Education
Teaching Assistant
IOB4-T3 Machine Learning for Design
2023-2024Machine learning (ML) is a computational approach that aims at “giving computers the ability to learn without being explicitly programmed” (A. Samuel, 1959). To meaningfully envision and design future iPSSs that is beneficial and useful to people and society, designers must engage with the details of how ML systems “see” the world, “reason” about it, and interact with it; experience the quirks, biases, and failures of ML technology; contend with how agency, initiative, trust, and explainability mediate the interaction between human and iPSSs; and understand how functionalities enabled by ML can be designed in iPSSs. Students in this course will gain practical experience with ML technology and learn how to think critically of what ML systems can do, and how they could and should be integrated into iPSSs.
CS4145 Crowd Computing
2022-2023Crowd Computing is an emerging field that sits at the intersection of computer science and data science. Crowd computing studies how large groups of people can solve complex tasks that are currently beyond the capabilities of artificial intelligence algorithms, and that cannot be solved by a single person alone. It involves algorithmically engagement and coordination of people by means of Web-enabled platforms. These complex tasks are mainly focused on the creation, enrichment, and interpretation of data, making crowd computing a building block of data science.