graduate and undergraduate courses

This graduate-level course delves into human-centered design for foundation models in AI, covering a range of topics including bias, fairness, explainability, and human-AI collaboration. It emphasizes the importance of data management, privacy, cultural values, and the development of transparent and trustworthy AI systems.

This course offers an extensive overview of user experience design and research, focusing on human-computer interaction and interaction design. It covers interaction theories, design principles, prototyping, interface evaluation, and user interface implementation, along with qualitative and quantitative research methods for user-centered data analysis, highlighting their strengths and limitations.

In this course, students learn to apply design thinking and UX research methods to develop intuitive and user-centric technological solutions, gaining practical skills in prototype creation and user feedback analysis.

In this course, students learn qualitative UX research methods including heuristic evaluations, interviews, focus groups, observational field studies, and thematic analysis to understand user needs and behaviors for informed design strategies. 

This course provides a comprehensive introduction to digital technology, covering hardware, software, programming, the internet, web development, cybersecurity, and databases. It aims to equip students with foundational knowledge and skills in computing systems through lectures, lab exercises, and project work.

The course provides a deep dive into the essence of interaction design, concentrating on understanding how design choices affect user experience across cognitive, affective, perceptual, physiological, environmental, and social dimensions. It encourages students to critically evaluate these aspects, emphasizing the impact of interaction design decisions.

In this course, students engage deeply with quantitative UX research methods, focusing on essential metrics for summative assessment, including survey methodologies, A/B testing, usability metrics, and web analytics. A significant emphasis is placed on mastering statistical tests such as Chi-square and ANOVA, along with comprehensive coverage of experimental design principles, spanning both parametric and non-parametric approaches. 

This course encompasses a comprehensive understanding of interaction design's concepts, theories, and principles, along with an exploration of various psychological aspects such as perception, cognition, and user behavior. It includes an analysis of human information processing constraints and limitations, and teaches the application of visual design principles and cognitive ergonomics to optimize interaction design.