML":["PyTorch","Contrastive learning","Hyperbolic embeddings","Agentic systems","Deep learning","Multimodal contrastive learning (study)"],"Languages":["Python","TypeScript","Haskell","Dart","SQL"],"Frameworks":["React / Node","Django","Flutter","Next.js"],"Data":["SQL","Snowflake","LightGBM","Statistical modelling","Data analysis"],"Compute":["GPU compute","Batch training"],"Cloud / AI":["Azure OpenAI","Azure Foundry and AWS S3 (workshop experience)"]},"capabilities":[{"id":"research","title":"ML Research","labels":["PyTorch","Multimodal contrastive learning (study)"],"summary":"Following a question beyond the obvious answer. My research explores how models learn useful representations of complex behaviour.","evidence":["Researched self-supervised representations using graph embeddings, contrastive learning and non-Euclidean geometry.","Used the autonomy of my role to investigate emerging methods and experiment with their application to practical problems.","Multimodal contrastive learning is an area of study, not a claim of an implemented or deployed multimodal system."]},{"id":"ai-systems","title":"AI Systems","labels":["Python","Agent orchestration","Human review"],"summary":"Taking an idea from architecture to an end-to-end demonstration, and making its value clear to the people deciding what comes next.","evidence":["Designed an agentic solution for organisation-wide use, with persistent state, audit trails and mandatory human-review gates.","Built an end-to-end working demonstration. Presented the core concept, value and scaling approach to senior stakeholders.","Retained to develop a funded internal pilot. Intended scope is not a claim of organisation-wide deployment."]},{"id":"data-modelling","title":"Data & Modelling","labels":["SQL","GPU compute","Batch training"],"summary":"Working with large-scale financial data, then connecting analysis, modelling and evaluation to a concrete problem.","evidence":["Analysed financial transaction data with Python, SQL and Snowflake, and completed two fraud-model optimisations during placement.","Applied feature engineering, hyperparameter tuning and performance evaluation.","Gained experience in GPU compute and batch training; no public throughput or model-performance benchmark is claimed."]}],"interests":[{"label":"Snowboarding","note":"Carving and freestyle with Bath Snowsports — away from the screen."},{"label":"Specialty coffee","note":"Chasing brew methods and flavour."},{"label":"Mechanical keyboards","note":"Built several from scratch; reached 193 wpm."}],"projects":[{"id":"lexisnexis","slug":"lexisnexis-applied-ai","title":"LexisNexis — Applied AI","tagline":"From an idea to an end-to-end demonstration.","blurb":"Architecture, an end-to-end demo and senior-stakeholder communication. A self-initiated idea progressing into a funded internal pilot.","year":"Jul 2025 — present","role":"AI Engineer (Agentic AI) · previously Data Scientist","stack":["Python","SQLite","DVC","Papermill","LightGBM"],"summary":"I architected a fraud-model optimisation solution designed for organisation-wide use and built an end-to-end working demonstration. I presented its core concept, business value and approach to scaling to senior stakeholders. After my placement, I was retained as an AI engineering contractor to develop the funded internal pilot toward production.","highlights":["Custom Python orchestration, reusable Copilot skills, persistent SQLite state, audit trails and mandatory human review gates.","Recoverable DVC/Papermill workflows with LightGBM candidate-rule discovery and constrained logistic-regression policy weighting.","Built an end-to-end demonstration and presented the core concept, business value and scaling approach to senior stakeholders."],"status":"Internal pilot · toward production","context":"Fraud-model optimisation combines analysis, modelling and decisions that need accountable human review.","contribution":"Architected a solution designed for organisation-wide use, built an end-to-end demonstration, and presented its concept, value and scaling approach to senior stakeholders. Retained after placement to develop the funded internal pilot.","approach":["Custom Python orchestration and reusable Copilot skills coordinate work with persistent SQLite state.","Audit trails and mandatory review gates keep human judgement in the workflow.","DVC and Papermill make notebook-based work recoverable; LightGBM discovers candidate rules, with constrained logistic regression for policy weighting."],"evidence":["End-to-end working demo delivered by the end of the placement. Presented the core concept, value and scaling approach to senior stakeholders.","Retained as an AI engineering contractor in August 2026 to develop a capital-funded internal pilot."],"limitations":["Employer-confidential work: no source code, transaction data or internal evaluation results are published.","This describes