Martha Arbayani Bin Zaidan, B.Eng., M.Sc.(Eng.), Ph.D., MIET. was born in 1986, in Banda Aceh, Indonesia. In 2007, he received a BEng degree (with Cumlaude) in Electrical Engineering from Trisakti University, Indonesia. He holds MSc (Eng.) degree (with Distinction) in Control Systems in 2009 from Department of Automatic Control & Systems Engineering (ACSE), The University of Sheffield, UK. He earned his PhD as a Dorothy Hodgkin Postgraduate Award (DHPA) scholar in early 2014 from Rolls-Royce University Technology Centre for control and monitoring systems, at the University of Sheffield, UK, where his research was supported by aerospace industries, such as Rolls-Royce plc and Controls Data Services. His research focused on advanced Bayesian approaches for aerospace gas turbine engine prognostics.
Soon after that, he joined Centre of Advanced Life Cycle Engineering (CALCE) at University of Maryland, College Park, USA to work as a Postdoctoral Research Associate on Prognostics and Health Monitoring (PHM). On the spring 2015, he spent one semester as a Visiting Faculty at Sultan Qaboos University, Oman, where he taught courses on control systems engineering and engineering dynamics.
On the autumn 2015, he received Aalto Science Fellowship where he worked as a Postdoctoral Research Fellow in Aalto Science Institute (AScI) and Centre of Excellence in Computational Nanoscience (COMP), Aalto University, Finland. During two years his employment at Aalto University, he focused his research on machine learning strategies for intelligent health monitoring and applied physics (environmental and material sciences).
On August 2017, he has joined the Institute for Atmospheric and Earth System Research (INAR), previously known as division of atmospheric sciences and the Centre of Excellence in Atmospheric Science - from molecular and biological processes to the global climate, University of Helsinki, Finland, where he works as a senior Postdoctoral Researcher. Currently, he devotes his research on developing data mining and machine learning methods for automating data analysis and improving the understanding in the fields of atmospheric sciences, air pollution and related disciplines.
His general research interests are in applied artificial Intelligence and machine learning (e.g. fuzzy logic, artificial neural networks and deep learning, Bayesian techniques, Recursive Bayesian estimations, Kernel machines, sampling methods, inference approximations, self-organising maps), Health Monitoring Technologies (e.g. feature extractions, diagnostics, prognostics), Intelligent Control Systems, Systems Identification and various applications including bio-medical robotics, twin rotor dynamics, aircraft gas turbine engines, applied physics, atmospheric sciences and other intelligent engineering systems.