Fred Godtliebsen. Fred works in the Department of Mathematics and Statistics in the Statistics group, and is in addition an associate member of the Machine Learning Group, especially on health related research from a statistical viewpoint.




Associated faculty:

Susan Wei: Her research lies at the intersection of biostatistics and computer science and focuses on tackling the demands of modern data analysis.  Her development of statistical methodology brings together techniques for machine learning, high dimensional low sample size asymptotics, and empirical processes to tackle the challenges presented by modern data types.





Giovanni Sebastiani: His research interest are stochastic algorithms, Bayesian statistics, inverse problems, image analysis, biomedicine and magnetic resonance.






Phuong Ngo. Phuong is an expert on otimal and robust controllers for nonlinear dynamical systems using machine learning techniques. He has a PhD from Purdue University.





Jonas Nordhaug Myhre. Jonas is interested in policy gradients and methods related to continuous problems in reinforcement learning. He has a PhD in machine learning from UiT – The Arctic University of Norway. During his PhD he worked on the intersection between manifold learning and kernel methods.





Ilkka Launonen. Ilkka works on Bayesian methods for reinforcement learning.





PhD students:

Thomas Johansen. Thomas is an associated PhD student. His main project is melanoma detection using hyperspectral imaging.





Miguel Angel Tejedor Hernandez.  Miguel is a full time PhD student with the project.​ He has experience in the use of machine learning algorithms in medical applications using hyperspectral images. His current research interests are focusing on reinforcement learning and simulating the Glucose Insulin model.





Ashenafi Zebene Woldaregay.  Ashenafi is an associated PhD student, currently working at the department of Computer Science. His main project is the development of an early infectious disease outbreak detection system (EDMON) based on self-recorded data from people with type-1 diabetes.




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