Personal Information

Stalin Munoz Gutierrez
Mtro.
Tel.
+43 316 873 - 5491

Stalin Munoz Gutierrez

Since 1999 I’ve been involved in several research projects spanning symbolic and sub-symbolic artificial intelligence techniques to applied engineering and biology problems. I hold a computing engineering degree from UNAM, Mexico, and a master’s degree in complexity sciences from UACM, Mexico. I am currently pursuing a Doctorate degree at the Institute of Software Technology, TU Graz. For my doctoral studies, I work on execution monitoring for cognitive robotics within the project “Dependable Internet of Things in Adverse Environments”. My research interests include Artificial Intelligence, Computational Biology, Complexity Sciences, and Robotics.

Publications

Stan Muñoz Gutiérrez, Adil Mukhtar and Franz Wotawa Data-Driven Diagnosis of Electrified Vehicles: Results from a Structured Literature Review
Stan Muñoz Gutiérrez, Adil Mukhtar and Franz Wotawa Transformer-Based Signal Inference for Electrified Vehicle Powertrains
Ledio Jahaj, Stalin Munoz Gutierrez, Thomas Walter Rosmarin, Franz Wotawa and Gerald Steinbauer-Wagner A Model-based diagnosis integrated architecture for dependable autonomous robots 34th International Workshop on Principles of Diagnosis (DX’23)
Markus Tranninger, Stalin Munoz Gutierrez, Gerald Strommer, Richard Halatschek and Gerald Steinbauer-Wagner Model-Based Diagnosis for Autonomous Robots
Stalin Munoz Gutierrez and Gerald Steinbauer-Wagner The Need for a Meta-Architecture for Robot Autonomy Proceedings of the Second Workshop on Agents and Robots for Reliable Engineered Autonomy 81-97
Jesus Savage, Stalin Munoz Gutierrez, Luis Contreras, Mauricio Matamoros, Marco Negrete, Carlos Rivera, Gerald Steinbauer, Oscar Fuentes and Hiroyuki Okada Generating Reactive Robots' Behaviors using Genetic Algorithms ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence 698-707
Marco De Bortoli, Stalin Munoz Gutierrez and Gerald Steinbauer-Wagner Diagnosis of hidden faults in the RCLL 32nd International Workshop on Principle of Diagnosis
Hyobin Kim, Stalin Muñoz, Pamela Osuna and Carlos Gershenson Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-Class Classification with a Convolutional Neural Network
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