The AIM Colloquium is a series of talks offered by guests scientists from other universities, visiting the Chair of Artificial Intelligence Methodology and presenting cutting-edge research on methods and applications of artificial intelligence. These talks provide insights into exciting research questions and offer space for exchange and discussion.
This event will be hold in a hybrid format to enable flexibility and expand its reach beyond the university. The presentations are usually between 30 and 45 mins long and there's enough time for Q&A and discussions. You can find further information to the event below.
Note: Do not mistake the AIM colloquia with the AI Colloquia regularly organised by the RWTH AI Center, we highly and warmly recommend you to join!
If you would like to attend the talk in person or online, please send an email to our Research Network Coordinator, Maddy Ruppé by 25 February, 2024. Kindly note that our room capacities are limited and not all participants can join in person. Should we reach full capacity, we kindly invite you to join via the Zoom link we are happy to provide via email.
Automated machine learning (AutoML) was formed around the fundamental objectives of increasing efficiency in Machine Learning (ML) workflows, aiding the research of new ML algorithms, and contributing to the democratization of ML by making it accessible to a broader audience. Over the past decade, commendable achievements in AutoML have primarily focused on optimizing predictive performance. This focused progress, while substantial, raises questions about how well AutoML has met its broader, original goals. In my talk, I will argue that a key to unlocking AutoML's full potential lies in addressing the currently underexplored aspect of user interaction with AutoML systems. I envision a more human-centered approach in future AutoML research, promoting the collaborative design of ML systems that tightly integrates human expertise with AutoML methodologies. To this end, I will spotlight our recent research projects on human-centered AutoML.
