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Department of Informatics Interactive Visual Data Analysis Group

Fundamentals of People Oriented Computing

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.

Title Fundamentals of People-Oriented Computing
Specialization People-Oriented Computing
Term Fall 2021
IVDA Topic Human-Centered Interactive Visual Data Analysis

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

About the Course

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

Our Topic: Human-Centered Interactive Visual Data Analysis


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.

All Topics

  • 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)