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This course investigates the ethical, legal, and policy foundations of contemporary information technology. Through lectures, readings, discussions, and short assignments, we will address contemporary challenges ranging from the contests over intellectual property and privacy in a networked world to questions of inequality and control over technology. We will cover key areas of technology law and policy such as computing ethics; intellectual property; competition, antitrust, and freedom of expression; privacy and security; and AI ethics. We will also address new ethical questions and controversies that law and policy has yet to sort out. Through this course you'll learn about the key frameworks, processes, and institutions that govern the contemporary world of information technology, along with key theories and methods from academic fields that shape and inform them (law, philosophy, economics, political science, communication, sociology, etc.). You will also learn core reading and analytic skills central to success in the worlds of social science, law, policy, and many other settings. But above all you'll learn to engage critically and strategically with the worlds of information and technology around you, deciding what kind of information consumer, user, producer, and citizen you want to be.

Computing requires difficult choices that can have serious implications for real people. This course covers a range of ethical, societal, and policy implications of computing and information. It draws on recent developments in digital technology and their impact on society, situating these in the context of fundamental principles from computing, policy, ethics, and the social sciences. A particular emphasis will be placed on large areas in which advances in computing have consistently raised societal challenges: privacy of individual data; fairness in algorithmic decision-making; dissemination of online content; and accountability in the design of computing systems. As this is an area in which the pace of technological development raises new challenges on a regular basis, the broader goal of the course is to enable students to develop their own analyses of new situations as they emerge at the interface of computing and societal interests.

This course is designed to introduce students to the conceptual, design, and technical aspects of developing front-end accessible websites. No prior knowledge of programming, or web design is assumed nor necessary. In the course, we will cover basic web technologies such as HyperText Markup Language (HTML), Cascading StyleSheets (CSS), and some JavaScript (JS). We will also cover theories and principles that will help you become a better UX designer. This includes information, visual, and interaction design principles; usability and user testing principles and processes.

This course provides an introduction to data science using the statistical programming language R. We focus on building skills in inferential thinking and computational thinking, guided by the practical questions we seek to answer from data sets arising in medicine, economics and other social sciences. The course starts with essential R programming principles, and how to use R for data manipulation, visualization, and sampling. These techniques are then used to summarize and visualize real data sets, draw meaningful conclusions from those data, and assess the uncertainty surrounding those conclusions. Throughout the process, students will learn to develop hypotheses about their data, and use simulations and statistical techniques to test these hypotheses. The course also covers how to use the Tidyverse open-source R packages to clean and organize complex data sets, and create high quality graphics for data visualization.

Independent work in information science as part of a student-led team project. Intended for first-year students on project teams, project team members in training, and for project team members performing tasks that are not core to information science.

This interdisciplinary course examines network structures and how they matter in everyday life. The course examines how each of the computing, economic, sociological and natural worlds are connected and how the structure of these connections affects each of these worlds. Tools of graph theory and game theory are taught and then used to analyze networks. Topics covered include the web, the small world phenomenon, markets, neural networks, contagion, search and the evolution of networks.

In this project-based course, students are introduced to the conceptual, design, and technical aspects of developing accessible server-side rendered websites. Students will learn how to respond to HTTP requests, dynamically render HTML, populate and store web content in a relational database using SQL, process web forms, and securely implement login/logout sessions through server-side programming. Through a succession of homeworks and projects, students learn and practice how to apply these principles to the creation of interactive and data-driven websites. Modern best practices are emphasized, including the use of version control, development containers, and generative AI.

This course introduces students to the conceptual, design, and technical aspects of authoring client-side interactive web applications. Students will use the JavaScript programming language to author dynamic client-side interactive and accessible components, implement REST APIs, query data from a document database, and asynchronously request and render API data client-side. Through a succession of homeworks and projects, students will learn and practice how to apply these principles to the creation of client-side rendered websites. Modern best practices are emphasized, including the use of version control, development containers, and generative AI.

Introduces students to the Communication and Information Technologies focus area of the communication department and the Human Systems track for information science. It examines several approaches to understanding technology and its role in human behavior and society. Topics include psychological aspects of computer-mediated communication; how design plays a role in the way we interface with technology and collaborate with each other; and the ways in which communication technology is situated inside social and institutional structures and cultural formations.

Balancing environmental, economic, and societal needs for a sustainable future encompasses problems of unprecedented size and complexity. Computing and information science can play an important role in addressing critical sustainability challenges faced by present and future generations. The goal of the course is to introduce students to a range of sustainability challenges and to computational methods that can help address such challenges. Sustainability topics include sustainable development, biodiversity and wildlife conservation, poverty mitigation, food security, renewable resources, energy, transportation, and climate change. In the context of these sustainability topics, the course will introduce students to mathematical and computational modeling techniques, algorithms, and statistical methods. The course is at the introductory undergraduate level. Students are expected to have basic knowledge of probability theory and calculus.

Provides an introduction to the role computing and information technologies played in political public life, from tabulating machines used to calculate the census to Big Tech's impact on democratic procedures, the future of labor, and the environment. Though organized around four thematic units (Recognizing and Representing, Knowing, Working, and Belonging), the course pays attention to the chronological trajectory of technologies and political practices and students will develop the skills necessary for historical analysis. While focusing on the US experience the course also highlights the international flow of labor, materials, and ideas. By studying the development of computing historically, we will grapple with the effects of computing and data sciences on society today, paying special attention to critiques of economic, racial, and gender injustice. The course will meet twice a week, and each meeting will include a lecture followed by a discussion.

INFO 2950 is an applied introductory course on the foundations of data science, focusing on using data to identify patterns, evaluating the strength and significance of relationships, and generating predictions using data. Topics covered include the core principles of statistical programming (such as data frames, Python/R packages, reproducible workflows, and version control), univariate and multivariate statistical analysis of small and medium-size datasets, regression methods, hypothesis testing, probability models, basic supervised and unsupervised machine learning, data visualization, and network analysis. Students will learn how to use data to make effective arguments in a way that promotes the ethical usage of data. Students who complete the course will be able to produce meaningful, data-driven analyses of real-world problems and will be prepared to begin more advanced work in data-intensive domains.

This is an applied introductory course for students who wish to harness growing digital and computational resources. The focus of the course is on using data to identify patterns, evaluate the strength and significance of relationships, and generate predictions using data, carrying students through the entire data science workflow from data collection to communication of results. These techniques are implemented using a reproducible workflow, programmatic techniques, and version control software. Students will learn how to use data to make effective arguments, in a way that promotes the ethical usage of data.

In order to regulate and govern, states must marshall data: the whos, whats, and wheres of a nation get rendered in records and statistics, at least approximately, and they tell us a lot about identity and values. In this course we will use Census data as a jumping-off point for an investigation of data practices of governance. Students will learn tools for geospatial data science in Python/Geopandas and will learn principles of mapping. Readings include selections from geography, anthropology, science and technology studies, and urban sociology.

This course states and motivates the observation that cognition is fundamentally a computational process and explores the implications of this idea. Students are introduced to a variety of conceptual tools for thinking about cognitive information processing, including statistical learning from experience and the use of patterns distilled from past experience in guiding future actions. They learn to apply these tools to gain understanding of perception, memory, motor control, language, action planning, problem solving, decision making, reasoning, intelligence, and creativity.Applications of the newly acquired computational cognitive science concepts and tools to ecological issues - in particular, the accelerating climate catastrophe - are discussed in this course on a regular basis.

A project-based course in which programmers and designers collaborate to make a computer game. This course investigates the theory and practice of developing computer games from a blend of technical, aesthetic, and cultural perspectives. Technical aspects of game architecture include software engineering, artificial intelligence, game physics, computer graphics, and networking. Aesthetic and cultural aspects include art and modeling, sound and music, game balance, and player experience.

This course addresses the contemporary socio- technological challenges and debates about digital media and information technologies and their impact on society. It covers a broad spectrum of topics spanning digital technologies, social behaviors, and society. We will critically evaluate research evidence and its relevance to various social contexts and practices, diverse users and uses of social media, ethical and policy implications, trade-offs of potential solutions, and the impact of socio-technological shifts on individuals and communities. Our overarching goal is to challenge commonplace assumptions about these tools and phenomena; and to ask deeper questions about their profound impact on society.

This course will cover technologies for representing, modeling and displaying data in the context of interactive web pages. Practical skills for building web pages will be mixed with data mining algorithms and visualization design theory. We will use the D3 Javascript library to develop both static and dynamic visualizations, learn more about programming in Javascript, and explore web scalable vector graphics (SVG). Through design critique and formal study, we will identify the techniques visualization developers employ to create the right visualization for a given use case.

