Online Algorithms with Predictions
Our focus is on improving optimization algorithms in online decision-making. Using techniques from online algorithms for solving optimization problems, we can provide worst-case guarantees, but normal (averagecase) behavior may not be satisfactory. Using techniques from machine learning, we can often provide good behavior in practice, but guarantees are lacking, and in particular missing for situations not captured by the training data. We aim at combining the best features from these two areas.
Cyber-Physical Systems with Humans in the Loop
Constructing cyber-physical systems with humans in the loop enables new applications like bio-computing, active learning systems, and intelligent medical systems. These applications allow humans and machines to collaborate on real-world tasks, integrating aspects of both Cyber-Physical Systems (CPS) and Socio-Technical Systems (STS). They feature close cooperation between software technologies, focusing on situational awareness, safety, privacy, usability, and easy error handling.
DeCoRe: Tools and Methods for the Design and Coordination of Reactive Hybrid Systems
A recurring problem of digitalised industries is to design and coordinate hybrid systems that include IoT (Internet of Things), edge, and cloud solutions. Currently adopted methods and tools are not effective to this end, because they rely too much on informal specifications that are manually written and interpreted by humans.
Initiatives to improve recruitment and retention of IT students
Denmark needs more IT specialists. But how do we get more young people to study computer science and become IT specialist? This project, consisting of two subprojects, focuses on initiatives that can improve both recruitment and retention of a larger but also more diverse group of young people e.g., female students and students without prior programming experience.
Algorithms education via animation videos
Several highly popular YouTube channels for mathematics and other scientific content (e.g., 3blue1brown, Numberphile, Veritasium) with millions of views indicate that learners may respond very positively to professionally produced educational videos. This project aims at creating and evaluating an initial library of such videos to supplement teaching in algorithms.
Software Infrastructures for Teaching at Scale
To teach many recent topics within digital technology at scale requires proper software infrastructures to support the teaching for lab exercises and projects. Some of these topics are data-driven systems, AI and cloud computing. Commercial providers are offering cloud computing and AI resources, however, in many situations these are ill fit for teaching activities as they are complex for early learners, are problematic due to GDPR, make teaching material obsolete by rapidly changing their UIs and when scaled add a significant cost.
Learning Technology for Improving Teaching Quality at Scale
Teaching quality and student feedback is negatively impacted by lack of teachers and many students. There is a need to consider how learning technologies can help improve teaching quality and student feedback both in physical and digital learning environments.
Supporting Diversity via inclusive Teaching/Learning Activities
The mix of students in digital technology is low in diversity (e.g. female students). This is a problem on a societal level which also impacts the study environment.
EXPLAIN-ME: Learning to Collaborate via Explainable AI in Medical Education
Together with clinicians, this project aims to develop explanatory AI that can help medical staff make qualified decisions by taking the role as a mentor who provides feedback and advice for the clinicians. It is important that the explainable AI provides good explanations that are easy to understand and utilize during the medical staff’s workflow.