"Increases in data and computational power mean Artificial Intelligence (AI) has considerable societal impact. Although impactful, the AI theory is extremely far from creating true intelligence. Thus, the question is not what AI will do to humans, but how humans will improve AI and what they will do with it?"

Decebal Mocanu is an Assistant Professor in Artificial Intelligence and Machine Learning within the Data Mining group, Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e) since September 2017, and a member of TU/e Young Academy of Engineering. His short-term research interest is to conceive scalable deep artificial neural network models and their corresponding learning algorithms using principles from network science, evolutionary computing, optimization and neuroscience. Such models shall have sparse and evolutionary connectivity, make use of previous knowledge, and have strong generalization capabilities to be able to learn, and to reason, using few examples in a continuous and adaptive manner.

Most science carried out throughout human evolution uses the traditional reductionism paradigm, which even if it is very successful, still has some limitations. Aristotle wrote in Metaphysics “The whole is more than the sum of its parts”. Inspired by this quote, in long term, Decebal would like to follow the alternative complex systems paradigm and study the synergy between artificial intelligence, neuroscience, and network science for the benefits of science and society.

In 2017, Decebal received his PhD in Artificial Intelligence and Network Science from TU/e. During his doctoral studies, Decebal undertook three research visits: the University of Pennsylvania (2014), Julius Maximilian University of Wurzburg (2015), and University of Texas, Austin (2016).

Prior to this, in 2013, he obtained his MSc in Artificial Intelligence from Maastricht University . During his master studies, Decebal also worked as a part time software developer at We Focus BV in Maastricht. In the last year of his master studies, he also worked as an intern at Philips Research in Eindhoven, where he prepared his internship and master thesis projects. Decebal obtained his Licensed Engineer degree from University Politehnica of Bucharest. While in Bucharest, between 2001 and 2010, Decebal started MDC Artdesign SRL (a software house specialized in web development), worked as a computer laboratory assistant at the University Nicolae Titulescu, and as a software engineer at Namedrive LLC.

• Department of Mathematics and Computer Science
• Data Science Center Eindhoven (DSC/e)

Partners in (semi-)industry 

• Philips
• TNO

PhD students:

• Anil Yaman
• Shiwei Liu
• Ghada Sokar
• Zahra Atashgahi

PDEng students supervision (graduated)

• Eleftherios Koulierakis, Detection of outbreak of infectious diseases : a data science perspective, July 2018

MSc students supervision (graduated)

• Joost Pieterse (cum laude), Evolving sparse neural networks using cosine similarity, July 2018
• Bram Linders (2nd supervisor), Prediction and reduction of MRP nervousness by parameterization from a cost perspective, February 2019
• Thomas Hagebols, Block-sparse evolutionary training using weight momentum evolution: training methods for hardware efficient sparse neural networks, March 2019

Conference tutorials

• D.C. Mocanu, E. Mocanu, P.H. Nguyen, M. Gibescu, Z. Vale, D. Ernst, Scalable Deep Learning: from theory to practice (T11), International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), Link, Materials
• D.C. Mocanu, E. Mocanu, Z. Vale, D. Ernst, Scalable Deep Learning: from theory to practice (T24), International Joint Conference on Artificial Intelligence (IJCAI 2019), Link, Materials
• E. Mocanu, D.C. Mocanu, Scalable Deep Learning: from theory to practice, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2019), Link, Materials