Meet the researchers behind PaCE.
Principal Investigator
Thomas holds a PhD in Political Science from the University of Michigan. His research explores the causes of war, conflict forecasting, and political methodology. He is the principal investigator of ERC grant PaCE: Patterns of Conflict Escalation (2022-26).
thomas.chadefaux@tcd.ie
PhD Student
Emmanuel is a PhD candidate at Trinity College Dublin, where he conducts research on developing algorithms for clustering functional and longitudinal data. He has a strong mathematical background, having obtained two master's degrees in Mathematical Sciences and Data Science from the African Institute of Mathematical Sciences in Ghana and the Skolkovo Institute of Science and Technology in Moscow, respectively. His research interests span across Applied Mathematics, Statistics, and Machine Learning.
eakeweje@tcd.ie
Project Manager
Jemimah was awarded a MSc in Applied Social Research and a PhD in Sociology from Trinity College Dublin. She has worked on a number of EU funded research projects and currently acts as project manager for PaCE, supporting the team's research activities; liaising with colleagues across Trinity College and beyond; managing the website and events.
baileyje@tcd.ie
Research Fellow
Jungmin Han is a postdoctoral researcher at Trinity College Dublin. With a primary interest in international rivalry, foreign policy, and public behaviours in democracy, the motivation of his research is to enhance understanding of the micro-foundation of enduring rivalries and their domestic consequences. He employs a multi-method research strategy to generate empirical insights, including quantitative analyses of observational and experimental data and machine-learning approaches.
jhan@tcd.ie
PhD Student
Junjie holds an MPhil degree in Probability and Mathematical Statistics from Hong Kong Baptist University, and his research interests include statistical inference, spatio-temporal model, and text analysis. He is currently a PhD student at Trinity College Dublin and investigating conflict forecasting.
liuj13@tcd.ie
Research Fellow
Chien Lu is currently a research fellow in the PaCE project. He obtained his Ph.D. from Tampere University in Finland. His dissertation explored the intersection of machine learning with a focus on probabilistic representation learning methods and game culture studies. His research interests are centered on developing computational methods to study social phenomena, with an emphasis on probabilistic machine learning, and Bayesian data analysis.
luc4@tcd.ie
Postdoctoral Researcher
Josh is a postdoctoral researcher at Trinity College Dublin in the PaCE project team. He did a PhD in Electrical Engineering and Electronics at the University of Liverpool as part of the Distributed Algorithms Centre for Doctoral Training. His thesis was on batch Bayesian parameter estimation under concept drift. His research interests include uncertainty quantification, time series analysis in changing environments, graph neural networks and sequential Monte Carlo methods. He has a particular interest in using these methods for epidemiological applications and drawing parallels between epidemiology and conflict prediction.
Research Fellow
Yohan is currently working in the PaCE project team at Trinity College Dublin as a research fellow. He received a Ph.D. in Political Science at Texas A&M University. His research focuses on the study of conflict processes and political economy, with a particular interest in the use of machine learning and statistical methods to analyse these topics.
yohan.park@tcd.ie
Research Fellow
Guoxin was awarded a PhD from University College Dublin in 2024. He developed an MAE-based pretraining framework for ECG analysis and an ECG biometric authentication approach using self-supervised learning for IoT edge sensors. Earlier, he worked on continuous user authentication using a genuine wearable chest-strap ECG device. He is currently a research fellow in the PaCE project team at Trinity College Dublin. His research interests include self- and unsupervised learning for physiological time-series (ECG), wearable sensing and edge AI, efficient deployment (quantization, pruning), and dynamic neural networks.
Research Assistant (2022-23)