Computer Science (2023)

Human-Centered AI is a proposed (three-day course) explores current HCI research related to interactive human-centered, AI-driven systems that provide utility in the tasks they perform, and issues related to acceptability by users. The course includes lectures, critical reflections on the assigned readings, hands-on exploration of examples, and discussion relating the topic to the PhD students’ research. 

Artificial Intelligence has experienced a tremendous increase in attention in recent years across all sectors in society ranging from health, transportation, finance, construction, entertainment among others. Taking an optimistic view, Ben Schneiderman envisions,” computing devices that dramatically amplify human abilities, empowering people and ensuring human control.” He proposes that, “Human-Centered AI (HCAI), enables people to see, think, create, and act in extraordinary ways, by combining potent user experiences with embedded AI support services that users want.” Taking departure in this view, we explore prevailing research and discuss the important issues related to how AI system predict and monitor and adapt to the user. Research themes include the staples of HCI such as task performance and usability/experience and HAI-specific concerns about transparency, explainability, predictability, user control, and ethical implications. 

HCI research is a fundamentally interdisciplinary field that is growing and rising to the challenges and opportunities with AI. In this course, participants will learn a concise history of the topic of Artificial Intelligence, understand the basic technical terms and techniques, and will gain an overview of broad topics of current human-centered AI research. Examples from autonomous transportation, voice interfaces, robotics, public information systems and others provide a deeper understanding of the state-of-the-art research and design of HAI. Students will learn how to critically examine HAI research articles identifying the strengths and weaknesses and possible future directions. Students will consider their current research and how themes of this course relate to their work.

Relational Database Management Systems (RDBMSs) have been applied in many different domains. Systems specialized for specific domains or use-cases can, however, achieve much better performance. This course provides deep insight into novel specialized data management systems such as time series management systems (TSMSs), graph databases, and feature stores. The course will present the specific challenges these kinds systems deal with followed by examples of concrete solutions and architectures.

Organizer:     Christian Thomsen

Lecturers:      Prof. Wolfgang Lehner, TU Dresden (to be confirmed)

ECTS:              2

Time:              Autumn 2023

Place:

Zip code:

City:

Number of seats:     25

Deadline:

Important information concerning PhD courses:

We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

The rapid development of Artificial Intelligence (AI) techniques benefits various real-world applications. However, current AI models are vulnerable due to attacks, privacy leakage and etc, which call for an urgent need to develop trustworthy AI techniques.   

The course aims at introducing recent advancements in the field of trustworthy AI from both algorithm and application perspective. In particular, the main tackled topics will be: (i) privacy, (ii) interpretability, (iii) fairness, (iv) robustness. In addition, there are some illustrations of the key opportunities and challenges for the future trustworthy AI.