Project List

 This project aims to measure price and wage inflation expectations by modelling agents as being able to choose between different expectation rules based on their past performance. The study will be conducted by a group of two nuclear researchers (who specialize in macroeconomics and econometrics) and several consultants with positions in leading monetary policy institutions (who will aid with feedback and in the dissemination of the results).

Bayesian Structural Equation Modeling (BSEM) has gained significant attention due to its ability to resolve common issues found in frequentist approaches, such as nonconvergence, Heywood cases, sample size limitations, and inadmissible solutions. Furthermore, BSEM can estimate complex models that classical maximum likelihood methods often struggle with. A crucial component of BSEM is the incorporation of prior knowledge via prior distributions, which provides a unique advantage over frequentist methods by allowing previously known information to be transparently included in model specifications. Proper prior elicitation is essential for translating domain expertise into probability distributions, thus improving the accuracy and reliability of the model. Despite this, the development and widespread adoption of robust prior elicitation techniques in BSEM remain limited. This project aims to advance the field of BSEM through innovative computational methods and practical applications, with a focus on GPU processing and its potential to enhance the efficiency of Bayesian computations. By leveraging parallel computing capabilities provided by modern graphic processing units, the project seeks to significantly accelerate the computational processes involved in BSEM. Additionally, the project will explore the use of BSEM in developing psychometric instruments, employing techniques such as Markov Chain Monte Carlo (MCMC) using the No-U-Turn Sampler (NUTS) for latent model estimation. These innovations promise to offer deeper insights into measurement precision and enhance the validity of psychometric instruments across diverse populations. The expected outcomes of this project include improved computational efficiency, optimized model estimations, and broader adoption of Bayesian methods in applied research settings. By addressing both theoretical and practical challenges, this project aims to contribute to the broader use of BSEM in psychometric analysis.

  This project will develop and train an AI agent in identifying relevant patterns of conflict and affectivity during team interaction, both in text and video, and conveying appropriate real-time feedback. A set of three studies will test the role of the agent-led feedback on team dynamics and outcomes, such as performance, interpersonal affect regulation, engagement, as well as the role of the agent's features in its acceptance by the team.    

Despite decades of devoted research, cancer remains a tremendous health threat and societal burden. Europe's Beating Cancer Plan aims to improve the lives of more than 3 million people by 2030 by improving prevention, early detection, diagnostics, therapeutics, and quality of life. The biggest single hurdle here is the highly inadequate way cancer data, both from research and healthcare, are still being dealt with. While other areas of society (e.g. e-finance, e-commerce, logistics, travel, meteorology, etc.) have fully exploited advances in data and information technology to serve organisations as well as individual consumers, so far this has failed in the health domain. Consequently, cancer data are hard to Find, Access, make Interoperable and Reuse. Evidently this is not caused by lack of suitable technology, but rather by organisational, social and cultural causes. Inherently, solving the problem requires a cultural shift from the current craftsmanship approach to cancer research and data, to a drastic collaboration model at industrial scale. CANDLE therefore aims to scale-up and improve existing (inter)national health data infrastructures, align maximally with national EHDS implementations in member states, including HDAB’s, DAAMS’s and SPE’s. CANDLE will also identify and resolve potential barriers (https://www.health-ri.nl/en/participation/obstacles-removal-trajectory ) that jeopardize effective implementation of UNCAN.eu and ECPDC digital platforms. CANDLE aims to equipe data users and NCDN developers with a ‘ready-to-use' CANDLE Resource Kit in a process oriented (research journey, patient journey, data life cycle) way. In summary, CANDLE will provide an avenue towards a successful and highly desired data transformation in European cancer research and serve as a catalyzer for the UNCAN.eu and ECPDC platforms by advancing the development of NCDNs to reach the goal of the Cancer Mission and Europe’s Beating Cancer Plan, i.e. reducing the burden of cancer.

The already established consortium of Aston University (UK), University College London (UK), Ruralis University (Norway), University of Turin (Italy), and University Institute of Lisbon (Portugal) will seek funding from the EU's ‘100 Climate-Neutral and Smart Cities by 2030’ initiative. Our proposal includes developing a scalable tool to evaluate and guide urban mobility policies, supporting sustainable development and the 15-minute city concept, especially post-COVID-19. This tool will analyze mobility's complex nature, integrating environmental sustainability and regional urban system characteristics like geography and demographics. AIenhanced, it will assess impacts on affordability and accessibility, providing localized insights. We seek pumpprimingfunds for two residential workshops at Aston University to solidify collaboration, complete application writing, and establish a proof of concept