Data scientists often present information to disseminate their findings. This course introduces theories and applications of communicating with data, with an emphasis on visualizations. To support this approach, we will focus on the what, why, and how of data visualization. What focuses on specific types of visualizations for a particular purpose, as well as tools for constructing these plots. In how we will focus on the process of generating a data visualization from pre-processing the raw data, mapping attributes of the data to plot aesthetics, strategically determining how to define the visual encoding of the data for maximal accessibility, and finalizing the visualization to consider the importance of visual appeal. In why we discuss the theory tying together the how and the what, and consider empirical evidence of best-practices in data communication.

The class will introduce methods for computer-assisted analysis of historical and literary text collections. It will cover corpus curation, representing text as data, building statistical models from text, and interpreting quantitative results. The class will also reflect on how computational methods fit with existing practices in the humanities, and how we can use models as complements to our own interpretations. Following the course, students will be able to assist faculty in quantitative and computational humanities scholarship.

Inequality is high in American society. Income and wealth are concentrated in far fewer hands than in other industrialized countries. Labor market outcomes are patterned by disparities across lines of race, gender, and class. This course will introduce social science theories about the origins of inequality, emphasizing how inequality is transmitted over time and across generations. Building on these theories, students will deploy tools for data science to visualize inequality, understand inequality, and evaluate hypothetical policy interventions that might reduce inequality. We will use the R programming language. A theme of the course is that applied work in this area can give rise to new data science tools, which may help solve some of society's most pressing challenges.

This course focuses on user experience design (UX) and the life cycle of interface design from the user perspective. We will discuss key aspects of the human-centered design process: understanding, analyzing, and formalizing user needs, exploring possible design solutions to address user needs, creating prototypes to externalize design ideas, and evaluating the usability of these prototypes.

In this course, we will examine the role of electronic and digital technologies in the arts of the late 20th and 21st centuries with emphasis on Europe and North America. Beginning with the cybernetically and systems-inspired work of the late sixties, we will explore early uses of computer technology, including early experiments in synthetic video in the 1970s. An overview of pre-internet telematic experiments will lead to an investigation of net art and later currents of digital art. The ongoing development of behavioral art forms will be a central theme. Critical evaluation of various attitudes concerning technology will be encouraged.

Causal claims are essential in both science and policy. Would a new experimental drug improve disease survival? Would a new advertisement cause higher sales? Would a person's income be higher if they finished college? These questions involve counterfactuals: outcomes that would be realized if a treatment were assigned differently. This course will define counterfactuals mathematically, formalize conceptual assumptions that link empirical evidence to causal conclusions, and engage with statistical methods for estimation. Students will enter the course with knowledge of statistical inference: how to assess if a variable is associated with an outcome. Students will emerge from the course with knowledge of causal inference: how to assess whether an intervention to change that input would lead to a change in the outcome.

This course will cover intermediate-level applications of data science, with focus on discovery, interpretation, and communication of meaningful patterns in data. Topics will include regression, classification, clustering, and forecasting, with an overview of machine learning algorithms and statistical inference. Lectures and problem sets will make extensive use of python programming.

Technology has transformed how people teach and learn today. It also offers unprecedented insight into the mechanics of learning by collecting detailed interaction and performance data, such as in online courses and learning management systems like Canvas. At the intersection of education and data science, learning analytics are used to make sense of these data and use them to improve teaching and learning. This course blends learning theories and methodologies covering a wide range of topics with weekly hands-on activities and group projects using real-world educational datasets. You will learn how learning works, major theories in the learning sciences, and data science methods. Students collect and analyze their own learning trace data as part of the course. Learning outcomes: Students will learn to articulate key ideas in the learning sciences; articulate the potential benefits and dangers of learning analytics for students, teachers, and institutions; choose and apply appropriate methods for analyzing different kinds of educational data and be able to articulate why; and interpret the results of basic learning analytics.

This course explores new developments at the intersection of law and information technology. The class is structured as a series of dialogues with a diverse group of scholars - professors, practitioners, journalists, and others -- investigating how law and new technologies interact with and shape one another across many different domains. Students will be exposed to innovative research about technology policy, privacy, platforms, law enforcement, the nature of expertise, and the changing nature of legal and technical practice. The class will include both a public lecture component and a smaller group discussion.

This course will introduce students to the field of Ubiquitous Computing - a multidisciplinary research area that draws from Machine Learning, Machine Perception, Signal Processing, Human Computer Interaction, as well as psychology and sociology. Class discussions and hands on exercises will be the focus of this class with a few formal lectures. Discussions will highlight the various challenges in data collection, representation and tractability of models, and evaluation. We will brainstorm ideas on how future research can go about tackling some of these challenges. Students will be required to lead and critique papers. Everyone will take turns presenting papers and leading discussions. Participation in discussions will be evaluated as well as mini projects assignments during the term and an end of term final project.

This introductory course will emphasize the interpersonal skills necessary by project teams to manage demanding industry projects. Through a semester long simulated IS project, students will develop and apply a range of skills including time management, effective conflict resolution, negotiation, risk management, change control, and appropriate goal setting to adequately manage and execute their projects. Students will work in teams to engage in the 5 phases of the project management life cycle which includes: project initiation, planning, execution, monitoring, and project closure.

This course explores selected topics in the law, policy, and politics of cybersecurity. Of specific interest will be a historical understanding of the development of the internet and how that history laid the foundation for the insecurity of products, services, and internet users’ experience. Using a four-factor approach: law, market, social norms, and technology, we will explore the global landscape and its implications for situating cybersecurity in international law and global internet governance. The focus of the course will be on the United States, however, and will include topics such as the federal government’s approach to cybersecurity, the National Cybersecurity Strategy, information security and risk management, types of cyberattacks and case studies, national defense generally and global internet governance.

Increasingly, social and economic interactions are mediated by online platforms and algorithmic systems that rely on ubiquitous connectivity and data accumulation for their operation. While these systems have brought an unprecedented number of new products and services that have fostered growth and innovation, they have also created novel challenges to privacy and data security. In this course, we explore privacy and data security as multi-faceted concepts with economic, legal, social, ethical, and psychological underpinnings.

Project-based follow-up course to INFO 3152. Students work in a multidisciplinary team to develop a game that incorporates innovative game mechanics. Advanced topics include nontraditional input schemes, advanced storytelling, social gameplay, and serious games. There is a special emphasis on developing games that can be submitted to festivals and competitions, or that can be commercialized.

This course will address a wide range of information policy issues such as privacy, security, antitrust, intellectual property, algorithmic fairness, net neutrality, risk mitigation strategies, and other legal and policy compliance concerns in a simulated project management environment. Patterned on life cycle project management for products and services in contemporary large-scale technology companies, students will adopt specific topic areas for applied research and analysis working dynamically with other team members. Course outcomes include conducting upper-level research in specific information policy domains, experiential group dynamics, persuasive analytic presentations, fundamentals of project management in the technology sector, and insights into corporate hierarchies, organization, and functionalities.

In the coming decades machine intelligence will transform the economy, society, and global politics. This course will study these processes and the political challenges to the development of beneficial AI. Topics will include problems in AI safety; effects on- and of inequality and technological unemployment; the strategic and tactical impacts of autonomous weapons, cyberweapons, and AI-enabled intelligence operations; global institutions for providing global public goods, the legitimate aggregation of preferences, and the beneficial governance of AI development.

Networks II builds on its prerequisite course and continues to examine how each of the computing, economic, sociological and natural worlds are connected and how the structure of these connections affects these worlds. In this course, we will construct mathematical models for and analyze networked settings, allowing us to both make predictions about behavior in such systems, as well as reason about how to design such systems to exhibit some desirable behavior. Throughout, we will draw on real-world examples such as social networks, peer-to-peer filesharing, Internet markets, and crowdsourcing, that illustrate these phenomena.

The social impact of technologies is typically thought about fairly late, if ever, in the design process. Indeed, it can be difficult at design time to predict what effects technologies will have. Nevertheless, design decisions can inadvertently lock in particular values early on. In this course, we will draw on science & technology studies, technology design, and the arts to analyze the values embodied in technology design and to design technologies to promote positive social impact. What social and cultural values do technology designs consciously or unconsciously promote? To what degree can social impact be built into a technology? How can we take social and cultural values into account in design?

This course explores the social, legal, and ethical facets of surveillance in modern life. New information technologies facilitate data collection at unprecedented scales and reaching across multiple domains. We explore the history and theory of data collection for purposes of social and political control, and how privacy is conceptualized and deployed in response to surveillant systems. We discuss how surveillance and privacy are situated in law and culture; the emergence of surveillance in law enforcement, workplaces, institutions, families, and self-tracking contexts; and the relationships among surveillance, power, and marginalization of vulnerable groups. We consider avenues through which surveillance is resisted, including technological and policy-based design strategies to mitigate the effects of data collection.

