Electronics and Electrical Engineering

Sebastian Neira Castillo (MIEEE, MIET) is a Lecturer in Electrical Machines and Drives at The University of Edinburgh. He received a dual PhD in Engineering from Pontificia Universidad Catolica de Chile and the University of Edinburgh, with a thesis titled "Design of Power Converters with Embedded Energy Storage for Hybrid DC-AC Applications".

His research expertise lies within the power electronics field with extensive practical experience in developing novel power converter topologies and control systems with direct use in electrical machine drives, renewable energy applications and energy storage systems. A core component of his work is the experimental validation of power conversion systems, with experience testing up to megawatt-scale power ratings. Since 2019, he has actively participated in collaborative research projects, resulting in the publication of 1 patent application and 30 peer-reviewed articles.

PhD in Electrical Engineering, Pontificia Universidad Catolica de Chile and University of Edinburgh, 2023.

Título de Ingeniero Civil Electricista (Electrical Engineer), Pontificia Universidad Catolica de Chile, 2016.

Member of the Institute of Electrical and Electronic Engineers (IEEE)
Member of the Institution of Engineering and Technology (IET)
Next Generation Network (NGN) Member of CIGRE

Electronics and Electrical Engineering 1
Power Conversion [4/MSc]
Advanced Power Electronics and Machines [5/MSc]

Risk and Resilience Day 2026

Adaptive Low-Dimensional Monte Carlo Methods for High-Dimensional Multi-Target Tracking

Research Theme

Sensor Signal Processing

Aim

Develop new particle filter solutions for tracking of interacting multiple targets.

Objectives

Develop new interacting particle filters that can tackle multi-target tracking in a scalable manner
Understand the hidden low dimensional structures within multi-target systems to enable high dimensional scalable Monte Carlo filtering
Explore how these models can further be used to predict intent for groups of cooperating and/or adversarial targets.

Description

High-dimensional multi-target tracking poses severe computational challenges for sequential inference. Although particle filters provide a principled Monte Carlo framework, their efficiency collapses with dimension. This project aims to identify and exploit hidden low-dimensional structures within multi-target systems, enabling adaptive filtering methods that retain Monte Carlo flexibility while mitigating the curse of dimensionality. We will develop local particle filters, block-based filtering, and topology discovery methods to automatically detect weakly interacting groups of targets. We may consider scenarios involving tens to a few hundred targets. At this stage, we will primarily use simulated data, with the flexibility to incorporate real data from partners in later stages. The outcome will be scalable algorithms for structured high-dimensional tracking, combining statistical efficiency with real-time feasibility in sensing and defence applications.

Applications

Applications for entry into 2027 will open later in the year.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree.

This project requires a student with a high level of mathematics or numeracy.

Further information on English language requirements for EU/Overseas applicants.

Full funding is available for this project.

31 Jan, 2027