Martha Arbayani Zaidan obtained his Master’s degree in Control Systems in 2009 and his Ph.D. in Automatic Control and Systems Engineering in 2014, both from the University of Sheffield, UK. His doctoral study was conducted at Rolls-Royce University Technology Center for control and systems monitoring, supported by Rolls-Royce plc. He then worked as a Postdoctoral Research Associate (2014-2015) at Maryland University at College Park, US, and as a Postdoctoral Research Fellow (2015-2017) at Aalto University, Finland. From 2017, he has joined Institute for Atmospheric and Earth System Research (INAR) at University of Helsinki. He received the title of Docent in Atmospheric and Environmental Data Science in 2020. In between 2021 and mid-2022, Dr. Zaidan served INAR as a Research Associate Professor at Nanjing University, China. On July 2022, he has returned to University of Helsinki to work as a HIIT Research Fellow funded by HIIT and INAR. In his current role, he works on developing data science and machine learning methods for various applications in sensing systems, atmospheric and environmental sciences.
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, etc.), health monitoring technologies (e.g., feature extractions, diagnostics, prognostics), intelligent control systems and systems identification. The applications include various engineering and scientifical systems including bio-medical robotics, twin rotor dynamics, aircraft gas turbine engines, applied physics, geoinformatics, atmospheric and environmental sciences as well as other intelligent engineering systems.
In 2022, Dr. Zaidan publications are mainly in the topics of automating scientifical data analysis, intelligent sensor calibration and virtual sensors. In his current role, Dr. Zaidan also delivers lectures in the courses of Statistical Analysis for Environmental Field Measurements and Environmental Data Sciences. He has also participated actively in various research projects and thesis supervision for Ph.D. and summer intern students.
Sample of publications in 2022:
[1] M.A. Zaidan, N.H. Motlagh, P.L. Fung, A.S. Khalaf, Y. Matsumi, A. Ding, S. Tarkoma, T. Petäjä, M. Kulmala, and T. Hussein. Intelligent air pollution sensors calibration for extreme events and drifts monitoring. IEEE Transactions on Industrial Informatics, 19(2):1366–1379, 2023.
[2] M.A. Zaidan, Y. Xie, N.H. Motlagh, B. Wang, W. Nie, P. Nurmi, S. Tarkoma, T. Petäjä, A. Ding, and M. Kulmala. Dense air quality sensor networks: Validation, analysis, and benefits. IEEE Sensors Journal, 22(23):23507–23520, 2022.
[3] P. Su, J. Joutsensaari, L. Dada, M.A. Zaidan, T. Nieminen, X. Li, Y. Wu, S. Decesari, S. Tarkoma, T. Petäjä, M. Kulmala, and P. Pellikka. New particle formation event detection with Mask R-CNN. Atmospheric Chemistry and Physics, 22(2):1293–1309, 2022.