a pilot building toward production, not a launched production platform.","Organisation-wide use describes the intended architectural scope, not a claim of organisation-wide deployment or tested production scale.","Descriptions are owner-supplied; no independently verified performance uplift is claimed."],"confidential":true,"selected":true,"url":"https://owencdev.info/projects/lexisnexis-applied-ai","markdownUrl":"https://owencdev.info/projects/lexisnexis-applied-ai/markdown"},{"id":"lexisnexis-ml","slug":"lexisnexis-ml","title":"LexisNexis — ML","tagline":"Understanding fraud through data and representation.","blurb":"Fraud-model optimisation, financial-data analysis and research into self-supervised representations during my industrial placement.","year":"Jul 2025 — Aug 2026","role":"Data Scientist · Industrial placement","stack":["Python","SQL","Snowflake","Feature engineering","Representation learning"],"summary":"During my industrial placement at LexisNexis Risk Solutions, I completed two fraud-model optimisations and analysed large-scale financial data using Python, SQL and Snowflake. Alongside this applied work, I researched self-supervised fraud representations and gained practical experience in GPU compute and batch training.","highlights":["Completed two fraud-model optimisations, working across feature engineering, tuning and evaluation.","Analysed large-scale financial data using Python, SQL and Snowflake.","Researched graph embeddings, contrastive learning and non-Euclidean geometry for self-supervised fraud representations.","Gained practical experience in GPU compute and batch training."],"status":"Industrial placement","context":"Fraud modelling involves understanding complex financial behaviour, developing useful features and evaluating changes carefully.","contribution":"Completed two fraud-model optimisations and analysed large-scale financial data. Researched self-supervised representations alongside practical modelling work.","approach":["Used Python, SQL and Snowflake to analyse large-scale financial data.","Worked across feature engineering, model tuning and evaluation for fraud-model optimisation.","Investigated graph embeddings, contrastive learning and non-Euclidean geometry as approaches to self-supervised fraud representations.","Gained practical experience in GPU compute and batch training."],"evidence":["Two fraud-model optimisations completed during the industrial placement, July 2025 to August 2026.","Research into self-supervised fraud representations accompanied applied data-science work."],"limitations":["Employer-confidential work: no transaction data, source code or internal evaluation results are published.","Research directions are not claims of deployed models or measured performance uplift.","Descriptions are owner-supplied; no independently verified results are claimed."],"confidential":true,"selected":true,"url":"https://owencdev.info/projects/lexisnexis-ml","markdownUrl":"https://owencdev.info/projects/lexisnexis-ml/markdown"},{"id":"typeforge","slug":"typeforge","title":"TypeForge","tagline":"Practice that adapts to the way you type.","blurb":"A typing application in development, with implemented adaptive AI drills and real-time analytics being developed.","year":"Jul 2025 — present","role":"Solo developer","stack":["React","Node.js","TypeScript","Keystroke analytics"],"summary":"I am developing a typing application that tracks cadence, keystrokes and accuracy, with AI-driven drills designed and implemented to adapt to user weaknesses. It comes from building mechanical keyboards and reaching 193 wpm. A public demo is available; the product remains in development, with no public evaluation or full product release claimed.","highlights":["Designed and implemented adaptive AI-driven drills.","Developing real-time cadence, keystroke and accuracy analytics.","A personal exploration of more targeted typing practice."],"status":"In development","context":"Typing practice can focus on a headline speed rather than the particular weaknesses a person needs to practise.","contribution":"Designing and building a React/Node.js typing application, including implemented AI-driven adaptive drills.","approach":["Developing real-time analytics for cadence, keystrokes and accuracy.","Adaptive drills target user weaknesses.","Personal typing and keyboard-building experience informs the product direction."],"evidence":["Adaptive AI-driven drills designed and implemented, as described in my current project record."],"limitations":["A public demo is available; the product is still in development, not a full product release.","No measured learning uplift, timing precision or model benchmark is published."],"confidential":false,"selected":true,"url":"https://owencdev.info/projects/typeforge","markdownUrl":"https://owencdev.info/projects/typeforge/markdown","liveUrl":"https://typeforge-alpha.vercel.app"}]}