Connectivity Fund

Julian F. Schumann – TU Delft PhD student For humans to trust autonomous vehicles, they need to have confidence in the vehicles’ ability to reliably resolve space-sharing conflicts with other traffic participants in a safe manner – such as in the case of crossing or merging paths. Planning safe and efficient interactions for autonomous vehicles […]

Giovanni Franzese – TU Delft PhD candidate The adaptability of robot manipulators to many different tasks is currently constrained by systematic hard coding of each specific task. Recent machine learning methods like Learning from Demonstrations (LfD) and Reinforcement Learning (RL) have shown promising results in having fast reprogramming of the task using human demonstrations or

Jeroen G. Rook – University of Twente PhD candidate Comparing algorithms is a non-trivial task. Often, a set of representative problem instances are used to compare algorithms. However, these problem instances introduce biases in the comparison outcomes, which is often not taken into account. The confidence of the comparison can be strengthened by using statistical

Aiming towards combating the challenges faced by fake video detection, the prime objective of this research is to develop a proactive, advanced explainable, human collaborated AI-based online disinformation detecting tool for securing a trustworthy social media environment.

Nieves Montes PhD Student at Artificial Intelligence Research Institute (IIIA-CSIC) This research visit will focus on models for empathetic software agents. This means embedding autonomous agents with the ability to model their peers and understand the reasons behind their behaviour. This work is to enhance the performace of agents in cooperative tasks, where they need

Brian Logan Associate Professor at Utrecht University Many activities that are easy for humans, such as walking together with other humans, are hard to program as a set of rules for Artificial Intelligence (AI) robots. This is why machine learning is so popular in AI: correct behaviour can be learned by trial and error. However,

Marcos Matabuena University of Santiago de Compostela In this project, in collaboration with Gábor Lugosi (UPF), we propose new uncertainty quantification methods based on the design of new Conformal Inference strategies for complex data that arise in modern personalized medicine applications. The new uncertainty methods can examine the reliability and safety of results obtained with

Manfred Jaeger Associated Professor at Aalborg University From Social networks to bibliographic databases: many important real-world phenomena consist of networks of connected entities. The mathematical model of such networks is that of a graph, which in its basic form just consists of a collection of nodes that are connected by edges. To model the rich

Antonio Carta Post-doc at Pisa University Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt and generalize continually, in an efficient, effective and scalable way appears to be fundamental for a more sustainable development of Artificial Intelligent systems. However, access

Roberto Maria Rosati PhD Student in Information Engineering at University of Udine Recently, matheuristics have emerged as a promising research branch in combinatorial optimization. Thanks to this collaboration supported by TAILOR connectivity fund, we will design and apply novel matheuristic techniques to a variety of timetabling problems that are under investigation at University of Udine.