This experimental, collaborative and seminar-based class will explore the material ethics of computing - the ways in which computing rests upon, emerges from, and ultimately returns to the earth, with deep and sometimes negative implications for sustainability, equity and justice in a rapidly changing world. Drawing on journalistic sources and academic fields ranging from anthropology, philosophy, public policy and environmental ethics to law, science and technology studies and human-computer interaction, the course will examine problems of computing-related sourcing and extraction, energy and consumption, and waste and repair, and how these are distributed and experienced in vastly different ways by different social groups and actors. Cases and examples will be drawn from near-to-hand and around the world.

How to make sense of the vast amounts of information available online, and how to relate it and to the social context in which it appears? This course introduces basic tools for retrieving and analyzing unstructured textual information from the web and social media. Applications include information retrieval (with human feedback), sentiment analysis, and social analysis of text. The coursework will include programming projects that play on the interaction between knowledge and social factors.

This course examines moral and ethical issues in communication, new media, and technology. Using theories and research in moral philosophy and psychology, we examine how people perceive and reason about ethical issues presented by media and technology, and how moral action is influenced by cognitive, emotional, and ethical belief systems. Issues of autonomy, transparency, harm, privacy, manipulation, justice, democracy, equality, and care are discussed. We analyze the consistency between personal and professional ethics, the importance of moral character and agency, and the translation of moral thought to ethical action, and address the development of professional and personal ethical codes of conduct and research for communication professionals in the areas of new media and technology.

This course explores a variety of techniques for making appealing and convincing representations of digital data. Students will build a toolbox of techniques for representing information, explore ways for making visualizations interactive, investigate how cognition makes data easy or hard to understand, and use their web development and data-scraping abilities to construct new visualizations. Projects centered around current issues will allow students to make use of their new knowledge both individually and in groups, and in-class critique sessions will refine their understanding of what makes for successful and unsuccessful designs.

This class provides an introduction to modern rapid prototyping techniques such as laser cutting, 3D printing and microcontroller programming (such as the Arduino system). Using these tools, small multidisciplinary groups conduct the hardware project of their choice exploring topics as varied as: Universal Access, tangible interfaces, toys, personal or medical assistants and new musical instruments.

In this interactive studio-based course, students will gain practical experience independently creating high-fidelity prototype apps. This course has a significant software development focus, exposing students to software development methods and tools necessary for developing interactive software prototype applications. This includes employing UI component libraries, event-based programming, generative AI, basic command line usage, and debugging. Learning how to solve technical problems and use unfamiliar technology independently is emphasized; students should not expect step-by-step lectures or instructions for authoring their prototypes. Proficiency in dynamic client and server-side web programming is assumed.

As conversations become central to the way in which we acquire and consume information and as the societal implications became evident (e.g., potential for misinformation, hate speech, ethical questions surrounding the use of chatbots), there is an increasing need for automated ways to analyze their quality. This course will cover computational approaches to conversational analysis: starting from topics that were traditionally addressed within the Sociology subfield of Conversational Analysis, continuing with reviewing existing computational methods that address those topics, and further guiding the development of new methods and their application to real-life conversational data. In addition to lecturing in-class activities will include close interaction with conversational data, such as collaborative annotation of conversational phenomen, in-depth discussion of hand-picked examples, as well as joint and iterative development of annotation guidelines that lend themselves for computational modeling.

Course covers the relationship between communication networks and social capital and explores the tension between individual and group capital.

Algorithms increasingly guide high-stakes decision-making across many domains. This has potential upsides, since algorithms can improve decision-making, but also serious risks, since recent years have showcased the many ways that algorithms can be biased. This course will teach you principles for designing fair algorithms, emphasizing accessibility to a broad audience via practical takeaways which are directly relevant to the real world through case studies and guest speakers. Case studies will be drawn from diverse settings where algorithms are applied, such as large language models, speech recognition systems, healthcare, criminal justice, sustainability, and education. Students will come away with a strong understanding of how algorithm-related choices can have widespread societal impact.

This upper-level course provides an in-depth understanding of and experience with advanced concepts and techniques for researching, ideating, critiquing, designing, prototyping, and evaluating interactive technologies intended for people to use and incorporate in their lives. The course focuses on advanced user research and design methods.

"Re-Designing Robots" is a studio-based graduate course focused on building and deploying robots in real-world settings such as homes, workplaces, and public spaces. Students will work individually and collaboratively in interdisciplinary teams, incorporating perspectives from engineering, computer science, art, design, and social sciences. The course emphasizes critical examination of the societal roles and ethical implications of robotic technologies, encouraging students to ask not just how robots can function better, but how they can meaningfully and responsibly enhance human activities. Key topics include prototyping interactive robotic systems, video interaction analysis, and hands-on experimentation with physical robot prototypes. Through immersive, hands-on projects, students will develop the expertise to thoughtfully design robots that integrate seamlessly and responsibly into everyday life.

This course will introduce User Experience design technique taught in a Studio. Students will work on a series of design studies and a semester long design project exploring advanced User Interaction design techniques. The course is designed to help students start a design portfolio to reflect their work. Students will be expected to buy small art supplies.

This course provides practical, experiential training designed to enhance students' teamwork and team-leadership skills through hands-on activities, reflection, and collaborative discussions. Recognizing that technology fundamentally shapes how we collaborate, students will explore how to effectively use technological tools—such as remote collaboration platforms, virtual meeting applications, and novel AI tools—to enhance team interactions and productivity. Each session includes interactive tasks aimed at building critical interpersonal skills, including managing group dynamics, resolving conflicts, improving communication, facilitating decision-making processes, and developing leadership strategies in technologically supported environments. Students will regularly reflect on their personal experiences and collaboratively assess how different technologies influence teamwork and collaboration. Grounded in insights from organizational behavior, social psychology, and human-computer interaction, the course emphasizes the practical application of skills essential for navigating and leading teams effectively. Upon completion, students will be equipped with concrete strategies and confidence to build and lead effective teams.

Focuses on reading and evaluating the theories and research methodologies used to investigate communication via computer systems. Assignments include student collaborations using electronic conferencing and other advanced communication technologies, as well as reflections on and evaluations of these collaborations in light of current theories and research findings. Topics include virtual teams, videoconferencing, and others as they emerge.

This course explores personal connections in the digital age, and how information and communication technologies impact our lives and relationships. It focuses on how people manage interactions and identities, develop and maintain relationships, accomplish social goals, create shared meanings, and engage in collaboration and conflict in social media. Emphasis will be placed on how current thinking in relational communication can explain and anticipate interpersonal dynamics on the Internet, but also on how online behaviors may challenge traditional principles of human communication. A major part of the course is a semester-long research project in which students working in small groups design, run, and present their own empirical study of personal relationships and technology.

Examines how new communication technologies affect the way we produce and understand language and modify interaction with one another. Focuses on the collaborative nature of language use and how Internet technologies affect the joint activities of speakers and listeners during the construction of meaning in conversation

To date, most computing technologies have primarily benefited urban, affluent, and literate people in developed regions by empowering them with more information, resources, and agency. These technologies currently exclude billions of people worldwide, such as rural residents, people with disabilities, and indigenous communities, who are too poor to afford modern devices, too remote to be connected, or too low-literate to navigate the mostly text-driven Internet. In recent years, researchers and practitioners have examined how computing technologies can be designed or appropriated to empower such underserved communities. This course introduces students to the field of Information and Communication Technologies and Development (ICTD). Through discussions of case studies from the Global South, students will study how computing technologies are used in different global development domains, such as agriculture, finance, health, social justice, and education. They will gain understanding of socio-economic, cultural, and political forces that impact technology adoption in low-resource environments and will learn to design, build, and evaluate inclusive technologies to empower marginalized people.?

This course covers the fundamental technical and organizational concepts and challenges related to the development of Business Intelligence Systems, a key component crucial to the competitiveness of a wide range of organizations. Topics covered include: data profiling, dimensional data modeling, data transformation, metadata systems, data governance, data delivery options, and an overview of emerging technologies in this space. Course is comprised of interactive lectures, work/lab sessions, and a substantial team project.

Evaluation is a pervasive feature of contemporary life. Professors, doctors, countries, hotels, pollution, books, intelligence: there is hardly anything that is not subject to some form of review, rating, or ranking these days. This senior seminar examines the practices, cultures, and technologies of evaluation and asks how value is established, maintained, compared, subverted, resisted, and institutionalized in a range of different settings. Topics include user reviews, institutional audit, ranking and commensuration, algorithmic evaluation, tasting, gossip, and awards. Drawing on case studies from science, technology, culture, accounting, art, environment, and everyday life, we shall explore how evaluation comes to order our lives - and why it is so difficult to resist.

Independent reading and research for undergraduates.

Involves working as a teaching assistant in a course in the information science or information science, systems, and technology major.

