Causal Inference and Generative Models in Dynamic Processes to Analyze and Enhance Resilience in Complex Networks

Project ID: 2531bd1660

(You will need this ID for your application)

Research Theme: Artificial Intelligence and Robotics

Research Area(s): Artificial Intelligence Technologies
control engineering
Energy networks

UCL Lead department: Electronic and Electrical Engineering (EEE)

Department Website

Lead Supervisor: Laura Toni

Project Summary:

Do you want to teach AI to understand the ‘why’ behind complex global events? Imagine an AI that can not only predict a power outage but also pinpoint its root cause, or simulate future weather patterns based on different environmental decisions. This project dives into the heart of how interconnected systems work, from our planet’s climate to the infrastructure that powers our cities.

We are looking for a creative and driven PhD candidate to explore the intersection of causality, network science, and generative AI. Your mission will be to build intelligent systems that can understand the chain of events in complex networks. This has two exciting sides. First, you’ll develop generative models that can look into the future, creating multiple “what-if” scenarios to help us make better decisions—for instance, how a change in policy might affect a water distribution network. Second, you will reverse-engineer phenomena, tracing events back to their source to understand their origin, like finding the precise location of a leak or the starting point of a cascading failure in a power grid.

You will be working with cutting-edge techniques like graph generative models and decision-making under uncertainty. The applications are vital for building a more sustainable future: improving the resilience of our energy and water systems, and deepening our understanding of large-scale environmental phenomena.

This is a chance to work on fundamental AI challenges with direct, real-world impact. If you have a strong background in computer science / engineering or a related field and are passionate about using AI to solve complex, meaningful problems, apply to this scholarship.