Kategorie: ‘Courses’
Empathic Computing Lab
Empathic computing investigates how technology can support collaboration, shared understanding and human-centered interaction. In this lab, student teams design and develop their own projects using immersive and pervasive media—ranging from XR headsets and smartphones to tablets, laptops, and wearable devices. Projects may leverage open standards like WebXR, or use engines such as Unity, depending on the goals and platforms chosen. The focus is on creating meaningful, responsive experiences through thoughtful interaction design and emerging technologies.
Mixed Reality Lab
Mixed Reality combines virtual, augmented, and extended reality into a continuum of spatial computing experiences on devices such as the Meta Quest 3, Microsoft HoloLens 2, HTC Vive Pro, and Android smartphones. In this practical Bachelor lab, students in teams of 3-4 design and implement their own mixed reality application using modern toolchains (e.g., Unity and OpenXR). Participants learn key MR interaction patterns, input and UX design, and deployment to target devices. Registration for the lab is handled via SuPra; late registrants may join a waiting list.
Data Ecosystems Lab
Modern web applications centralize data, creating isolated silos, whereas decentralized data ecosystems enable users to store and manage their own data, granting true data sovereignty. In this lab, small teams will tackle distinct challenges, such as ingesting wearable sensor streams, processing fitness metrics, or sharing activity logs with friends, to build components of a fully decentralized fitness‑tracking app that leverages the Solid framework for secure, user‑owned data exchange.
Empathic Computing
The lecture provides a systematic overview of the field of empathic computing at the interface of human-computer interaction, extended reality, affective computing, and virtual worlds. The focus is on how interactive systems can be designed to enable people to better understand and empathize with the perspectives, experiences, thought processes, and emotions of others, especially in distributed and immersive collaboration scenarios.
Bulding Large Language Model Applications Lab SS25
Large Language Models (LLMs), such as GPT, Claude and Llama, are powerful tools that have transformed the landscape of Natural Language Processing (NLP), enabling advanced applications in various fields. The “Building Large Language Model Applications,” course is a practical, hands-on course designed to provide students with in-depth knowledge and experience in developing applications utilizing LLMs. Throughout the course, students will work on real-world projects and learn how to design, implement, and deploy advanced LLMs systems.
Building Large Language Model Applications Lab WS25/26
Large Language Models (LLMs), such as GPT, Claude and Llama, are powerful tools that have transformed the landscape of Natural Language Processing (NLP), enabling advanced applications in various fields. The “Building Large Language Model Applications,” course is a practical, hands-on course designed to provide students with in-depth knowledge and experience in developing applications utilizing LLMs. Throughout the course, students will work on real-world projects and learn how to design, implement, and deploy advanced LLMs systems.
Data Science in Medicine WS25/26
Health data analytics is one of the main drivers for the future of medicine. Various sources of big data, including patient records, diagnostic images, genomic data, wearable sensors, are being generated in our everyday life by health care practitioners, researchers, and patients themselves. Data science aims to identify patterns, discovering the underlying cause of diseases and well being by analyzing this data.
Data Science in Medicine SS25
Health data analytics is one of the main drivers for the future of medicine. Various sources of big data, including patient records, diagnostic images, genomic data, wearable sensors, are being generated in our everyday life by health care practitioners, researchers, and patients themselves. Data science aims to identify patterns, discovering the underlying cause of diseases and well being by analyzing this data.
Knowledge Graphs Seminar SS25
Knowledge Graphs are large graphs used to capture information about the real world in such a way that is is useful for applications. In these data structures, there are all sorts of entities (for example, people, events, places, organizations, etc.). Knowledge Graphs are used by many organizations to represent the information they need for their operations. The most well-known example is Google, where a knowledge graph is used to enrich the search results. Also personal assistants, such as Amazon’s Alexa, Apple’s Siri and Google Now, as well as question answering systems such as IBM Watson, make use of knowledge graphs to provide information to their users.
Besides these, also other information graphs, are in use by large organizations to improve or personalize their services. Examples include the Facebook graph, the Amazon product graph, and the Thompson Reuters Knowledge Graph.
The graph also contains all sorts of information about these entities (e.g., age, opening hours, …) and relations between them (e.g., “this shop is located in Aachen”). Furthermore, it may contain context information (e.g., the source of some information) and schema information or background knowledge (e.g., “shops have opening hours”).
Deliverables of this seminar
This seminar consists of an introductory course on Knowledge Graphs. You will give a short outline presentation on your assigned topic to set overview and expectations about the paper you’re going to write. The main deliverable of the seminar is a paper that describes the state of the art of your assigned topic. While you do not need to contribute original research, your task is to show the scientific competences of literature research, presentation of a research question and understanding and putting relevant papers into context. Furthermore, you are asked to critically assess and compare strengths or challenges of existing solutions. You will review your peer’s papers and give relevant feedback to enhance your scientific writing skills. You will present your paper in a final presentation in a block seminar at the end of the semester.
Privacy Enhancing Technologies for Data Science
This lecture covers current research results in the area of Privacy Enhancing Technologies (PETs) which can be applied to Data Science. These PETs have the potential to enable a new generation of privacy-enabled services which are not focused on maximizing the collection of user data. We use a mix of recent book chapters and papers from conferences and journals of the last few years as primary source material.