This course explores what artificial intelligence (AI) can teach us about human storytelling. We'll tackle this question through a two-pronged approach: story understanding and story generation. Students will explore how AI and machine learning technologies have revolutionized our approach to analyzing narratives as well as computational methods and concepts for modeling stories, moving towards a broader understanding of storytelling's societal effects.

This independent study course offers IS majors (i.e., undergraduates whose applications to affiliate with the IS major have been approved) an opportunity to reflect on concepts from information science as they were encountered and applied in a recent internship. Students write a short paper describing their work experience and how it connects to the educational objectives of the information science major.

Advanced independent work in information science as part of a student-led team project. Students must make individual arrangements with a faculty sponsor. Students are expected to spend 3-4 hours per week per credit hour working on the project.

This independent study course offers IS MPS Grad Students strictly for CPT purposes only.

This is an applied course for data scientists with little-to-no programming experience who wish to harness growing digital and computational resources. The focus of the course is on generating reproducible research using programming languages and version control software. Major emphasis is placed on a pragmatic understanding of core principles of programming and packaged implementations of methods. Students will leave the course with basic computational skills implemented through many computational methods and approaches to data science; while students will not become expert programmers, they will gain the knowledge of how to adapt and expand these skills as they are presented with new questions, methods, and data.

This course will cover technologies for representing, modeling and displaying data in the context of interactive web pages. Practical skills for building web pages will be mixed with data mining algorithms and visualization design theory. We will use the D3 Javascript library to develop both static and dynamic visualizations, learn more about programming in Javascript, and explore web scalable vector graphics (SVG). Through design critique and formal study, we will identify the techniques visualization developers employ to create the right visualization for a given use case.

Technology has transformed how people teach and learn today. It also offers unprecedented insight into the mechanics of learning by collecting detailed interaction and performance data, such as in online courses and learning management systems like Canvas. At the intersection of education and data science, learning analytics are used to make sense of these data and use them to improve teaching and learning. This course blends learning theories and methodologies covering a wide range of topics with weekly hands-on activities and group projects using real-world educational datasets. You will learn how learning works, major theories in the learning sciences, and data science methods. Students collect and analyze their own learning trace data as part of the course. Learning outcomes: Students will learn to articulate key ideas in the learning sciences; articulate the potential benefits and dangers of learning analytics for students, teachers, and institutions; choose and apply appropriate methods for analyzing different kinds of educational data and be able to articulate why; and interpret the results of basic learning analytics.

This introductory course will emphasize the interpersonal skills necessary by project teams to manage demanding industry projects. Through a semester long simulated IS project, students will develop and apply a range of skills including time management, effective conflict resolution, negotiation, risk management, change control, and appropriate goal setting to adequately manage and execute their projects. Students will work in teams to engage in the 5 phases of the project management life cycle which includes: project initiation, planning, execution, monitoring, and project closure.

This course explores selected topics in the law, policy, and politics of cybersecurity. Of specific interest will be a historical understanding of the development of the internet and how that history laid the foundation for the insecurity of products, services, and internet users’ experience. Using a four-factor approach: law, market, social norms, and technology, we will explore the global landscape and its implications for situating cybersecurity in international law and global internet governance. The focus of the course will be on the United States, however, and will include topics such as the federal government’s approach to cybersecurity, the National Cybersecurity Strategy, information security and risk management, types of cyberattacks and case studies, national defense generally and global internet governance.

Increasingly, social and economic interactions are mediated by online platforms and algorithmic systems that rely on ubiquitous connectivity and data accumulation for their operation. While these systems have brought an unprecedented number of new products and services that have fostered growth and innovation, they have also created novel challenges to privacy and data security. In this course, we explore privacy and data security as multi-faceted concepts with economic, legal, social, ethical, and psychological underpinnings.

Students work in a multidisciplinary team to develop a game that incorporates innovative game mechanics. Advanced topics include nontraditional input schemes, advanced storytelling, social gameplay, and serious games. There is a special emphasis on developing games that can be submitted to festivals and competitions, or that can be commercialized. Students may take this course as either a programmer or a designer; the prerequisites differ depending upon the selected track. Contact the instructor for more information.

This course will address a wide range of information policy issues such as privacy, security, antitrust, intellectual property, algorithmic fairness, net neutrality, risk mitigation strategies, and other legal and policy compliance concerns in a simulated project management environment. Patterned on life cycle project management for products and services in contemporary large-scale technology companies, students will adopt specific topic areas for applied research and analysis working dynamically with other team members. Course outcomes include conducting upper-level research in specific information policy domains, experiential group dynamics, persuasive analytic presentations, fundamentals of project management in the technology sector, and insights into corporate hierarchies, organization, and functionalities.

The course is organized around five major case studies on the use of discrete optimization and data (AI at large) for smart cities. Namely, 1) bike sharing, 2) bus transportation planning, 3) fairness in ambulance allocation (and police patrol), 4) downed tree reporting (NYC Department of Parks and Recreation), and 5) parcel delivery.The teaching is structured to provide three levels of understanding: a first level in which the problem is presented with its importance, impact, and characteristics. The need of a tech-based quantitative approach is claimed and justified. A second level in which, a high-level version of the algorithmic approach used to solve the problem is presented. A third level, dependent on students' interest and knowledge, in which the algorithmic approach is detailed.

This course introduces students to ethical thinking about digital systems. It focuses not only on how technology may be used for good or for ill, but how to take ethics, systematically, into consideration in analyzing, designing and developing it. Through lectures and discussion, the course introduces ethical concepts, such as privacy, fairness, and safety, and demonstrates their relevance to past and contemporary systems. In small groups, students will apply what they learn to in-class exercises and will be guided toward a final project in which they demonstrate learned concepts in concrete systems of their choosing. Although the course is designed to accommodate a wide range of educational backgrounds, it expects students to complete a common set of readings and participate actively in classroom discussions.

The course focuses on how to transition from legacy energy delivery infrastructures dependent on fossil fuel to a sustainable decarbonized grid that harnesses distributed renewable energy resources and responsive demand from buildings, electrified transportation systems, and industrial loads. The content includes models and abstractions for the architecture of the cyber-physical energy system, its economics, and future evolution, and numerical optimization and learning methods in support of the infrastructure's safety critical operations in the legacy system and in the future architecture. At the MSc level the students will focus on learning how to use tools and data while at the graduate level the students will be asked to also solve problems, formulate novel solutions, interpret results. Similarly, to differentiate the MSc from PhD level and course outcomes, the final project will require the MSc students to pick one out of a set of predefined problems while the PhD students will have to define an original problem and solution.

The social impact of technologies is typically thought about fairly late, if ever, in the design process. Indeed, it can be difficult at design time to predict what effects technologies will have. Nevertheless, design decisions can inadvertently lock in particular values early on. In this course, we will draw on science & technology studies, technology design, and the arts to analyze the values embodied in technology design and to design technologies to promote positive social impact. What social and cultural values do technology designs consciously or unconsciously promote? To what degree can social impact be built into a technology? How can we take social and cultural values into account in design?

This course explores the social, legal, and ethical facets of surveillance in modern life. New information technologies facilitate data collection at unprecedented scales and reaching across multiple domains. We explore the history and theory of data collection for purposes of social and political control, and how privacy is conceptualized and deployed in response to surveillant systems. We discuss how surveillance and privacy are situated in law and culture; the emergence of surveillance in law enforcement, workplaces, institutions, families, and self-tracking contexts; and the relationships among surveillance, power, and marginalization of vulnerable groups. We consider avenues through which surveillance is resisted, including technological and policy-based design strategies to mitigate the effects of data collection.

This experimental, collaborative and seminar-based class will explore the material ethics of computing - the ways in which computing rests upon, emerges from, and ultimately returns to the earth, with deep and sometimes negative implications for sustainability, equity and justice in a rapidly changing world. Drawing on journalistic sources and academic fields ranging from anthropology, philosophy, public policy and environmental ethics to law, science and technology studies and human-computer interaction, the course will examine problems of computing-related sourcing and extraction, energy and consumption, and waste and repair, and how these are distributed and experienced in vastly different ways by different social groups and actors. Cases and examples will be drawn from near-to-hand and around the world.

This course introduces students to the design and evaluation of digital systems that are usable by people with a wide range of abilities and needs. Students will learn core concepts in accessibility, assistive technologies, and design patterns that support inclusion. The course emphasizes hands-on practice with evaluating and designing accessible websites, mobile applications, and emerging technologies. Examples of topics include assistive technologies for people with vision disabilities, web accessibility standards, and mobile accessibility. Through a combination of lectures, in-class exercises, and projects, students will develop the skills to create technology that is more usable and effective for everyone.

