Who am I?
Martha Arbayani Zaidan, Ph.D., Docent, is a Research Scientist, specializing in Artificial Intelligence & Machine Learning for atmospheric and environmental Sciences as well as systems engineering and applied physics applications. He is a Senior Member of the Institute of Electrical and Electronics Engineers (SMIEEE) and a Docent at the University of Helsinki. Currently, he serves as an Academy Research Fellow and a Data Scientist at Department of Computer Science (CS) and Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, Finland. He is also leading the group of "Intelligent enviRonmental mOnitoring and aNalytics" (IRON) hosted at CS and INAR.
He devotes his research to developing data mining and machine learning methods for improving sensing technologies, automating data analysis, and improving understanding in the fields of atmospheric and environmental sciences, air pollution, sensing technologies, renewable energies, engineering systems, and related disciplines. He has acquired research funding of more than € 1.5 Million, given by Research Council of Finland, European Commission Joint Research Centre - European Partnership on Metrology (EURAMET), Helsinki Institute for Information Technology (HIIT), Engineering and Physical Sciences Research Council (EPSRC), and Rolls-Royce plc.
A brief story of my professional life
Dr. Zaidan was born in March 1986, in Banda Aceh, Indonesia. In 2007, he received an S.T. (equivalent to a B.Eng. degree) with Cumlaude in Electrical Engineering from Trisakti University (Indonesia). He holds an MSc (Eng.) degree with Distinction in Control Systems in 2009 from the Department of Automatic Control & Systems Engineering (ACSE), the University of Sheffield (UK). He earned his Ph.D. 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 the 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, Muscat (Oman), where he taught courses on control systems engineering and engineering dynamics.
In the autumn of 2015, he received Aalto Science Fellowship where he worked as a Postdoctoral Research Fellow at Aalto Science Institute (AScI) and Centre of Excellence in Computational Nanoscience (COMP), Aalto University (Finland). During two years of his employment at Aalto University, he focused his research on machine learning strategies for intelligent health monitoring and applied physics (environmental and material sciences).
From August 2017 to August 2020, he 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. For his contributions, in September 2020, he was granted the title of Docent in Atmospheric and Environmental Data Sciences from the University of Helsinki, Finland.
From January 2021 to June 2022, he worked as a Research Associate Professor at the Joint International Research Laboratory of Atmospheric & Earth System Sciences, School of Atmospheric Sciences, Nanjing University (China). While working at Nanjing University, he has continued his service as a Researcher at INAR, University of Helsinki. In July 2022, he returned to Finland to act as a HIIT Research Fellow, working at the Department of Computer Science (CS) and INAR at the University of Helsinki. From September 2023, he has become an Academy Research Fellow and a Data Scientist at the same institutions (CS and INAR).
Dr. Zaidan's 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-organizing 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 and environmental sciences as well as other intelligent engineering systems.