Applied Mathematics, Data Science & AI Research
- Applied Mathematics
- Mathematical Modelling
- Scientific Computing
- Data Science
- Artificial Intelligence
- Healthcare Analytics
Geovany Batista Polo Laguerre
Mathematics is the foundation everything else stands on. I build outward from it, through scientific computing, artificial intelligence, and healthcare analytics, to turn complex systems into decisions people can trust.
A model earns trust only when its mathematics does. That conviction is what connects every project on this page, from differential equations to machine learning to public health.
Research Highlights
These eight areas are not separate disciplines. They are complementary perspectives on the same underlying question: how to study complex systems rigorously enough to trust the answers they give us.
Applied Mathematics
Pure mathematics and functional analysis as a foundation for modeling real systems, from finite structures to continuous dynamics.
- Functional Analysis
- Group Theory
- Differential Equations
Current: Character Theory in Finite Fields
Numerical Analysis
Discretization and stability of PDE/ODE systems, from finite differences to operator splitting schemes.
- Finite Differences
- Operator Splitting
- Lax-Friedrichs
Current: Westervelt equation simulations, LAMIA
Scientific Computing
Reproducible numerical simulation of physical and environmental systems, from ultrasound propagation to atmospheric flow.
- Python
- C++
- Symbolic & Numerical Methods
Current: Cyclone flow modeling, MEMIAD
Mathematical Modelling
Compartmental and non-autonomous ODE systems for environmental transfer and exposure dynamics under seasonal forcing.
- Compartmental Models
- ODE Systems
- R / Shiny
Current: Chlordecone contamination modeling
Artificial Intelligence
Machine learning and multi-agent systems applied to decision support, from classical ML to reproducible research tooling.
- Scikit-learn
- TensorFlow / PyTorch
- Multi-agent Systems
Current: Scientific Assistant research framework
Healthcare Data
Data-driven decision support for clinical and public-health contexts, from triage prioritization to exposure modeling.
- Random Forest
- Risk Scoring
- Python
Current: Medical Triage System
Biostatistics
Statistical inference and predictive modeling for health and environmental data, strengthened through the Master's Environment & Health specialization and applied work on healthcare data.
- Statistical Inference
- Predictive Modeling
- R
Current: Environment & Health specialization, Master's programme
Digital Health
Interactive tools that turn mathematical models into usable decision-support instruments for researchers and practitioners.
- Shiny
- Data Visualization
- Reproducible Pipelines
Current: Interactive simulation dashboards
Individually, each is a discipline. Together, they form one way of thinking about complex systems: the profile below is what that balance looks like in practice.
Interdisciplinary Balance
Scientific Profile
Read this less as a skills inventory and more as a map of balance. Each axis depends on the others: mathematical modelling means little without the computing to test it, and computing means little without the statistical grounding to trust its output.
Toolkit
The tools that make this balance operational:
- Python
- R
- SQL
- C++
- TypeScript
- DuckDB
- Apache Iceberg
- Trino
- dbt
- Shiny
- TensorFlow
- Scikit-learn
- PyTorch
The sections that follow show that balance applied to real problems: the projects currently shaping my research identity.
Current Work
Active Projects
Three projects currently define where my research is headed.
Behind each of these is also a less formal record of how the thinking actually unfolded, which is what Geo's Stories is for.
Beyond The Models
Geo’s Stories
Every research project also has a human story: the ideas, experiments, unexpected discoveries, failures, and lessons learned along the way. I write about that side of the work on Geo's Stories.
Get In Touch
Research Collaboration
Good research rarely happens alone. I’m always open to interdisciplinary collaboration, knowledge sharing, and conversations that might turn into something more, with labs, supervisors, or fellow researchers working on related problems. For detailed background, see my CV or explore my Scientific Journey.
Research Conversation
A 30-minute conversation about research ideas, collaboration, or a specific data or modelling problem you're working through.
Open the scheduling page or use the direct Calendly link: calendly.com/geobatpo07/30-minutes-de-causerie