This course examines moral and ethical issues in communication, new media, and technology. Using theories and research in moral philosophy and psychology, we examine how people perceive and reason about ethical issues presented by media and technology, and how moral action is influenced by cognitive, emotional, and ethical belief systems. Issues of autonomy, transparency, harm, privacy, manipulation, justice, democracy, equality, and care are discussed. We analyze the consistency between personal and professional ethics, the importance of moral character and agency, and the translation of moral thought to ethical action, and address the development of professional and personal ethical codes of conduct and research for communication professionals in the areas of new media and technology.

This course introduces students to privacy technologies and surveys the current state of digital privacy from multiple perspectives, including technology, law, policy, ethics, economics, and surveillance.

Massive amounts of data are collected by many companies and organizations and the task of a data scientist is to extract actionable knowledge from the data - for scientific needs, to improve public health, to promote businesses, for social studies and for various other purposes. This course will focus on the practical aspects of the field and will attempt to provide a comprehensive set of tools for extracting knowledge from data.The course will cover the topics needed to solve data-science problems, which include problem formulation (business understanding), data preparation (collection, sampling, integration, cleaning), data modeling (characterization, model selection, and analysis), implementation (large-scale data processing, feedback loops, QA) and communication (data presentation, visualization). Advanced topics such as causal inference and processing streaming data will be presented.Throughout the course, the students will perform a data-science mission with all the required steps, from problem formulation to result presentation.

This course takes a deep dive into the user experience (UX) lifecycle--from conceiving an idea for a product to evaluating a prototype--and user research methods. We will cover techniques for understanding your target users (e.g., observations and interviews) and designing and evaluating your prototypes (e.g., lab studies and walkthroughs). If you have taken a human computer interaction class before, many of these techniques will be familiar. Our goal in this course is to go deeper, help you gain more practice and expertise so that you will be ready to apply what you learn when you graduate. The course lectures will cover the theory, history, and practical aspects of UX and user research methods. Student deliverables include many short in-class exercises and weekly homework assignments.

Crowdsourcing and human computation refer to various ways that computing has brought together human labor to achieve outcomes that were previously beyond our individual capabilities or expectations. Wikipedia's millions of articles, the success of Linux and other open source software projects, citizen science, and the generation of large datasets for machine learning and artificial intelligence using microlabor are all examples of ways in which technology and people together have exceeded the capabilities of people or machines in isolation. This course will survey the state of the art in this area and give practical knowledge of the area, grounded in numerous examples in diverse settings.

This course explores the psychological and sociological foundations of communication and information technology. The course focuses on the theories from the cognitive, behavioral and social sciences that play a role in understanding people's use of, access to and interactions with information and communication technologies. The course will examine multiple levels of analysis - individual, social, and social systems/communities - and present multiple disciplinary perspectives. The course will also provide a high-level view of methodologies used in the behavioral and social sciences.

This course explores a variety of techniques for making appealing and convincing representations of digital data. Students will build a toolbox of techniques for representing information, explore ways for making visualizations interactive, investigate how cognition makes data easy or hard to understand, and use their web development and data-scraping abilities to construct new visualizations. Projects centered around current issues will allow students to make use of their new knowledge both individually and in groups, and in-class critique sessions will refine their understanding of what makes for successful and unsuccessful designs.

Data scientists often present information to disseminate their findings. This course introduces theories and applications of communicating with data, with an emphasis on visualizations. To support this approach, we will focus on the what, why, and how of data visualization. What focuses on specific types of visualizations for a particular purpose, as well as tools for constructing these plots. In how we will focus on the process of generating a data visualization from pre-processing the raw data, mapping attributes of the data to plot aesthetics, strategically determining how to define the visual encoding of the data for maximal accessibility, and finalizing the visualization to consider the importance of visual appeal. In why we discuss the theory tying together the how and the what, and consider empirical evidence of best-practices in data communication.

Trust & Safety is an emerging field that focuses on reducing the harm from interpersonal abuse in digital spaces. The abuse types involved - harassment, misinformation, unwanted sexual content - are often lawful but awful, requiring developers to build their own socio-technical frameworks of what is appropriate behavior in their platform. In this course, we will look at digital abuse through an analysis of historical incidents. We will study how the field developed standards across algorithmic response, product design and manual removals. Students will join teams to describe an emerging online abuse type, develop appropriate moderation pipelines (e.g., using modern machine learning such as LLMs), and detail associated policies, all in an environment mimicking the realities seen in practice. This course might expose students to disturbing material.

This class provides an introduction to modern rapid prototyping techniques such as laser cutting, 3D printing and microcontroller programming (such as the Arduino system). Using these tools, small multidisciplinary groups conduct the hardware project of their choice exploring topics as varied as: Universal Access, tangible interfaces, toys, personal or medical assistants and new musical instruments.

This course evaluates the role of the information ecosystem in contributing to the health of our society with the goal of developing ideas and tools that can help citizens be equipped with the knowledge and information required to address the challenges that we face. Specifically, the course examines the socio-technical aspects of our information ecosystem and how they may hinder, or challenge, the functions of society in particular in representative democracies such as the United States. Key topics include (a) how the design of platforms, algorithms, monetary models, and online communities impacts our information ecosystem; (b) the challenges for credible and trustworthy information ecosystem and how can they be addressed; (c) the breadth of abuse and adversary online from mis- and disinformation to online harassment, including the types of abuse, the adversarial actors online, what are they goals, methods, and how they can be countered.

This course presents an introduction to virtual and augmented reality technologies, with focus on fundamental principles from 3D math, human perception, graphics, and interaction. Concepts from the contributing fields of computer vision, computer graphics and human computer interaction will be introduced in the context of virtual and augmented reality. Students will be tasked with creating their own virtual or augmented reality application as a course project.

This course will introduce students to the fields of ubiquitous computing and interactive device design, drawing from such diverse fields as electronic circuit design, signal processing, machine learning, and human-computer interaction. Taking a hands-on, lab-based approach, students will prototype their own interactive devices and use them to collect data and interact with users. Students will learn about fabrication techniques and printed circuit board design, and apply them to create their own devices. For the final project, students will build a functional embedded or wearable device that can be applied to problems in health technology, urban design or other real-world applications. Topic areas include electronics, sensors, actuators, interface design, physical prototyping, and user testing.

This course covers the human-centered and technical workings behind interactive devices ranging from cell phones and video game controllers to household appliances and smart cars. This is a hands-on, lab-based course. For the final project, students will build a functional IoT prototype of their own design, using Python, single-board Linux computer, embedded microcontrollers, and/or other electronic components. Topics include electronics prototyping, interface design, sensors and actuators, microcontroller development, physical prototyping, and user testing.

Behavioral economics studies the effect of psychological, social, cognitive and emotional factors on humans' decisions and behavior. This course will help students learn key concepts from behavioral economics and apply them in their daily lives, in the design of products, and in the research of human behavior. This course will explore the opportunities and challenges faced by researchers and practitioners when exploring the interplay between behavioral economics and technology.

This course focuses on user experience design (UX) and the life cycle of interface design from the user perspective. We will discuss key aspects of the human-centered design process: understanding, analyzing, and formalizing user needs, exploring possible design solutions to address user needs, creating prototypes to externalize design ideas, and evaluating the usability of these prototypes.

Robots are making their way into our everyday lives, working across several applications including in people's home, healthcare, and retail settings. As these systems become more integrated into our lives, it is important that they are designed to be useful, functional, and socially acceptable; however, this remains a key challenge for the field of human-robot interaction (HRI). This is a project-based course that covers core computational, engineering, and social challenges and approaches for effective HRI in human-centered environments. Topics include perception of people, computational HRI, design in HRI, coordination and collaboration, and experimental robotics. Students should expect to learn about seminal research in HRI, gain hands-on experience with physical mobile robots, and implement systems for real-time interaction with users.

Designing products and experiences for head-mounted displays, often with novel inputs such as hands and eyes, introduces new challenges for designers when compared to traditional devices and user interfaces. This course presents the theoretical and practical foundations of the field of 3D User Interfaces (3DUIs) and 3D interaction design for virtual and augmented reality applications. The course will highlight the differences between 2D and 3D interaction design, introduce design principles, best practices for prototyping, and case studies. Students will be tasked with designing, implementing, and evaluating interaction prototypes of different fidelity for 3DUI tasks such as selection and manipulation, travel, and system control.

The goal of this course is to educate students about the complexity and nuances of healthcare delivery. The course will be especially useful for non-clinicians who intend to go into fields that will require a detailed understanding of healthcare. Class sessions will analyze healthcare, so that students learn to identify tensions in healthcare and opportunities for improvement. Students will come away from the course with a deeper appreciation of why it is difficult to change healthcare. They will then be able to anticipate the intended and unintended consequences of interventions and policies that they and others might implement.

