Fundamentals of People-Oriented Computing is a new teaching format that replaced the "Master Basismodule".

It is part of the People-Oriented Computing(POC) study direction, comprising the topics offered by IFI that focus on the relationship between humans and technology. POC deals with the human-centered design of information technology. User-friendliness, security, and cultural and ethical aspects are of core importance. You create interactive systems and visualize information.

Several professors will offer topics to choose from. The topic I'm offering is "Human-Centered Interactive Visual Data Analysis"

The Fundamentals of POC is designed to:

• Provide a deep dive into a POC topic that excites you
• Give you experience in reading and analyzing scientific literature
• Give you the chance to gain familiarity with a professor or research group in POC
• Complement other lectures and teaching formats with a self-directed module

Basic format of topic is reading scientific literature and individual oral examination

• Reading is typically 250-300 pages, depending on format
• Exam is 30 minutes
• Course grade is determined by the exam grade

This topic investigates the human-centered component in data-centric research and practice. We will study concepts, methodologies, and techniques focusing on the Human-Centered AI, Human-Centered Machine Learning, and Human-Centered Data Analysis in particular.

• Human-Centered Interactive Visual Data Analysis (J. Bernard)
• Human-Computer Interaction for Mental Health (E. Huang)
• Sustainable Human-Computer Interaction (E. Huang)
• Smart and Automated Homes: Practices and Technologies (E. Huang)
• Gender Bias in Online Platforms (A. Hannak)
• Automation in News Recommendation (A. Hannak)
• Efficiency of Human-AI Teams (A. Hannak)
• Interactive Visualization Design (C. Wacharamanotham)
• Machine Learning and Optimization in Interactive Interfaces (C. Wacharamanotham)
• Advanced Real-Time Lighting & Shading (R. Pajarola)
• Mathematical Principles for Scientific Computing and Visualization (R. Pajarola)
• Dimensionality Reduction and Clustering Techniques (R. Pajarola)
• Scientific, Bio-Medical 3D Visualization (R. Pajarola)