Special Topics in Information

In this class students will continue research projects from INFO 217A. HCI research. The class includes weekly one-on-one meetings with each project team. Students will read literature related to their project assigned by the instructor and continue their projects. The final deliverable for the class will be a full conference or journal paper.

This course on generative AI blends the theoretical foundations of LLMs with hands-on applications. Topics include transformer architectures, prompt engineering, API integration, and retrieval augmented generation (RAG). The course emphasizes practical skills, including working with open-source models, fine-tuning LLMs, implementing graph-based enhancements and using Agentic technologies to build applications. Ethical considerations are integrated throughout, with a focused module on bias assessment and mitigation strategies. By the course’s end, participants will possess a robust toolkit for leveraging LLMs for application development.

Behavioral science applies psychological insights to economic and mathematical theories of decision making. In this class, we will discuss the recent theoretical and empirical advances made in the field. Being thoughtful about the role of psychology can lead to a greater understanding of how economic agents make decisions, how the economy works, and how policy decisions will affect people. This course will utilize lectures, discussion, group interactions, and problem sets to provide an introduction to the topic of behavioral science. The primary goal of the class is to provide knowledge about the field and begin to explore how the topic might impact policy decisions in various contexts.

Public policy and civic organizations are increasingly guided by data products such as empirical graphs, statistical analyses, and machine learning predictions. However, no data product can deliver an absolute, unimpeachable truth. Therefore, building these products requires navigating a delicate balance between a) moving forward with identified issues and b) refining and improving the product to address issues. This class will help students develop their intuition for striking that balance through hands-on experience.

Peoples and communities around the world will be confronting the challenges of climate change, ecosystem degradation, and biodiversity loss for many decades to come. This course will explore the different ways in which the informatics and computing field can contribute to our individual and collective efforts to mitigate and adapt to the effects of climate change.

Through readings and discussions, students will critically engage with foundational and leading-edge perspectives on diverse topics such as systems thinking for sustainable computing, sustainability in/through design, collapse informatics, fighting climate misinformation and climate anxiety, as well as how knowledge and tools from the fields of machine learning, human-computer interaction, web3, IoT, and remote sensing are being applied to novel solutions in many different settings.

Student-led projects will research the information needs and information seeking behaviors of individuals and communities, both now and into the future, and design information tools and resources to support them in their efforts of climate mitigation, adaptation, advocacy, and education.

While often defined as “the computational study of culture”, cultural analytics might be best understood as a radical interdisciplinary experiment, one that seeks to understand cultures — socialties, histories, cognition, the literary — through empirical models and patterns, built on effective computational representations of relevant cultural constructs. This experiment calls for a unique skill set: one needs to be familiar with approaches in the interpretive humanities and computer/information science; one also needs to cultivate an interdisciplinary mindset: recognize and appreciate the affordances and limitations of both qualitative and quantitative traditions. This class is imagined as a possible point of departure for those who are so inclined.

Students will learn how to identify opportunities for meaningful product and design innovation in our increasingly complex, interconnected technology landscape. We will apply frameworks and toolkits from systems theory, strategic foresight practice, and speculative design to develop, prototype, and test ideas. Through course projects, students will practice methods such as systems mapping, scenario planning, horizon scanning, diegetic prototyping, and anticipatory ethnography. Students will learn how to translate insights from these methods into a coherent vision and strategy, connect this work to a product roadmap, and effectively communicate this end-to-end process in a case study. Guest lecturers will be invited to share perspectives on how they apply the frameworks and toolkits from class in industry settings. By the end of the course, students will be well-equipped to apply systems and futures thinking within an organization to inform strategy, and build human-centered systems that benefit individuals and society. This course can support students interested in a range of cross-functional industry roles, including design, product management, and engineering.

The Future of Cybersecurity Working Group (FCWG) assembles students, researchers, and faculty from across the campus with a shared interest in security. We read and discuss the current cybersecurity scholarship and workshop projects related to cybersecurity. Our goal is to support critical inquiry into security and explore how it relates to political science, law, economics, the military, and intelligence gathering. Students are required to participate in weekly sessions, present short papers on the readings, and write response pieces.

