Director of Data Analytics & AI · MealSuite
I lead AI where mistakes are expensive.
I own AI end to end, deciding what AI can be trusted with, what stays deterministic, and how it is governed. I work the way I want companies to work, alongside AI agents I built as digital workers.
Publishes academically as Wei Xu · ORCID 0000-0002-0271-1944
Questions people ask
Who is Dr. Linda Xu?
Director of Data Analytics & AI at MealSuite, where she owns AI end to end: AI strategy, AI governance and decision-support systems. PhD in operations research, University of Plymouth.
Is she the same person as Wei Xu?
Yes. She publishes academically as Wei Xu (ORCID 0000-0002-0271-1944), on supply chains under uncertainty and on technology management.
What has she delivered?
Over 30 departments under central AI governance, a 38% reduction in AI cost, and 99.9% accuracy in standard reporting.
How does she work?
Alongside AI agents she built as digital workers. They take the repeat work, so her time goes to strategy, judgement and people.
Path to AI leadership
- PhD
PhD, Operations Research
University of PlymouthSimulation and genetic algorithms for supply chains under uncertainty - 2017–2019
Research Lead, IoT and Digital Twin
University of Sheffield32% higher factory productivity from a digital twin for modular housing - 2019–2022
Consultant Director, Digital Supply Chain Strategy
Syst DynamicsMore than €16M of digital and AI/ML programmes - 2022–2026
Enterprise Automation and Data Transformation Lead
Veralto12-person team, $15M budget, 95% fewer support emails - 2026–present
Director of Data Analytics & AI
MealSuiteOwns AI end to end: 38% lower AI cost, over 30 departments governed
Selected results
| Outcome | Result | What it involved | Role | Evidence |
|---|---|---|---|---|
| AI cost | −38% | Central visibility of AI spend by department, vendor and token use | Current role | – |
| AI governance | 30+ | Departments under central governance; every MCP connection governed; real-time AI cost for every employee | Current role | Barndoor case study |
| Reporting accuracy | 99.9% | Hybrid deterministic and LLM design; model routing cut LLM spend by up to 30%, part of the 38% | Current role | – |
| Support emails | −95% | AI order assistant across 800,000+ SKUs | Veralto | – |
| Freight audit | $460K | Recovered each year, with 96% less manual work | Veralto | – |
| Supply chain cost | 36–48% | Lower cost from optimised strategies in simulation tests on company data | PhD research | Operational Research, 2022 |
Featured · Barndoor customer story
“There is no playbook.”
What I told our CTO on my first day in the role, when staff did not know what to do or if anything should connect to AI. Barndoor’s case study describes how my team applied PII and prompt-injection policies to the servers that hold customer information, and brought over 30 departments under central MCP governance.
Read the case studyHow I work
I built AI agents that work as digital workers for me. They take the repeat work, so my time goes to strategy, hands-on model work and the people behind each project.
My digital workers take
- The weekly AI spending report
- The weekly AI brief
- Travel booking in Ramp
- Organising my calendar
- Deadline reminders
- Keeping the AI governance onboarding plan up to date
I keep the judgement
- Data architecture under complicated regulations
- How to start AI governance when there is no playbook
- What our strategy should be when new models arrive every few weeks
- How to build the model lifecycle
- What return an agent can really bring
Every MCP connection is governed, my own agents included.
Research
From mathematical models to machine intelligence
My PhD in operations research, at the University of Plymouth, built mathematical models of multi-stage supply chains under uncertainty. I tested strategies in simulation against what the companies actually did, then tuned them with optimisation algorithms: genetic algorithms, including a multi-objective version, and stochastic approximation. It was machine intelligence before language models: a computer searching for better decisions than people could find by hand.
The tools I use now are different. The discipline is the same: model the decision, define what good means, test against the current way of working, then tune. I publish academically as Wei Xu.
lower cost from optimised strategies against the company’s original approach, in simulation tests on its own data (Xu and Song, Operational Research, 2022)
Mathematical modelling
Multi-stage supply chains with uncertain lead times and demand
Simulation
Strategies tested against what each company actually did
Optimisation algorithms
Genetic algorithms and stochastic approximation
Machine learning
Decision trees, with rules a person can read and explain
Language models
Fine-tuning and reinforcement-learning training on a self-hosted model