Małgorzata Forystek is a Statistics Master's student at the Bayesian Computational Statistics & Modeling research group (BAYESCOMP) under the supervision of Professor Håvard Rue. She received her B.Sc. degree in Applied Mathematics from AGH University of Science and Technology in Kraków, Poland, in 2023.
Her research interest is mainly in Bayesian and computational Statistics and Data Science.
Sihan Chen joined the PhD program in Statistics at King Abdullah University of Science and Technology (KAUST) in 2021, under the supervision of Prof. Marc G. Genton. Prior to that, he received his Bachelor’s and Master’s degrees from The Chinese University of Hong Kong. Sihan's doctoral research focused on developing robust and scalable statistical methods for spatial data analysis. His research interests include spatial statistics, robust estimation, high-performance computing, and applied data science.
Zipei Geng completed his undergraduate education in 2019 with a Bachelor of Science in Statistics from Shandong University, alongside a dual Bachelor of Science with Honours in Statistics from The University of Manchester. He subsequently pursued a Master of Science in Statistics at ETH Zurich (Eidgenoessische Technische Hochschule Zuerich) from 2019 to 2022, where his studies concentrated on variable selection and high-dimensional statistics. Currently, he is pursuing his doctoral studies as a PhD candidate in Statistics at King Abdullah University of Science and Technology, working under the guidance of Prof. Marc G. Genton.
Hans Montcho is a Postdoctoral Fellow researcher in the Bayesian Deep Learning group. He holds a PhD in Statistics from KAUST and an MSc in Statistics from the Federal University of São Carlos and the University of São Paulo. Before that, he also worked as a data scientist for a global credit bureau.
His current research focuses on developing scalable probabilistic methods for complex statistical and machine-learning problems.
His research focuses on Bayesian computation, spatial statistics, latent Gaussian models, model assessment, uncertainty quantification and more recently on Bayesian deep learning.
Andrea Rocha is a research specialist in statistics at KAUST, working with the Stochastic Processes and Mathematical Statistics group. Before joining KAUST, she was an associate professor in the Department of Scientific Computing at the Federal University of Paraíba (UFPB), Brazil, where she also held various academic and coordination roles over nearly 15 years.
Her academic background includes a Ph.D. in Computational Mathematics, an M.Sc. in Statistics, and a B.Sc. in Statistics, all from the Federal University of Pernambuco (UFPE). Her doctoral and master’s work was supervised by Professor Andrei Toom.
Andrea’s research bridges theory and application in computational statistics, with a focus on probabilistic modeling, regression analysis, and stochastic processes. She has authored several publications in international journals and co-authored academic books on probability and random processes.
Andrea’s research lies at the intersection of computational mathematics, statistical inference, and applied probability. Her main areas of interest include:
She has contributed to the theory and application of statistical methods through both individual research and collaborations.
Damilya Saduakhas is a PhD candidate in Statistics at King Abdullah University of Science and Technology (KAUST) in the Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) division, working under the supervision of Professor David Bolin. She received her B.S. degree in Mathematics from Nazarbayev University, Kazakhstan, in 2020 and her M.S. degree in Statistics from KAUST in 2022. Her work on log-Gaussian Cox processes on metric graphs received the Best Paper Award at Spatial Statistics 2025 and the Best Poster Award at ISBA World Meeting 2024. During her time at KAUST, she served as president of the ASA Student Chapter and as Academic and Research Counselor on the Graduate Student Council.
Damilya's research focuses on spatial statistical methods based on stochastic partial differential equations, with applications to environmental and urban data.
Janet van Niekerk is a Research Scientist in the Bayesian Computational Statistics and Modeling group. She works on Bayesian methods including prior construction and computational framework development for various applications, with a focus on Biostatistics.
Efficient Bayesian methods for practical implementation, statistics for medical applications, complex survival analysis, INLA.
Junshu Jiang is a Ph.D. candidate in Statistics in the Stochastic Processes and Mathematical Statistics (StochProc) research group at King Abdullah University of Science and Technology (KAUST), supervised by Prof. David Bolin. He obtained a B.S. in Biological Sciences from the South China Agricultural University (SCAU) in 2018, and an M.S. in Computer Engineering (in Artificial Intelligence) from the KU Leuven (KUL) university in 2022.
Junshu's research focuses on statistical modeling of financial extremes, stochastic volatility modeling, and causal discovery in extreme environments.
