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Petri Myllymäki is a Director at Helsinki Institute for Information Technology (HIIT) , Computer Science Adjunct Professors , and Finnish Center for Artificial Intelligence (FCAI) within Aalto University's School of Science, Department of Computer Science . His research focuses on Bayesian networks, information discovery, and interactive intent modeling . Active in ACM Transactions on Information Systems and SIGIR Conference publications Recipient of 2016 CPHC/BCS Academy of Computing Distinguished Dissertation runner-up and 2017 Kone Foundation Rajapinta award Collaborated with institutions like Stockholm University and researchers including Giulio Jacucci and Samuel Kaski His 15 most recent publications (2025-2015) span decision-making under conflicting objectives, machine learning applications in social science, Bayesian network structure learning algorithms , and cognitive modeling for exploratory search . Key technical subfields include hashing techniques, two-dimensional representation, computational efficiency, and user experience optimization . Scientific awards : 2016 CPHC/BCS Academy of Computing Distinguished Dissertation runner-up 2017 Kone Foundation Rajapinta award As an information discovery expert , he has contributed to open-access research through Interactive Visualization and Scinet frameworks, impacting Sustainable Development Goals related to digital innovation and knowledge dissemination.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +3 more

Dr. Martha Arbayani Zaidan serves as an Academy Research Fellow at the University of Helsinki's Department of Computer Science and holds the prestigious Docent title (equivalent to Adjunct Professor) at the Institute for Atmospheric and Earth System Research (INAR) and Helsinki Institute of Sustainability Science (HELSUS). Her interdisciplinary work bridges computer science, atmospheric physics, and sustainability science to advance environmental sensing technologies. Her educational foundation includes a PhD in Automatic Control and Systems Engineering from the University of Sheffield (2014), where she developed Bayesian approaches for complex system prognostics, complemented by an MSc in Control Systems (2009) and BEng in Electrical Engineering (2007). Dr. Zaidan's research focuses on environmental sensing systems , particularly drone-based air quality monitoring and machine learning applications for atmospheric data analysis. She investigates critical phenomena including black carbon distribution in Asian megaregions, urban-suburban air quality disparities in East Africa, and aerosol size distribution modeling. Her work emphasizes practical applications for sustainability challenges through advanced computational methods. Her scientific contributions have earned significant recognition, including the competitive Academy Research Fellowship from Finland's Academy of Finland (2023) and the HIIT Research Fellowship (2022), reflecting her leadership in environmental AI research. As an active research leader, Dr. Zaidan directs the Academy of Finland project on AI-enhanced sensing technologies and participates in the large-scale InterEarth RESET initiative. She co-founded the annual EnvSys workshop series, fostering international collaboration in environmental sensing for smart cities. Her supervision record includes mentoring graduate students in computational environmental science.

• Environmental Sensing
• Air Quality Monitoring
• Atmospheric Science
• +4 more