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Postdoctoral Researchers
Zetai Cen
University of Bristol
Zetai Cen obtained his PhD in Statistics at the London School of Economics and Political Science, with research interests in tensor data, high-dimensional statistics, factor modelling, and spatiotemporal modelling.
Han Yan
LSE
Han Yan is a Research Officer in the Department of Statistics at the London School of Economics (LSE), working with Professor Qiwei Yao. His research interests include various aspects in statisics and machine learning, such as time series analysis, anomaly detection and deep learning. Prior to joining LSE, he obtained his PhD in statistics at Peking University in 2025, under the supervision of Professor Song Xi Chen.
Gengyu Xue
University of Warwick
Gengyu Xue previously obtained her PhD in the Warwick Statistics Centre for Doctoral Training, under the joint supervision of Professor Yi Yu and Dr Haotian Xu.
Her research interests include change point analysis, differential privacy, fairness and functional data analysis. Before starting her PhD, she received her Master’s degree from the University of Oxford and her Bachelor’s degree from the University of Warwick.
Laura Baracaldo Lancheros
Lancaster University
Laura Baracaldo Lancheros obtained her PhD in Statistical Science from the University of California, Santa Cruz, under the supervision of Professor Rajarshi Guhaniyogi.
She previously worked as a Visiting Assistant Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara.
Her research interests include Bayesian high-dimensional spatio-temporal modelling, scalable Bayesian methods for structured data, and applications in ecology, neuroscience, epidemiology, and genomics.
Kes Ward
Lancaster University
Kes works particularly with high-velocity count data such as streams of photons, where no one photon is anomalous but the signals they create must be monitored to find emerging patterns. Kes is interested in computational questions about designing efficient and effective anomaly detection algorithms for these kinds of scenarios. How do we find anomalous intervals in one signal if the number of intervals in our signal is too large to check them all? How can we find anomalies occurring over subsets of different signals if the number of possible subsets is too large to check as well? What do we do if we want to solve both of these problems at once? And how can we adapt an anomaly detection method based on a Normality assumption to work with data that is not Normally distributed?
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