This course will explore what HCI knowledge and methods can bring to the study, design, and evaluation of AI systems with a particular emphasis on the human, social, and ethical impact of those systems. Students will read papers and engage in discussions around the three main components of a human-centered design process as it relates to an AI system:

1. needs assessment,
2. design and development, and
3. evaluation.

Following these three main design phases, students will learn what needs assessment might look like for designing AI systems, how those systems might be prototyped, and what HCI methods for real-world evaluation can teach us about evaluating AI systems in their context of use. The course will also discuss challenges that are unique to AI systems, such as understanding and communicating technical capabilities and recognizing and recovering from errors.

Guest lectures will be given by experts in AI ethics (e.g., Timnit Gebru) and fairness, accountability, and transparency in AI systems (e.g., Motahhare Eslami).

For this course, we are going to tackle one of the world’s biggest challenges (voted on by the students). We will organize as an innovation lab tasked with developing new products and so as to better understand the principles, process, and outputs of interaction design. The goal will be to be able to apply the concepts and frameworks we cover in class to a real problem space.

Students will be responsible for developing a robust prototype over the final few weeks of the course. They will also write a reflection on the prototype development process, drawing on the theoretical concepts covered in the course. On the last day of class, students will present their work to a panel of industry experts for feedback.

This course explores the theory and practice of building knowledge graphs from unstructured text, equipping students to develop AI-powered systems that can reason with structured information. The course progresses from foundational concepts in knowledge representation and language models to advanced techniques in entity extraction, knowledge graph construction, and semantic web technologies. Students will learn to enhance applications with graph-based retrieval augmented generation (GraphRAG), create effective visualizations, and fine-tune models for domain-specific knowledge needs. Designed to be accessible for those with minimal coding experience, the curriculum emphasizes hands-on learning through Python labs, guided projects with real-world datasets, and a culminating final project that demonstrates practical application. Students will develop a critical understanding of interpretability and ethical considerations in knowledge-intensive AI systems.

Playful design and gamification are methods which use elements from video games (such as levels, achievements, or avatars) to make real-world applications more appealing. They are used in business, education, and even in physiological training and rehabilitation. While playful design primarily emulates the “look and feel”, gamification also applies some of the game mechanics. In the seminar, we will look at best practices in both areas. We will examine the models on which the current solutions are based. Psychological and cognitive aspects (e.g., motivation, task load) will also be discussed. In the second half of the seminar, we will create our own (visual) prototype of a gamified application in groups of 2-4 persons.

Karl Marx wrote, “the philosophers have hitherto only interpreted the world in various ways. The point, however, is to change it.” This discussion-based seminar examines varied political philosophies and the roles they suggest for the potentially transformational work of design, development, governance of built systems. We begin with an examination of liberal democratic capitalism and the imaginaries of technology, communication, and governance that align with it. We then explore design, deployment, and governance practices aligned with visions of economic democracy, direct democracy, socialism, Afrofuturism, Zapatismo, and Native American sovereignties. This is a discussion based seminar with the option of a research paper or project proposal as a final project.

This course brings students together as a product team to apply data science and analytics skills to nonprofit and academic research projects. Students gain hands-on experience working with real-world data, using both foundational and advanced techniques — such as machine learning, data engineering, and online experiments — to generate actionable insights and solutions.

How do you create a concise and compelling User Experience portfolio? Applying the principles of effective storytelling to make a complex project quickly comprehensible is key. Your portfolio case studies should articulate the initial problem, synopsize the design process, explain the key decisions that moved the project forward, and highlight why the solution was appropriate. This course will include talks by several UX hiring managers who will discuss what they look for in portfolios and common mistakes to avoid.

Students should come to the course with a completed project to use as the basis for their case study; they will finish with a completed case study and repeatable process. Although this class focuses on UX, students from related fields who are expected to share examples and outcomes of past projects during the interview process (data science, product management, etc.) are welcome to join.