Decision Support Through Intelligent System Feedback for Maritime

Dr. Brendan Sullivan, Assistant Professor, University of Twente, Netherlands, 21 de abril de 2023

Abstract:

The maritime industry is a complex and dynamic field that requires constant adaptation and change to remain competitive and efficient. To facilitate this, decision support systems (DSS) have been developed to aid in the decision-making process. The DSS presented utilizes model-based engineering techniques to create dynamic models that can be adjusted and optimized as needed according to real-time data from a maritime vessel.

Bio:

Brendan Sullivan is an Assistant Professor at the University of Twente in the Netherlands. As part of the manufacturing systems and systems engineering research unit his research focuses on digitalization, value-based decision analysis, and advancing changeable and resilient system theories/methods through collaborative research with industry and government. He holds a PhD in Industrial Engineering from Politecnico di Milano, where he conducted research on the use of changeable engineering systems to improve system resilience and extended value. Dr. Sullivan’s work has been published in a number of relevant publications, including the Journal of Systems Engineering, the International Journal of Digital Engineering, and the Proceedings of the International Conference on Systems Engineering.

Computing High Energy Physics

Dr. Vladimir Relovic, Member of the team that discovered the Higgs boson with the di-photon decay channel, 11 de abril de 2023

Abstract:

Dr. Vladimir will summarise the history of computing in High energy physics and present challenges posed by the upgrade program for the Large Hadron Collider (LHC). The increase in the complexity of future particle-collision events together with solutions for increased data collection rate will be presented using the example of the Trigger and Data Acquisition System of the Phase II upgrade of the CMS detector at CERN. 

Bio:

Ph.D. in Physics,  University of New Mexico (2007).  Member of the particle physics experiment CDF on Tevatron at Fermi National Laboratory (IL, USA), where he searched for supersymmetry. From 2007 working on the CMS experiment on the LHC accelerator at CERN. Member of the team that discovered the Higgs boson with the di-photon decay channel.  Served as  coordinator of the High Level Trigger and the Level-1 Trigger groups in CMS. Since 2022, the Team Leader of the Vinca Institute Group in CMS. 

Generative models for graphs - challenges and responses

Prof. Dr. Magda Gregorová, Head of Center for Artificial Intelligence and Robotics, Technical University of Applied Sciences Würzburg-Schweinfurt, Germany, 31 de mazo de 2023

Abstract:

Deep generative models aim at learning the probability distributions of high-dimensional data. Having learned the distributions, the models can sample from these, producing new realistic data examples. Great progress have been achieved on domains such as images, videos or language. The task of graph generation poses several important challenges due to the discreteness of the  data structures and complex internal relationships. 

Bio:

Magda Gregorova comes from Prague, Czech Republic, where she obtained her Master‘s degree in Statistics (2001) from the University of Economics. She started her career of an applied statistician in the Czech National Bank, where she headed a technical unit on financial statistics and collaborated closely with the ECB and the IMF. After several years in banking she has decided to follow an international career and joined Eurocontrol, the European Organization for the Safety of Air Navigation based in Brussels, Belgium, as a statistical analyst and forecaster of European air traffic. She then moved to Geneva, Switzerland, where she obtained in 2018 a PhD in machine learning from the Computer Science Department of the University of Geneva. She continued as a post-doc in the Data Mining and Machine Learning group of the University of Applied Sciences of Western Switzerland.