一、报告题目：Neural-like P systems: From theory to applications
二、报 告 人：David Orellana-Martín (Spain)
四、报告地点：6A301
五、主办单位： 西华大学金沙集团888881
六、报告人简介：
Dr. David Orellana Martín received his B.Sc. degree in 2014, his M.Sc. degree in 2016, and his Ph.D. degree in 2019 at the University of Seville. He received the Best Ph.D. thesis of 2019 Award from the International Membrane Computing Society. He was an ERCIM fellow at NTNU (Norway) from 2019 to 2021, a postdoctoral fellow at the University of Seville from 2021 to 2023, and currently, he is an assistant professor at the University of Seville. His main research interests are computational complexity theory, unconventional computing (especially membrane computing), and machine learning and high-performance computing. He is a member of the Website Committee of the International Membrane Computing Society, apart from being part of the Program Committee of some international conferences and part of the Editorial Board of a JCR indexed journal.
七、报告内容简介：
In the framework of membrane computing, the search for new models that capture elements from nature adapting them as ingredients for these devices and acquiring knowledge about the power of these ingredients. On the one hand, theoretical research is based on two principles: demonstrating the computational power of the model, looking for universal machines capable of computing as many functions as a Turing machine, and finding frontiers of efficiency, proving that some models are only able to solve in an efficient way tractable problems, while adding some ingredients lead to efficient solutions of NP-complete problems; On the other hand, neural-like P systems have been widely used for several types of applications, ranging from optimization of complex systems to classification tasks, passing by medical image processing and fault diagnosis in power systems, among others. In this talk, both theoretical and experimental topics will be overviewed, concluding with some interesting open research lines.
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