The deployment of AI systems in multiple domains raises fundamental concerns over societal values, such as, accountability, liability, fairness, transparency and privacy. The dynamic nature of AI systems requires a new set of skills informed by ethics, law, and policy to be applied throughout the life cycle of such systems: design, development and deployment. It also involves ongoing collaboration among data scientists, computer scientists, lawyers and ethicists. Tackling these challenges calls for an interdisciplinary approach: deconstructing these issues by discipline and reconstructing with an integrated mindset, principles and practices. This course aims to do so by bringing together students with diverse disciplinary backgrounds into teams that work on joint tasks in an intensive series of in-class sessions. These sessions include lectures, discussions, and group work. The course also brings together students from three campuses: Cornell Tech, Tel Aviv University Faculty of Law, and Technion with instructors and TAs from each.

This course provides hands-on experience developing and deploying foundational machine learning algorithms on real-world datasets for practical applications (e.g., healthcare, computer vision). Students will learn about the machine learning pipeline end-to-end including dataset creation, pre- and post-processing, annotation, annotation validation, preparation for machine learning, training and testing a model, and evaluation. Students will focus on real-world challenges at each stage of the ML pipeline while handling bias in models and datasets. Lastly, students will analyze the strengths and weaknesses of regression, classification, clustering, and deep learning algorithms.

This course considers the data science challenges beyond training an accurate predictive model, especially for systems about people (data of behavior), and for people (deployed models to influence behavior). Whether for online marketplaces, transportation, governmental, or urban systems, effective data science in such settings requires dealing with user incentives and strategic behavior, networked and decentralized decision-making, and privacy and ethics concerns. Primary evaluation will be through programming based assignments (in Python) and conceptual questions.

Inequality is high in American society. Income and wealth are concentrated in far fewer hands than in other industrialized countries. Labor market outcomes are patterned by disparities across lines of race, gender, and class. This course will introduce social science theories about the origins of inequality, emphasizing how inequality is transmitted over time and across generations. Building on these theories, students will deploy tools for data science to visualize inequality, understand inequality, and evaluate hypothetical policy interventions that might reduce inequality. We will use the R programming language. A theme of the course is that applied work in this area can give rise to new data science tools, which may help solve some of society's most pressing challenges.

This course introduces students the various real-world health related problems such as patient screening, risk modeling, disease subtyping and precision medicine, along with their associated data, such as patient clinical records, medical images, physiological and vital signals from wearable sensors, multi-omics, etc. and how to use appropriate machine learning algorithms to analyze these data and help with the corresponding real-world health problems. The machine learning techniques involved in this class include classic supervised and unsupervised learning, network analysis, probabilistic modeling, deep learning, transfer learning, federated learning, algorithmic fairness and interpretability. We will also invite clinicians or researchers working in the health industry to deliver guest lecturers in the class. The students will gain hands-on experience on analyzing real world health data during course assignments and projects.

Historically, computing encompassed one-dimensional Input/Output using command line interfaces. The GUI and mouse brought 2D I/O to computing, and later VR and 3D Scanning allowed for 3D I/O. The next thing is interacting with matter as I/O for computing: Digital Fabrication. This course teaches fabrication techniques, and how to leverage it to control physical matter. This will allow you to develop hardware or understand how to talk to hardware developers within your team. We will dive down to the bare bones of fabrication machines by creating custom slicers to control 3D printers using basic coding. We will take apart existing products to study how they were made and build entirely new products from scratch.

Algorithms increasingly guide high-stakes decision-making across many domains. This has potential upsides, since algorithms can improve decision-making, but also serious risks, since recent years have showcased the many ways that algorithms can be biased. This course will teach you principles for designing fair algorithms, emphasizing accessibility to a broad audience via practical takeaways which are directly relevant to the real world through case studies and guest speakers. Case studies will be drawn from diverse settings where algorithms are applied, such as large language models, speech recognition systems, healthcare, criminal justice, sustainability, and education. Students will come away with a strong understanding of how algorithm-related choices can have widespread societal impact.

This upper-level course provides an in-depth understanding of and experience with advanced concepts and techniques for researching, ideating, critiquing, designing, prototyping, and evaluating interactive technologies intended for people to use and incorporate in their lives. The course focuses on advanced user research and design methods.

Social scientists such as sociologists, economists, and anthropologists, as well as policymakers seek to better understand cities, in order to answer important theoretical questions, such as: what is a city in the first place; how does a city grow; what is a better city (and who is it better for); and (how) can we create and/or develop better cities. This course provides an integrative introduction to these disciplinary perspectives on cities so students acquire an overview of the key systems, processes, and technologies that must be managed, operated, and maintained in any urban center. The goal of the class is to unpack the complex interplay among institutions, markets, and citizens in the context of the financial, political, administrative, and legal policies that exist in urban communities so that students can understand urban systems in a holistic manner. We will deploy the lenses of three defining cornerstones of urban environments - institutions; real estate; and mass transit - to examine the management of cities and conflicts that arise within them, the design and use of public spaces and infrastructure, and the relationship between the public and the private spheres. The course pedagogy is a mix of participatory learning in the classroom and field visits/labs and expert lectures. The course will equip students with the conceptual and analytical tools necessary to ensure equitable, sustainable, and financially and politically viable approaches to urban challenges. It is primarily designed for students interested in working for a city, or in starting, working at, advising, or financing for-profit or not-for-profit ventures operating in the urban technology and innovation space.

The course will ground students in the basics of urban and spatial design practices as well as the surrounding environmental, sociological, political, and economic dimensions. This course will use primary and secondary source material and theoretical and empirical works to understand cities as complex interrelated environments. Students will learn about the multidimensional drivers that shape the urban systems and built environment that form our cities. The course will teach students how to analyze and transform urban environments to create livable and sustainable cities for future generations. The course will also contribute a design perspective for the program: project based, empirical, iterative, situated, and integrative. The course uses New York City as a real-world laboratory and augments foundational and theoretical examples with case studies of successful and unsuccessful initiatives. Students will develop projects that address urgent urban challenges such as sustainability, infrastructure, resiliency, mobility, affordability, access, and equity and will propose forward-thinking and technologically driven design approaches to urban challenges.

This course provides a broad overview of the opportunities and challenges related to urban data and helps familiarize students with key datasets and the tools and methodologies to visualize and analyze them.The course will introduce a framework to reason about urban data and will present various tools and methodologies to process the data, including data mining, machine learning, GIS, network analysis, simulation, agent-based modeling, and data visualization. Traditional Big Data challenges will be reviewed and the associated challenges speci?c to urban data such as quality, privacy, bias, and data governance will be highlighted. Students will also be introduced to the relevant optimization and simulation models so as to enable them to leverage these tools for data-driven decision-making and creating policy.

This course provides practical, experiential training designed to enhance students' teamwork and team-leadership skills through hands-on activities, reflection, and collaborative discussions. Recognizing that technology fundamentally shapes how we collaborate, students will explore how to effectively use technological tools—such as remote collaboration platforms, virtual meeting applications, and novel AI tools—to enhance team interactions and productivity. Each session includes interactive tasks aimed at building critical interpersonal skills, including managing group dynamics, resolving conflicts, improving communication, facilitating decision-making processes, and developing leadership strategies in technologically supported environments. Students will regularly reflect on their personal experiences and collaboratively assess how different technologies influence teamwork and collaboration. Grounded in insights from organizational behavior, social psychology, and human-computer interaction, the course emphasizes the practical application of skills essential for navigating and leading teams effectively. Upon completion, students will be equipped with concrete strategies and confidence to build and lead effective teams.

In this interactive studio-based course, students will gain practical experience independently creating high-fidelity prototype apps. This course has a significant software development focus, exposing students to software development methods and tools necessary for developing interactive software prototype applications. This includes employing UI component libraries, event-based programming, generative AI, basic command line usage, and debugging. Learning how to solve technical problems and use unfamiliar technology independently is emphasized; students should not expect step-by-step lectures or instructions for authoring their prototypes. Proficiency in dynamic client and server-side web programming is assumed.

Focuses on reading and evaluating the theories and research methodologies used to investigate communication via computer systems. Assignments include student collaborations using electronic conferencing and other advanced communication technologies, as well as reflections on and evaluations of these collaborations in light of current theories and research findings. Topics include virtual teams, videoconferencing, and others as they emerge.

The smart cities movement was born during the Global Financial Crisis of 2007-8. When multinationals slashed spending on IT, governments ramped up stimulus spending. Big vendors like IBM, Cisco, and Siemens seized the opportunity to port enterprise tech to the municipal sector. This project didn't get far. While there have been successes, city governments were slow to define and procure smart cities solutions. And the context changed. Consumer tech innovation raced ahead, drawing a new group of giant firms into the fray. They brought new technologies, but also more money, new business models, and more aggressive approaches to government affairs and deregulation. This shift from smart cities to urban tech is ongoing and defines the landscape for engineering cyberphysical urban systems today. This course is a survey of smart cities and urban tech for engineers, organized around 12-15 broad technical capabilities that reflect widely-shared views of what this movement seeks to achieve.

