Computer Science and Engineering (2025)

Welcome to Human Centered Artificial Intelligence

Description: Artificial Intelligence has experienced a tremendous increase in attention in recent years across all sectors, including health, transportation, finance, construction, and entertainment. In the realm of software engineering, integrating AI in a human-centered manner is crucial for ensuring that these technologies enhance human capabilities and maintain ethical integrity. Ben Shneiderman 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."

This three-day PhD course, inspired by the Copenhagen Manifesto, explores the intersection of AI and software engineering, focusing on human-centric design principles and ethical considerations. Participants will focus into the core values of human-centered AI, such as responsibility, ethics, transparency, equity, inclusivity, continuous learning, and environmental sustainability.

The course is structured to provide an interactive and comprehensive learning experience, utilizing Liberating Structures to facilitate active engagement and collaboration. Participants will:

Understand the foundational principles of human-centered AI and their application in software engineering.

Explore strategies for designing AI systems that prioritize human needs, transparency, and equity.

Develop practical skills in implementing and evaluating human-centered AI technologies.

Reflect on the ethical implications and societal impact of AI in software engineering.

By the end of the course, participants will have a robust understanding of how to integrate human-centered principles into their work, ensuring that AI technologies serve the common good and promote human wellbeing.

Learning objectives: Understand Foundational Principles of Human-Centered AI: Grasp the core concepts and principles of human-centered AI, including responsibility, ethics, transparency, and equity.

Comprehend how these principles are applied in the context of software engineering and AI development.

Develop Skills in Human-Centered AI Design:

Learn and apply strategies for designing AI systems that prioritize human needs, autonomy, and user experience.

Gain practical skills in implementing and evaluating AI technologies that enhance human capabilities while ensuring ethical integrity.

Evaluate Ethical Implications of AI:

Critically assess the ethical challenges and societal impacts associated with AI technologies.

Develop frameworks and guidelines for ethical AI development, with a focus on minimizing harm and promoting fairness and inclusivity.

Promote Transparency and Equity in AI Systems:

Understand the importance of transparency in AI development and learn methods for implementing it effectively.

Explore strategies to ensure equity in AI systems, particularly in terms of access, fairness, and bias mitigation.

Foster Inclusivity and Continuous Learning in AI Projects:

Cultivate an understanding of the importance of inclusivity and continuous learning in AI development teams.

Develop strategies to support diverse and inclusive environments within AI projects and organizations.

Incorporate Environmental Sustainability into AI Development:

Identify practices that hinder environmental sustainability in AI development.

Learn and apply strategies to reduce the environmental impact of AI technologies, ensuring sustainable innovation.

Engage in Collaborative Learning and Knowledge Sharing:

Participate in interactive workshops using Liberating Structures to enhance collaboration and idea generation.

Contribute to group discussions and activities that reinforce the application of human-centered AI principles in real-world scenarios.

Organizer: Daniel Russo

Lecturers: Daniel Russo

ECTS: 3 ECTS

Time: 27, 28, 29 October 2025

Place: Aalborg University

Zip code: 9220

City: Aalborg

Maximal number of participants: 20

Deadline: 6 October 2025

Important information concerning PhD courses: 

There is 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 the 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 of the course.

We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.

Welcome to Urban Data Management, Representation and Mining

Description: Urban computing is an emerging field that aims to address the challenges of rapid urbanization, such as pollution, energy consumption, and traffic congestion. Urban computing involves acquiring, integrating, and analyzing large and heterogeneous data generated by a variety of sources in urban spaces, including sensors, devices, vehicles, buildings, and people, to tackle major urban challenges. 

The course will introduce 

1) urban data management, including urban data, spatial data indexing, spatial data query processing, and learned spatial indexes; 

2) urban data mining, including spatial data mining, spatiotemporal prediction, and reinforcement learning; 

3) geospatial entity representation for point objects, trajectories, and regions and their applications, including speed inference, region population estimation, etc.; 

4) foundation models for geospatial applications.

Prerequisites: Bachelor’s and master’s degrees in computer science or software engineering, including knowledge on machine learning and data management as introduced in typical undergraduate courses.

Learning objectives: The objective of the course is to provide students with a working understanding of basic knowledge, as well as research problems and solutions of urban computing.

Organizer: Christian S. Jensen

Lecturers: Professor Gao Cong, Nanyang Technological University, Singapore

ECTS: 2 ECTS

Time: June 19 - 20, 2025

Place: Aalborg University

Zip code: 9220

City: Aalborg

Maximal number of participants: 15

Deadline: May 29, 2025

Important information concerning PhD courses: 

There is 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 the 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 of the course.

We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.

Welcome to Quantum Computing

Description: The course will provide an introduction to quantum computing (e.g. qubits, measurements, quantum circuits etc.) and classic quantum algorithms such as Grover's quantum search algorithm. Afterwards, the course will demonstrate how classic computer science approaches can be used in the field of quantum computing. 

Prerequisites: Basic linear algebra

Learning objectives: Quantum basics, quantum circuits, quantum search, simulation and verification of quantum circuits.

Organizer: Max Tschaikowski

Important information concerning PhD courses: 

There is 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 the 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 of the course.

We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.