Radu-Casian Mihailescu

Dr Radu Mihailescu is an Associate Professor at the School of Mathematical and Computer Sciences in Heriot-Watt University Dubai (HWUD) since 2022. He holds a PhD in Artificial intelligence from King Juan Carlos University, in Madrid, Spain and prior to joining HW, he held the position of Associate Professor at Malmö University in Sweden, where he also acted as Program Director for the Applied Data Science Master’s Degree program and being affiliated with the Internet of Things and People Research Center. He has participated in over 10 national and international European research projects in collaboration with key industry partners and government agencies, in various capacities including as Principal Investigator, published over 50 academic papers and has been serving as a program committee member and reviewing in a number of international scientific conferences, journals and workshops. He has extensive academic and teaching experience at both undergraduate, postgraduate and PhD level with special focus in the area of Artificial Intelligence, Machine Learning, Deep Learning and Data Science. He has provided supervision and mentorship to doctoral graduates and postdoctoral fellows, supported by industry and government funding.

His research is centered primarily on advances in the field of machine learning (ML), leveraging state-of-the-art deep learning architectures, with a particular emphasis on Artificial General Intelligence (AGI), a domain aimed at creating computer systems with the ability to perform any intellectual task at human level and beyond. Key areas of his research include topics such as developing efficient learning schemes via out-of-distribution generalization, domain adaptation, meta-learning, active learning, interactive learning or few-shot learning, as well as focus on machine understanding, via building upon distributed representations learned by deep networks and incorporating reasoning as an integral part of the learning procedure, along with explainable AI (XAI), and addressing fairness and bias in AI. Moreover, he is also active in numerous applications of ML approaches to various real-world use-case such as computer vision, multi-modal large language models (LLMs), natural language processing for fake news detection, activity recognition based on the internet of things infrastructures, context-adaptive surveillance systems or ambulance coordination for acute stroke care.