Dr Omid Chatrabgoun is an Assistant Professor in Data Science and AI. He received PhD in Data Science from Shahid Chamran University (Iran) in 2015.  After graduation, he started working at Malayer University, Iran. He continued learning about analysing data and mostly focused on (Baysiean) Graphical Models-based supervised and unsupervised learning.  Also, he started working on machine learning and deep learning techniques. During his academic career, he has taught some courses relating to his expertise at undergraduate and postgraduate levels, including Statistical learning, Data science, and Machine learning. His primary research is in Modelling, Optimization and Uncertainty Quantification (UQ) in Data Science and AI using (Deep) Gaussian process for complex data. He is currently developing a novel approach for Machine learning of linear operational equations to model uncertainty quantification for highly complex systems using Gaussian processes. He has applied these methods to a wide range of applications in various multidisciplinary projects including construction of Gene Regulatory Networks (GRNs).

Dr Omid Chatrabgoun is co-author of many research outputs, published as peer-reviewed papers, book chapters and conference presentations. He has experience of gaining research grants and has been involved in consultancy and knowledge exchange projects with a range of research and industrial centres including Iran National Science Foundation (INSF) and Research Institute for Grapes and Raisin (RIGR).

Modelling, Optimization and Uncertainty Quantification in Machine learning Techniques and AI.
– Bayesian Machine Learning Techniques in Health and Biology, including Constructing Gene Regularity Networks (GRNs), Protein-Protein Interactions (PPIs) and Drug-Drug Interactions (DDIs).
– Non-parametric Bayesian Inference, mostly with (Deep) Gaussian Process.
– Modelling High-Dimensional and Complex Data using Gaussian and Non-Gaussian Graphical Models, including Bayesian networks, Dynamic Bayesian Networks, and Pair-copula Bayesian Models.