Luisa Fernanda Estrada Plata
Contact
Luisa-Fernanda.Estrada-Plata@warwick.ac.uk
Office: D1.04 (Zeeman)
Side Quests
Outside of my PhD, I like to keep life in motion. I train for a variety of endurance events, from swimming and running to triathlons, climbing and obstacle races...
You name it!
AMP Conference Climbing Social
Member of the Warwick & Oxford organising committee.
Moving Average's Ruby run
40th anniversary of Warwick's Maths, Stats and Computer Science running club.
24h Swim Relay team
Part of the "We Tri for Colombia" challenge. Every 拢1 donated turned into a 1K run, 4K bike or 150m swim. 拢265 raised, Sept 2026.
About Me
I am a PhD student in the Mathematics for Real World Systems CDT, supervised by . I am interested in using Artificial Intelligence for social good. My research focuses on multiagent systems and opinion dynamics on social networks using techniques from Game Theory, Graph Theory and Combinatorics.
I am also a seminar organiser for the Warwick SIAM-IMA chapter. We run the JAMS seminar series and organise social events, hackathons, and the AMP conference. If you are interested in giving a talk, contact us at siam@warwick.ac.uk.
Publications
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Chistikov, D., Estrada, L., Paterson, M. and Turrini, P., 2026. Journal of Autonomous Agents and Multi-Agent Systems (Accepted)
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Estrada, L., Glendinning, S. and Nugent, A., 2026. Invited contribution to La Matematica 5, 65. DOI 10.1007/s44007-026-00240-x
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Estrada, L., H枚gele, M. and Steinicke, A., 2026. Latin American Journal of Probability and Mathematical Statistics. v. XXII, p. 105-141. DOI 10.30757/ALEA.v23-06.
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Chistikov, D., Estrada, L., Paterson, M. and Turrini, P., 2024. In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 鈥�24, 363鈥�371. Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems. ISBN 9798400704864.
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Estrada, L. and H枚gele, M., 2022. Statistics and Probability Letters. vol. 190. ISSN 0167-7152.
Recorded Talks
I have given several talks at the SPAAM Seminar during my PhD; some recordings are available here. If you would like to get in touch, feel free to contact me.
Learning a Social Network by Influencing Opinions
Overview: We study a campaigner who seeks to learn the structure of a social network by observing the underlying diffusion process and intervening on it. We focus on the dynamics of synchronous majority updates on binary opinions. We derive upper bounds on the budget required to learn any network with certainty, accounting for both observation and intervention resources, and further refine these bounds for clique networks. We also examine the campaigner's learning performance when the available budget falls below these bounds. In such cases, we propose a greedy intervention strategy that maximises information gain at each opinion diffusion step.
PAC Learning Social Networks
Overview: Agents in social networks change opinions when influenced by sufficiently many peers. Existing literature typically assumes that the network structure and dynamics are fully known, which is often unrealistic. We ask how hard it is to learn a network structure from samples of the agents' synchronous opinion updates. Not exactly, but in a Probably Approximately Correct (PAC) sense. The answer (spoiler): It is HARD. However, we propose a polynomial-time greedy algorithm that performs well in experiments across various classes of random graphs.