Biopic

Who am I?

Martha Arbayani Zaidan, Ph.D., Docent, is a Research Scientist specializing in Artificial Intelligence (AI) and Machine Learning (ML) for atmospheric and environmental sciences, as well as systems engineering and applied physics. He is a Senior Member of the IEEE (SMIEEE) and a Docent at the University of Helsinki. Currently, he serves as an Academy Research Fellow and Data Scientist at both the Department of Computer Science (CS) and the Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, Finland.

Dr. Zaidan leads the "Intelligent enviRonmental mOnitoring and aNalytics" (IRON) group, hosted at CS and INAR, focusing on cutting-edge AI solutions for environmental monitoring and analytics as well as other engineering systems.

His research is dedicated to advancing data mining and machine learning methods to enhance sensing technologies, automate data analysis, and improve insights in atmospheric and environmental sciences, air quality, renewable energy systems, engineering systems, and related fields. To date, he has secured over €2.23 million in research funding from prestigious bodies such as the Research Council of Finland, Business Finland, the European Commission Joint Research Centre (EURAMET), the Helsinki Institute for Information Technology (HIIT), the 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. He graduated with an S.T. (equivalent to a B.Eng.) in Electrical Engineering, with Cum Laude honors, from Trisakti University (Indonesia) in 2007. He went on to earn an MSc (Eng.) with Distinction in Control Systems in 2009 from the Department of Automatic Control & Systems Engineering (ACSE) at the University of Sheffield (UK). In early 2014, he completed his Ph.D. as a Dorothy Hodgkin Postgraduate Award (DHPA) scholar at the Rolls-Royce University Technology Centre, University of Sheffield, where his research, supported by aerospace industries such as Rolls-Royce plc and Controls Data Services, focused on advanced Bayesian approaches for gas turbine engine prognostics.

Following his doctorate, Dr. Zaidan joined the Center for Centre of Advanced Life Cycle Engineering (CALCE) at the University of Maryland, College Park (USA) as a Postdoctoral Research Associate working on Prognostics and Health Monitoring (PHM). In spring 2015, he was appointed Visiting Faculty at Sultan Qaboos University, Muscat (Oman), where he taught courses on control systems engineering and engineering dynamics.

In autumn 2015, he was awarded the Aalto Science Fellowship and joined the Aalto Science Institute (AScI) and the Centre of Excellence in Computational Nanoscience (COMP) at Aalto University (Finland) as a Postdoctoral Research Fellow. During this period, his research focused on machine learning strategies for intelligent health monitoring and applied physics (environmental and material sciences).

From August 2017 to August 2020, Dr. Zaidan served as a Senior Postdoctoral Researcher at the Institute for Atmospheric and Earth System Research (INAR) at the University of Helsinki (formerly known as the Division of Atmospheric Sciences and Centre of Excellence in Atmospheric Science). In recognition of his contributions, he was awarded the title of Docent in Atmospheric and Environmental Data Sciences by the University of Helsinki in September 2020.

Between January 2021 and June 2022, Dr. Zaidan held the position of Research Associate Professor at the Joint International Research Laboratory of Atmospheric & Earth System Sciences, School of Atmospheric Sciences, Nanjing University (China), while maintaining his affiliation with INAR. In July 2022, he returned to Finland as a HIIT Research Fellow, working at both CS and INAR, University of Helsinki. Since September 2023, he has been serving as an Academy Research Fellow and Data Scientist at the same institutions.

Research Interests    

Dr. Zaidan’s research interests lie at the intersection of applied artificial intelligence and machine learning, covering areas such as fuzzy logic, artificial neural networks, deep learning, Bayesian methods, recursive Bayesian estimation, kernel machines, sampling techniques, and inference approximations. He is also active in the development of health monitoring technologies, including feature extraction, diagnostics, and prognostics.

His expertise further extends to intelligent control systems, system identification, and a wide range of applications including biomedical robotics, twin-rotor dynamics, aerospace gas turbine engines, applied physics, and atmospheric and environmental sciences, as well as other intelligent engineering systems.