Cities are increasingly recognized as a key level of government for environmental and sustainability policy. As at all levels, politics and policy are intensely intertwined, and perhaps moreso at the local level because the decisions involved often affect constituents directly and intimately - in their neighborhoods, in their homes, in their commutes. This course explores both the politics and the policy of sustainability in the municipal context. Covering a range of sustainability issues - such as air quality, public health, and transportation - it looks at the dynamics of making change happen at the local level, including variations in power among municipal governments; how issues get defined and allocated; how stakeholder management takes place (or doesn't); how agencies and levels of government interfere with each other; and how best practices can (and cannot) be transferred internationally. The course is reading-intense and includes case studies by historians rather than political scientists. The focus of most readings is on the United States, but students' research projects will require looking beyond the US and transferring practices to a US city.

This course is designed to give students hands on experience applying health tech tools and methods to real world clinical challenges. Students will work (individually or in pairs) with a clinical advisor to assess a particular clinical need for application of digital technology and based on that assessment students will develop a feasibility prototype. Through the implementation process, students will have the opportunity to shadow their clinical advisor in a clinical or research setting.

To date, most computing technologies have primarily benefited urban, affluent, and literate people in developed regions by empowering them with more information, resources, and agency. These technologies currently exclude billions of people worldwide, such as rural residents, people with disabilities, and indigenous communities, who are too poor to afford modern devices, too remote to be connected, or too low-literate to navigate the mostly text-driven Internet. In recent years, researchers and practitioners have examined how computing technologies can be designed or appropriated to empower such underserved communities. This course introduces students to the field of Information and Communication Technologies and Development (ICTD). Through discussions of case studies from the Global South, students will study how computing technologies are used in different global development domains, such as agriculture, finance, health, social justice, and education. They will gain understanding of socio-economic, cultural, and political forces that impact technology adoption in low-resource environments and will learn to design, build, and evaluate inclusive technologies to empower marginalized people.?

This course is a survey of the computing systems, technologies, and data sets used throughout the healthcare system--spanning provider, patient, payer, and pharma. Students will gain an understanding of the functional requirements and constraints placed on these digital systems and to provide a basis for future innovation.

This course covers the fundamental technical and organizational concepts and challenges related to the development of Business Intelligence Systems, a key component crucial to the competitiveness of a wide range of organizations. Topics covered include: data profiling, dimensional data modeling, data transformation, metadata systems, data governance, data delivery options, and an overview of emerging technologies in this space. Course is comprised of interactive lectures, work/lab sessions, and a substantial team project.

With the rise in the availability of clinical and behavioral data in the world, artificial intelligence (AI) is now increasingly applied in the field of healthcare. Researchers are working on using AI in different spheres of the healthcare process ranging from understanding of the human behavior, early diagnoses of medical conditions and even recommendations for personalized treatment procedures. Physicians are using machine learning algorithms on clinical images for early detection of cancer. Researchers have shown that smartphone sensor data can be used to detect events like opioid overdose and enable just-in-time interventions to save lives. Psychologists are mining data from social networks to detect mental health conditions. This project based course will introduce the students to these next generation AI based healthcare systems and will focus on the design and implementation of these systems for the real world.

Precision Behavioral Health will introduce students to the sensors, computational systems and algorithms that will play a transformative role in the diagnosis, treatment, and management of mental and behavioral health. Recent computational advancements have repurposed ubiquitous technologies for passive and remote mental health measurement. These technologies have the potential to disrupt mental healthcare, giving clinicians more resources to manage patients' mental health and deliver care at lower cost. Yet, these technologies will also disrupt clinical workflows and data sharing norms, requiring frameworks for responsible use. Within this course, students will participate in seminar discussions and hands-on projects to learn about the ongoing advancements in multi-modal sensor fusion and AI algorithms for symptom measurement, technology-driven intervention, and the ethical implications of building these tools.

Robot and automated systems often need to move in and around people. This technical and human-centered course surveys current physical, computation and sensing technologies that make mobile human robot interaction possible, and delves into key research problems in interaction, human modelling, and repair which are needed to tackle the grand challenge of mobile HRI. As part of this course, students will build working prototypes of mobile robots, and deploy them for study in human environments. The 5000-level version of this course focuses on platform development; the 6000-level version of this course focuses on the production of human participant research studies using mobile HRI.

Independent research for Master of Professional Studies students.

This course will focus on career development topics specifically for MPS Information Science students. Through a series of discussions, activities, and guest speaker lectures, students will further develop career success strategies with an emphasis on personal strengths, networking, communication, and professional branding. By the end of the course, students will be able to identify and articulate to prospective employers, their unique interface between computing and the world.

The goal of this course is to help students deepen their theoretical and historical perspective on social change and the relationship between social change and technology. We will read a range of revolutionary thinkers including, but not limited to, Karl Marx, Friedrich Engels, Antonio Gramsci, Stuart Hall, Mahatma Gandhi, E.F. Schumacher and Frantz Fanon. We will study their theories of revolution and the underlying tensions within the division of labor in society, and discuss the role that technology can play in forming and reforming these social relations. We will use this as a lens to reflect both upon existing technology and the underlying relations that create and sustain it, as well as to envision new technological possibilities supported by (and supporting) alternative forms of human organization. Coursework will consist of significant reading, regular reading reflections, design exercises and a final course project.

This workshop-based service learning course will engage students in participatory design projects that use technology to support community-based organizations in urban contexts. The engagement will be structured as team-based internships, where students will be required to commit up to 5 hours per week to support the organization in its technology needs. We will reflect on these experiences, and connect them to broader themes impacting cities and the civic organizations within them - including migration, race, gender and disability. Students will learn and apply user-centered and participatory design methodologies; theoretical frameworks that help us understand the modern city; the unique technology challenges facing small community-based and civic organizations.

All students of the Jacobs Institute are required to complete a two-semester Specialization research project. We offer two Tracks for completing this requirement. * Track 1 Specialization projects are conducted in small teams on topics suggested and supervised by researchers at Cornell Tech or Weill Cornell. Track 1 students register for 4 credits of INFO 5920 in their first year Spring semester, as well as 4 credits of INFO 5920 in their second year Fall semester. * Track 2 Specialization project topics can be defined by the student; so long as they are 'anchored' with a 3-credit approved-elective course, and reviewed for program relevance by the INFO 5920 instructor. Track 2 students register for 3 credits of their anchor elective course and 1 credit of INFO 5920 during their first year Spring semester. Track 2 students register for 4 credits INFO 5920 in their second year Fall semester. In general, Specialization projects result in tangible, concentration-relevant deliverables in the form of prototypes, analyses, research studies, and/or publications, which become a sharable part of the student portfolio.

Study of topics not currently covered in INFO offerings, as determined by faculty and student interest.

Computation is an essential tool for many facets of information science research. Examples of its utility include capture, access and analysis of digital data; visualization of data for analysis, interpretation and information extraction; construction of user focused applications; and analysis of textual and sensor-derived information to detect patterns and dynamics of human activities, social interactions and social networks. Effective use of computation requires a mixture of skills including structuring data, accessing data, and programming, choosing and applying computational analysis methods, and designing visualizations. This course covers the mixture of these skills with the goal of providing INFOSCI graduate students with the appreciation of their utility and ability to employ them in future research.

This course explores new developments at the intersection of law and information technology. The class is structured as a series of dialogues with a diverse group of scholars - professors, practitioners, journalists, and others -- investigating how law and new technologies interact with and shape one another across many different domains. Students will be exposed to innovative research about technology policy, privacy, platforms, law enforcement, the nature of expertise, and the changing nature of legal and technical practice. The class will include both a public lecture component and a smaller group discussion.

This course will introduce students to the field of Ubiquitous Computing - a multidisciplinary research area that draws from Machine Learning, Machine Perception, Signal Processing, Human Computer Interaction, as well as psychology and sociology. Class discussions and hands on exercises will be the focus of this class with a few formal lectures. Discussions will highlight the various challenges in data collection, representation and tractability of models, and evaluation. We will brain-storm ideas on how future research can go about tackling some of these challenges. Students will be required to lead and critique papers. Everyone will take turns presenting papers and in leading discussions. Participation in discussions will be evaluated as well as mini projects assignments during the term and an end of term final project.

This seminar course will explore algorithmic fairness through a non-ideal lens. Rather than imagining what algorithmic fairness should look like in an ideal world, we will explore what the research tells us it looks like today, and what can be done given the incentives and practical constraints of the world we are in. For instance, the “silver bullets” of algorithmic fairness are often abstract calls made to increase participation, transparency, and regulation. However, each approach faces difficulties in practice. In an era where algorithmic fairness faces resistance from many fronts, it is critical that we foreground concrete proposals in addition to abstract ideals. In doing so, we’ll also discuss the consequences of taking on a perspective that might be overly reformist at the expense of radical re-imagining, and think through whether complementarity exists.

