Andrea Cremaschi

Dr. Andrea Cremaschi is a Bayesian statistician by training. He obtained his doctoral degree in Statistics from the University of Kent (UK), with a thesis on novel modelling and computational strategies for high-frequency trading data. In the years that followed, he joined two Biostatistics groups in Norway and Singapore, where he worked in inter-disciplinary teams on biomedical applications. Through his work experiences, Dr. Cremaschi has developed a strong interest in the application of Bayesian Statistics to different types of problems, developing novel modelling strategies, as well as algorithmic solutions, devised to offer deeper insights in the investigation of modern problems.

Academic Experience

• Assistant Professor, IE University, 2024 – Present 

• Senior Scientist, Singapore Institute for Clinical Sciences (SICS), A*STAR, Singapore, 2023 - 2024 

• Assistant Professor (Adjunct), Department of Paediatrics, National University of Singapore (NUS), Singapore, 2023 - 2024 

• Senior Research Fellow, Singapore Institute for Clinical Sciences (SICS), A*STAR, Singapore, 2020 - 2023 

• Post-Doctoral Fellow, Yale-NUS College, Singapore, 2019 - 2020 

• Post-Doctoral Fellow, Department of Cancer Immunology, Institute of Cancer Research, Oslo University Hospital, Oslo (Norway) and Oslo Centre for Biostatistics and Epidemiology (OCBE), University of Oslo, Oslo (Norway), 2016 - 2019 

Academic Background

• Ph.D. in Statistics, School of Mathematics, Statistics and Actuarial Science (SMSAS), University  of Kent, Canterbury, Kent (UK)

• Master Degree in Mathematical Engineering, Politecnico di Milano, Milan (Italy)

• Bachelor Degree in Mathematical Engineering, Politecnico di Milano, Milan (Italy)

Selected Publications

• Cremaschi, A., De Iorio, M., Kothandaraman, N., Yap, F., Tint M.T., Eriksson, J. (2023) “Joint modelling of association networks and longitudinal biomarkers: an application to child obesity”. Statistics in Medicine, 34(6):1135-1152 

• Cremaschi, A., Argiento, R., De Iorio, M., Shirong, C., Chong, Y.S., Meaney, M.J., Kee M.Z. (2022) “Seemingly Unrelated Multi-State processes: a Bayesian semiparametric approach”. Bayesian Analysis, 18(3):753-775

• Molinari M, Cremaschi A, De Iorio, M., Chaturvedi, N., Hughes, A.D., Tillin ,T. (2022) “Bayesian nonparametric modelling of multiple graphs with an application to ethnic metabolic differences”. Journal of the Royal Statistical Society, Series C: Applied Statistics, 71(5):1181-1204