Prof Maia Angelova Turkedjieva

I am a Professor of Data Analytics and Machine Learning at the School of Information Technology at Deakin University from January 2017. I was a Professor of Mathematical Physics at the Department of Mathematics, Physics and Electrical Engineering at Northumbria University at Newcastle upon Tyne from 2004 to 1996. Before that I was a reder in Applied Mathematics from 2001 to 2004 and Senior and Prinvipal Lecturer in Mathematics at Northumbria University from 1996 to 2001. I worked in Oxford University from 1991 to 1996 as a College Lecturer in Physics at Somerville College and a member of Theoretical Physics Sub-Department. 

Professor of Data Analytics and Machine Learning - School of IT, Deakin University 2017 - to date

Visiting Professor - National University of Mexico, 2015 - to date

Honorary Professor - Amity School of Engineering and Technology - 2018 to date 

Professor of Mathematical Physics - Department of Mathematics, Physics and Electrical Engineering, Northumbria University 1997-2016

Lecturer in Physics - Somerville College, Oxford University 1991-1996

Assistant Professor in Physics - Faculty of Physics, Sofia University 1988-1991

Visiting Research Fellow, Departmnent of Chemistry, University of Kent at Canterbury  1988 -1991

Data analytics, machine learning and data mining, biomathematics, bioinformatics, mathematical physics, group theory, financial mathematics, condensed matter physics, mathematical crystallography.

Fellow of The Institute of Physics
Member of The London Mathematical Society

Member of The Australian Mathematical Society

Member of The International Union of Crystallography

member of The European Women in Mathematics

2015-2019: Loss of Complexity of human physiological signals with ageing and degenerative disease, funded by The Academy of Medical Sciences. 

2011-2014: Models for Ageing and Technological Solutions for Improving and Enhancing the Quality of Life (MATSIQEL), I was the Coordinator and principal investigator of the European research project funded by FP7-People-IRSES programme;