Dr Kristijonas Cyras has completed a BSc Mathematics degree (Honours) at Warwick University, and an MSc Mathematics Research degree (Distinction) with emphasis on mathematical logic at Leeds University. Kristijonas has completed a PhD Research in Computing degree with emphasis on Artificial Intelligence, particularly argumentation, at Imperial College London. Kristijonas has been a Research Associate at Imperial College London since May 2017, working on applications of argumentation to legislative and medical domains, as well as on explainable AI.
Since 2017, Kristijonas is working on developing novel Learning Health System techniques to facilitate Universal Health Coverage (UHC) in low- and middle-income countries, as part of the ROAD2H Project, in collaboration with the Institute for Global Health Innovation (IGHI) at Imperial College London, King’s College London, University of Serbia and China National Health Development Research Center (CNHDRC). In particular, Kristijonas is working on developping a novel argumentation model integrated with resource optimisation to alleviate health-care related decision making.
Kristijonas is a Postdoc Rep for the Department of Computing.
et al., 2018, Argumentation for explainable reasoning with conflicting medical recommendations, Reasoning with Ambiguous and Conflicting Evidence and Recommendations in Medicine (MedRACER 2018), Pages:14-22
Cocarascu O, Cyras K, Toni F, 2018, Explanatory predictions with artificial neural networks and argumentation, Workshop on Explainable Artificial Intelligence (XAI)
Cyras K, Satoh K, Toni F, 2016, Abstract Argumentation for Case-Based Reasoning., Fifteenth International Conference on the Principles of Knowledge Representation and Reasoning (KR 2016), AAAI Press, Pages:549-552
Cyras K, Toni F, 2016, ABA+: Assumption-Based Argumentation with Preferences., AAAI Press, Pages:553-556
Cyras K, Oliveira T, Argumentation for reasoning with conflicting clinical guidelines and preferences, 16th International Conference on Principles of Knowledge Representation and Reasoning, AAAI
et al., Argumentation for Explainable Scheduling, Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)