Research Project Planning & AI Experiments Management
Manage your water AI research pipeline with mindmaps, Gantt charts, and scientific todo-lists.
GeoAI & Machine Learning Engineer
About Me
Tetiana Starovoyt is a researcher and PhD candidate focused on developing hybrid neural network architectures for smart water infrastructure. Her work combines graph-based AI (GNN), physics-informed learning (PINN), and optimization tools such as EPANET, SCADA, and GIS to support critical infrastructure resilience.
Interests
- Hybrid AI & Graph Neural Networks
- Smart Water Systems & EPANET Modeling
- Physics-Informed Neural Networks (PINN)
- Critical Infrastructure Resilience
Education
PhD Artificial Intelligence for Water Infrastructure
National Technical University of Ukraine "KPI"
MSc Water Engineering & Data Science
NUWEE
BSc Systems Analysis
KPI
My Research
Use this area to speak to your mission. I’m a research scientist in the Moonshot team at DeepMind. I blog about machine learning, deep learning, and moonshots.
I apply a range of qualitative and quantitative methods to comprehensively investigate the role of science and technology in the economy.
Please reach out to collaborate
Featured Publications
Recent Publications
Recent & Upcoming Talks
Recent News
Adaptive AI for Smart Water Networks
Hybrid Graph-PINN models for sustainable water infrastructure and leak detection.
Visualizing Smart Water Systems with AI & GIS
How I combine Plotly, Mermaid, and structured data to explain GNN-PINN models and SCADA behavior in water distribution.
My Teaching Courses
Overview of teaching activities and materials
Structure your research with a second brain
Build your personal knowledge base for hybrid AI, water systems, and open science — and easily share insights with your collaborators.