Kitiporn Plaimas is currently an Associate Professor in the Department of Mathematics and Computer Science at Chulalongkorn University, Thailand. She received her Dr. rer. nat. degree from the Faculty of Mathematics and Computer Science, Heidelberg University, Germany, in 2011. From 2006 to 2011, she was involved in bioinformatics projects at the Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ). Since then, she has been actively engaged in various bioinformatics and systems biology research endeavors. In 2021, she received the Outstanding Mid-career Researcher Award from the Faculty of Science, Chulalongkorn University. Her research focuses on developing innovative strategies rooted in mathematics and computer science to model and analyze cellular mechanisms—such as metabolic processes, signal transduction pathways, and protein-protein interaction networks—in both prokaryotic and eukaryotic organisms, including plants. Her computational approaches utilize graph-based algorithms, network analysis, machine learning, and mathematical modeling.
Kitiporn Plaimas is currently an Associate Professor in the Department of Mathematics and Computer Science at Chulalongkorn University, Thailand. She received her Dr. rer. nat. degree from the Faculty of Mathematics and Computer Science, Heidelberg University, Germany, in 2011. From 2006 to 2011, she was involved in bioinformatics projects at the Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ). Since then, she has been actively engaged in various bioinformatics and systems biology research endeavors. In 2021, she received the Outstanding Mid-career Researcher Award from the Faculty of Science, Chulalongkorn University. Her research focuses on developing innovative strategies rooted in mathematics and computer science to model and analyze cellular mechanisms—such as metabolic processes, signal transduction pathways, and protein-protein interaction networks—in both prokaryotic and eukaryotic organisms, including plants. Her computational approaches utilize graph-based algorithms, network analysis, machine learning, and mathematical modeling.