Cristian Martín is an Associate Professor at the University of Málaga (UMA), and he is part of the ERTIS research group and the ITIS Software. Cristian Martín obtained a Ph.D. in Computer Science in 2018 at UMA, with an extraordinary Ph.D. thesis award. His research interests are focused on the IoT, machine-learning applied, digital twins, as well as paradigms such as Fog and Edge Computing. Previously he has been working in several technology companies on RFID technology and software development. He has several papers at international conferences and multiple publications in JCR-indexed journals, of which two of them have more than 800 citations, one received the best paper award in JNCA in 2017, and another publication was selected as Spring 2022 Editor Choice paper in FGCS. His research is highly cited, with more than 3000 citations on Google Scholar. He is the coordinating the European MSCA Staff Exchange project EVOLVE and Principal Investigator of the Spanish project DiTas (Proyectos de Generación de Conocimiento 2022 call). He has also participated in more than 20 research projects and contracts with companies. He has carried out four international stays, one at the University of Ghent, Belgium (2016, pre-doctoral), two at the IHP research institute, Frankfurt Oder, Germany (2020-2021, post-doctoral), and the last one in Incheon, Korea (2022). He is the author of the Kafka-ML framework and one of the authors of OpenTwins. He is co-supervising 3 doctoral theses, and has supervised more than 20 bachelor/master theses.
PhD in Computer Science, 2018
University of Málaga
M.S. in Software Engineering and Artificial Intelligence, 2015
University of Málaga
M.S. in Computer Science, 2014
University of Málaga
Result: Incremental learning with data streams in Kafka-ML. August 2022.
Result: Kafka-ML applied to structural health monitoring. June-August 2021.
Result: Kafka-ML: a framework for the management of AI/ML applications through data streams. January-June 2020.
Result: Appdaptivity: a framework for the development of IoT applications. September-December 2016.
For doctoral theses defended during the academic year 2018-2019 in Computer Science.