London air quality project
Developing machine learning algorithms and data science platforms to understand and improve air quality over London.
Posts by Collection
portfolio
Prize-collecting Travelling Salesperson Problem
Implementation in Python and C++ of algorithms for the Prize-collecting TSP.
TSPLIB with Profits
Python library for parsing TSP with Profits datasets.
Project Odysseus – Understanding London ‘busyness’ and exiting lockdown
Capturing activity over London to better understand ‘busyness’ and aid effective policy-making strategies for exiting the pandemic lockdown.
publications
An Expectation-Based Network Scan Statistic for a COVID-19 Early Warning System
NeurIPS workshop on Machine Learning in Public Health, 2020
Recommended citation: Haycock, C., Thorpe-Woods, E., Walsh, J., O'Hara, P., Giles, O., Dhir, N., and Damoulas, T. (2020). An expectation-based network scan statistic for a covid-19 early warning system. Machine Learning in Public Health workshop, Neural Information Processing Systems.
Download Paper
Near Real-Time Social Distance Estimation In London
The Computer Journal, 2024
Winner of the Wilkes Award 2024, which is presented once a year to the authors of the best paper published in the volume of The Computer Journal.
Recommended citation: James Walsh, Oluwafunmilola Kesa, Andrew Wang, Mihai Ilas, Patrick O’Hara, Oscar Giles, Neil Dhir, Mark Girolami, Theodoros Damoulas, Near Real-Time Social Distance Estimation In London, The Computer Journal, Volume 67, Issue 1, January 2024, Pages 95–109.
Download Paper
Routing on Sparse Graphs with Non-metric Costs for the Prize-collecting Travelling Salesperson Problem
ATT’24: Workshop Agents in Traffic and Transportation, October 19, 2024, Santiago de Compostela, Spain, 2024
Recommended citation: O’Hara, P., Ramanujan, M. S., & Damoulas, T. (2024). Routing on Sparse Graphs with Non-metric Costs for the Prize-collecting Travelling Salesperson Problem. ATT’24: Workshop Agents in Traffic and Transportation.
Download Paper
Distributionally Robust Optimisation with Bayesian Ambiguity Sets
NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty, 2024
Accepted for oral
Recommended citation: Dellaporta*, C., O'Hara*, P., & Damoulas, T. (2024). Distributionally Robust Optimisation with Bayesian Ambiguity Sets. In NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty.
Download Paper
Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets
Under review at ICML, 2025
Recommended citation: Dellaporta*, C., O'Hara*, P., & Damoulas, T. (2024). Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets. arXiv preprint arXiv:2411.16829.
Download Paper
Decision Making under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets
Under review at ICML, 2025
Recommended citation: Dellaporta*, C., O'Hara*, P., & Damoulas, T. (2025). Decision Making under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets.
Download Paper
Bayesian Distributionally Robust Routing Problems
Working paper, 2025
Ongoing work to develop distributionally robust optimisation algorithms for routing problems when the uncertainty is modelled via a Bayesian framework.
Recommended citation: O’Hara, P. & Damoulas, T. (2025). Bayesian Distributionally Robust Routing Problems. Working paper.
talks
Distributionally Robust Routing Problems
Published:
Abstract: Given a probability distribution over the cost function of edges in a graph, a stochastic routing optimisation problem finds a route in the graph that minimises a risk function of the route cost. In practise, it is rare that we have access to the true data-generating distribution over edge costs. Instead, we are given a set of empirical cost observations. From the observations, we can construct a (Bayesian) machine learning model of the costs, then do out-of-sample prediction with our model on new, unseen data. If the observations are noisy or in limited supply - or if our model is misspecified - we might not trust the posterior predictive distribution of the model. In this talk, we discuss routing optimisation algorithms that are robust with respect to an ambiguity set of distributions that are close to the predictive distribution of the model. Real-world applications to air pollution are demonstrated.
Routing on Sparse Graphs with Non-metric Costs for the Prize-collecting Travelling Salesperson Problem
Published:
Abstract: In many real-world routing problems, decision makers must optimise over sparse graphs such as transportation networks with non-metric costs on the edges that do not obey the triangle inequality. Motivated by finding a sufficiently long running route in a city that minimises the air pollution exposure of the runner, we study the Prize-collecting Travelling Salesperson Problem (Pc-TSP) on sparse graphs with non-metric costs. Given an undirected graph with a cost function on the edges and a prize function on the vertices, the goal of Pc-TSP is to find a tour rooted at the origin that minimises the total cost such that the total prize is at least some quota. First, we introduce heuristics designed for sparse graphs with non-metric cost functions where previous work dealt with either a complete graph or a metric cost function. Next, we develop a branch & cut algorithm that employs a new cut we call the disjoint-paths cost-cover (DPCC) cut. Empirical experiments on two datasets show that our heuristics can produce a feasible solution with less cost than a state-of-the-art heuristic from the literature. On datasets with non-metric cost functions, DPCC is found to solve more instances to optimality than the baseline cutting algorithm we compare against.
Distributionally Robust Optimisation with Bayesian Ambiguity Sets
Published:
Click here to watch the talk. See the workshop webpage here.
teaching
CS260 Algorithms - marking (21/22)
Undergraduate course, Department of Computer Science, University of Warwick, 2021
Marking manuscripts for CS260 Algorithms.
CS259 Formal Languages - Seminar Tutor (20/21, 21/22)
Undergraduate course, Department of Computer Science, University of Warwick, 2021
Seminar tutor for approx 30 students in the 21/22 academic year. Subjects taught include regular languages, context-free languages, and turing machines.
Guest Lecture, Convex Optimisation
Masters course, Department of Computer Science, University of Warwick, 2024
Delivered a guest lecture on distributionally robust optimisation and its applications to machine learning to a class of 4th year students. Invited by Dr Marcin Jurdzinski.
Supervision (20/21, 23/24, 24/25)
Undergraduate & masters dissertations, Department of Computer Science, University of Warwick, 2025
Jointly with Prof. Theodoros Damoulas, I have guided students through their dissertations by supporting their development and nurturing their curiosity.