Special Topics in Information

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 sustainability, sustainability in/through computing, fighting climate misinformation and climate anxiety, as well as how knowledge and tools from the fields of machine learning, human-computer interaction, remote sensing, speculative design, etc., 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.

As such, this course is all about making connections: bridging the interpretive traditions of literary and cultural studies — which guide our critical engagement with texts and cultural artifacts — with computational methods, ranging from featurized classifiers to large language models. It pursues two complementary ends: students will develop interpretive strategies and critical vocabularies for cultural analysis, and, through hands-on practice grounded in engagement and experience with the text, they will learn to represent cultural data — whether text, image, audio, or video — and train machine learning models as algorithmic measuring devices to systematically characterize cultural phenomena of interest.

This class welcomes a range of inclinations: maybe you know how to implement an RNN or Transformer from scratch but are curious whether those models can be used to study literature, culture, or something beyond positive or negative sentiments. Or, maybe you’ve experimented with off-the-shelf topic models or word embeddings to explore humanities questions and want to see how far you can take them.

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.

This course provides a foundation in the principles, architectures, and applications of generative artificial intelligence (GenAI). It explores how large language models (LLMs) work by guiding students through the complete model lifecycle: from core building blocks like embedding representations, attention mechanisms, and transformer architectures to adaptation techniques including in-context learning, instruction tuning, parameter-efficient fine-tuning (PEFT), and reinforcement learning from human feedback (RLHF). 

The course also explores advanced topics such as retrieval-augmented generation (RAG), agentic reasoning systems, and multimodal learning that extends beyond text to incorporate vision and other data modalities. 

Programming assignments, research paper readings, and a team-based final project will help students develop the technical proficiency to design, implement, and evaluate GenAI pipelines using Hugging Face, LangChain, and other open-source LLM APIs, while critically assessing their capabilities, limitations, and responsible use in real-world applications.

The Global Rights Innovation Lab Clinic (GRIL) offers students a unique opportunity to integrate digital technologies into legal advocacy. Our particular field of application is human rights, but this approach is broadly transferable to other areas of law and client-facing work in the fields of data science, public policy, and other social sciences.

GRIL students utilize data-driven and technological advancements for groundbreaking legal advocacy strategies. Serving organizational clients — grassroots organizations, national and international public interest and human rights groups, GRIL provides advocacy support and strategies to forge new pathways to address human rights challenges. GRIL clients want to harness data analysis, data science, and visualization to advance human rights investigations, litigation before national and international courts, or social justice policy advocacy.

The Global Rights Innovation Lab Clinic (GRIL) offers students a unique opportunity to integrate digital technologies into legal advocacy. Our particular field of application is human rights, but this approach is broadly transferable to other areas of law and client-facing work in the fields of data science, public policy, and other social sciences.

GRIL students utilize data-driven and technological advancements for groundbreaking legal advocacy strategies. Serving organizational clients — grassroots organizations, national and international public interest and human rights groups, GRIL provides advocacy support and strategies to forge new pathways to address human rights challenges. GRIL clients want to harness data analysis, data science, and visualization to advance human rights investigations, litigation before national and international courts, or social justice policy advocacy.

The Global Rights Innovation Lab Clinic (GRIL) offers students a unique opportunity to integrate digital technologies into legal advocacy. Our particular field of application is human rights, but this approach is broadly transferable to other areas of law and client-facing work in the fields of data science, public policy, and other social sciences.

GRIL students utilize data-driven and technological advancements for groundbreaking legal advocacy strategies. Serving organizational clients — grassroots organizations, national and international public interest and human rights groups, GRIL provides advocacy support and strategies to forge new pathways to address human rights challenges. GRIL clients want to harness data analysis, data science, and visualization to advance human rights investigations, litigation before national and international courts, or social justice policy advocacy.

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).

This course will run as a design studio where students design new products as a way to explore the principles, processes, and outcomes of interaction design. Each week, students will complete both in-class and take-home projects that apply concepts and frameworks to real problem spaces. Class sessions will simulate professional design practice through weekly critiques, peer feedback, and design reviews. Students will be expected to present their work every week, engage actively in discussion, and refine their designs based on input.

Assignments will range from rapid, low-fidelity exercises to more developed interaction prototypes, giving students practice across the spectrum of design methods. The course culminates in a final project during the last weeks, where students bring together the skills, approaches, and insights developed throughout the semester. By the end of the course, students will have built a portfolio project and gained experience working within the collaborative, iterative culture of interaction design.

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.

In this hands-on workshop, students will strengthen their skills in strategic thinking, product design and research, leadership, and cross-functional collaboration. Each week we’ll explore real-world challenges drawn from students’ ongoing projects, which may range from team and organizational dynamics to early start-up ideas or capstone projects. Through discussions, peer feedback, and faculty-led readings and research reviews, students will engage with the latest developments in the field, and gain practical hands-on experience as leaders.

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.

This course is designed to help graduate students understand the various roles technical and data professionals play in policy development and enforcement primarily in the public and civil society sectors. The course is a mixture of independent work with outside experts, invited talks from tech and data experts from government and civil society organizations, and readings.

This course examines how generative artificial intelligence (AI) is reshaping how products are designed, and how students can evolve their UX practice to thrive in this rapidly changing landscape. They will learn to critically evaluate AI tools to identify their value in UX workflows, articulate informed perspectives on emerging debates in UX for AI (e.g., how AI is redefining design and product roles), and make thoughtful decisions about when to delegate tasks to AI versus keeping them human-led.

Learning activities include hands-on use and structured evaluation of leading AI-enabled design tools (e.g., Figma Make, Lovable, V0), analysis and discussion of current academic and industry literature (e.g., how generative AI challenges the foundations of human-centered design), and conversations with industry experts building AI products. The course emphasizes cultivating an intentional exploration mindset — learning by researching, building, testing, and reflecting — to align emerging AI capabilities with human-centered design values.