Li Da · Luxembourg
I’ve spent more than a decade building software across mobile products, enterprise systems and digital transformation. More recently, my work has moved toward FinTech, AI and ICT risk—exploring how complex regulatory information can become usable software.
Currently building
I’m exploring how regulatory information can move from raw legal text to structured knowledge, evidence-grounded AI and eventually executable risk workflows.
Make source material structured and explorable.
Find evidence and answer with traceable citations.
Turn obligations into structured risk work.
Raw regulation→Structured knowledge→Evidence→Decision workflow
Knowledge layer / where it starts
Turning fragmented regulatory texts into structured, explorable knowledge.
Intelligence layer / where it gets useful
Evidence-grounded regulatory question answering.
A good answer is not the starting point. The difficult part is deciding what the question is really asking, finding evidence for each claim and making uncertainty visible.
I don’t want the model to sound confident. I want it to show its work.
Workflow layer / where it meets the work
Turning regulatory obligations into structured ICT risk workflows.
An experiment in bringing regulatory evidence, risk reasoning and second-line challenge into one reviewable workflow—not automating away professional judgement.
From obligation to opinion
The copilot proposes. The second line still challenges.
Selected work
Recent research and engineering work that shows the ground underneath my current direction: learning from messy data, modelling relationships and designing systems that have to communicate.
A multi-part study of Ethereum transaction data—from preparation and descriptive statistics to graph construction, whales and MEV behaviour.
What interested me Turning raw transactions into a network that can be inspected, measured and questioned. View the series ↗
Exploring whether cross-asset behaviour can reveal market fear regimes through dimensionality reduction, clustering and network structure.
Read the project ↗
Industrial and IoT systems connecting devices, client applications and backend services through dependable, message-driven workflows.
View reference ↗Experience
Software engineering led to enterprise transformation, deeper study in data and FinTech, and now practical work around ICT risk and regulatory AI.
Luxembourg · European headquarters
At the European headquarters of an international bank, responsible for IT security and AI work across 13 banking entities.
France · Italy
Graduate study focused on computer science, data, financial technology and applied machine learning.
China
Enterprise systems, SaaS workflows, backend platforms and technical leadership.
Shenzhen · Changsha
Mobile products, internet applications, backend services and client/server systems.
Tools I work with
Grouped by where I use them—not as a claim to know every tool equally.
Python
FastAPI
Pandas
NumPy
Scikit-learn
Java
Spring
MySQL
MongoDB
Docker
Linux
JavaScript
TypeScript
React
HTML
CSS
Git
GCP
AWS
iOS
Flutter
Hadoop
Spark
Cocos2dWriting
Long-form thinking about regulatory technology, cybersecurity, AI engineering and network science.
Vulnerability remediation, ownership and accountability.
↗ Market Fear Regime IdentificationClustering and network analysis across market regimes.
↗ Working Without a GPUA practical guide to deep-learning work with constrained compute.
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