The focus of this course is to create a research agenda in technical HCI. We will study how people go about creating an agenda instead of individual projects. To this end, we will look at the work of recent PhD graduates, understand their contributions to technical HCI and what methods they use to validate these contributions. Beyond that, we also look at how their projects form a larger agenda and what patterns they use to develop such an agenda. In the first half of the course, we practice this by looking at recent graduates who have a complete agenda, in the second half, we move on to explore what an agenda could look like for very recent papers, including those of students taking the class.

Statistical topic models such as LDA provide a powerful tool for discovering themes in large unlabeled text corpora. They are increasingly popular in a wide range of fields, both as a data-driven alternative to manual document coding methods, and also as an example of a difficult but tractable problem in statistical inference. This course will cover Bayesian model construction, inference techniques, evaluation, and applications beyond text such as community detection in networks and population admixture in genetics.

This course explores key theoretical and methodological approaches underlying the study of information, technology, and society, focused primarily (though not exclusively) on social science approaches-drawing from disciplines like sociology, communications, history, science & technology studies, and others. The course is designed to be rigorous and to prepare students to make their own analytically and theoretically sound contributions to scholarship about information, technology, and society.

Networks II builds on its prerequisite course and continues to examine how each of the computing, economic, sociological and natural worlds are connected and how the structure of these connections affects these worlds. In this course, we will construct mathematical models for and analyze networked settings, allowing us to both make predictions about behavior in such systems, as well as reason about how to design such systems to exhibit some desirable behavior. Throughout, we will draw on real-world examples such as social networks, peer-to-peer filesharing, Internet markets, and crowdsourcing, that illustrate these phenomena.

This course is designed to provide PhD students with an understanding of research into, through, and for design. The course will cover readings across literature in human-computer interaction, engineering, education, and architecture. Topics include design theory, design as a research method, empirical studies of designing, theory building in design, and history and philosophy of design.

This course explores the behavioral foundations of communication technology and the information sciences, and the ways in which theories and methods from the behavioral sciences play a role in understanding people's use of, access to and interactions with information and communication technologies.

The course will introduce methods for computer-assisted analysis of historical text collections. It will cover corpus curation, representing text as data, building statistical models from text, and interpreting results. Statistical and programming concepts will be introduced as needed. The class will also reflect on how computational methods fit with existing practices in the humanities, and how we can use models as complements to our own interpretations. Following the course, students will be able to assist faculty in computer-assisted scholarship.

This upper-level course provides an in-depth understanding of and experience with advanced concepts and techniques for researching, ideating, critiquing, designing, prototyping, and evaluating interactive technologies intended for people to use and incorporate in their lives. The course focuses on advanced user research and design methods.

Human-Computer Interaction (HCI) and design theory and techniques. Methods for designing, prototyping, and evaluating user interfaces. Basics of visual design, graphic design, and interaction design. Understanding human capabilities, interface technology, interface design methods, prototyping tools, and interface evaluation tools and techniques.

"Re-Designing Robots" is a studio-based graduate course focused on building and deploying robots in real-world settings such as homes, workplaces, and public spaces. Students will work individually and collaboratively in interdisciplinary teams, incorporating perspectives from engineering, computer science, art, design, and social sciences. The course emphasizes critical examination of the societal roles and ethical implications of robotic technologies, encouraging students to ask not just how robots can function better, but how they can meaningfully and responsibly enhance human activities. Key topics include prototyping interactive robotic systems, video interaction analysis, and hands-on experimentation with physical robot prototypes. Through immersive, hands-on projects, students will develop the expertise to thoughtfully design robots that integrate seamlessly and responsibly into everyday life.

Focuses on reading and evaluating the theories and research methodologies used to investigate communication via computer systems. Assignments include student collaborations using electronic conferencing and other advanced communication technologies, as well as reflections on and evaluations of these collaborations in light of current theories and research findings. Topics include virtual teams, videoconferencing, and others as they emerge.

This seminar will showcase a variety of methods for and applications of algorithmic fairness research across disciplines – from machine learning and statistics, to public health and public policy – and will instruct on both (a) reading out-of-discipline papers and (b) writing for a multidisciplinary audience. Weekly guest lectures will each cover a different topic in algorithmic fairness, and will involve guided paper discussions and question-answering sessions with invited academic researchers.

This course explores personal connections in the digital age, and how information and communication technologies impact our lives and relationships. It focuses on how people manage interactions and identities, develop and maintain relationships, accomplish social goals, create shared meanings, and engage in collaboration and conflict in social media. Emphasis will be placed on how current thinking in relational communication can explain and anticipate interpersonal dynamics on the Internet, but also on how online behaviors may challenge traditional principles of human communication. A major part of the course is a semester-long research project in which students working in small groups design, run, and present their own empirical study of personal relationships and technology.

Examines how new communication technologies affect the way we produce and understand language and modify interaction with one another. Focuses on the collaborative nature of language use and how Internet technologies affect the joint activities of speakers and listeners during the construction of meaning in conversation. The graduate students are expected to take the lead on the small group class projects, in terms of identifying research questions, working out the method, and performing statistics. This requires mentorship of the undergraduates on their team. The graduate students are also expected to submit more in depth analyses of the readings for their individual paper assignments.

This seminar course will introduce students to research related to technology and disability. This includes user research involving people with disabilities, technology design to address the unique challenges experienced by people with disabilities, and ethical and historical issues related to these areas.

This course will introduce User Experience design technique taught in a Studio. Students will work on a series of design studies and a semester long design project exploring advanced User Interaction design techniques. The course is designed to help students start a design portfolio to reflect their work.

Valuation is a pervasive feature of contemporary life. Professors, start-ups, immigrants, intelligence, insurance premiums, and human lives: almost everything these days is subject to some form of more or less methodical assessment. This seminar examines valuation as a socio-technical phenomenon and asks how value and values are established, co-produced, maintained, subverted, institutionalized, and resisted. Through a mix of reading, writing, and activities, we shall engage with theoretical, historical, and contemporary studies of (e)valuation in science & technology studies (STS), but also draw on related areas like economic sociology, critical accounting studies, anthropology, and information science. Taken together, these ideas provide a powerful lens for analyzing what counts in cultures, organizations, and other forms of social life.

This course examines the design, deployment, and adoption of computing technologies that aim to improve the lives of underserved populations in low-resource environments. Through discussions of case studies from the US and across the world, we will study how computing technologies are used in different global development domains, such as agriculture, finance, health, education, civic engagement. We will also explore many of the big debates in the field of ICTD through reading and discussing seminal papers, including how to approach and measure success, how to consider failure, what constitutes 'good' research, how to broach the 'digital divide'. The course will enrich students' foundational knowledge and information about current research in ICTD, and will better equip them to create and evaluate computing technologies for people in low-resource environments.

This is a course on networks and text in quantitative social science. The course will cover published research using text and social network data, focusing on health, politics, and everyday life, and it will introduce methods and approaches for incorporating high-dimensional data into familiar research designs. Students will evaluate past studies and propose original research.

This graduate seminar is intended to acquaint students with critical issues in designing and executing social research. The goal of the course is to equip students to make their own analytically sound contributions to social research about technology, while critically considering their own roles as social scientists. Among the topics the course is likely to cover are: formulating good research questions and fitting appropriate methods to them; practical aspects of data collection; forms of validity; critical assessment of constructs and classifications; the roles of failure and surprise in social research; and positionality and the researcher's relationship to social policy.

More and more of life is now manifested online, and many of the digital traces that are left by human activity are increasingly recorded in natural-language format. This research-oriented course examines the opportunities for natural language processing to contribute to the analysis and facilitation of socially embedded processes. Possible topics include sentiment analysis, learning social-network structure, analysis of text in political or legal domains, review aggregation systems, analysis of online conversations, and text categorization with respect to psychological categories.

This course will equip students to conduct causal research, with an emphasis on observational settings. The course focuses on defining a causal question, defending causal assumptions, and estimating the target quantity. These steps will be discussed in the potential outcomes framework and the structural causal modeling framework. A theme of the course is that one should design an observational study to mimic a hypothetical randomized experiment. Students will leave the course prepared to evaluate the credibility of causal claims, answer causal questions in their own research, and engage with new methods for causal inference.

Robot and automated systems often need to move in and around people. This technical and human-centered course surveys current physical, computation and sensing technologies that make mobile human robot interaction possible, and delves into key research problems in interaction, human modelling, and repair which are needed to tackle the grand challenge of mobile HRI. As part of this course, students will build working prototypes of mobile robots, and deploy them for study in human environments. The 5000-level version of this course focuses on platform development; the 6000-level version of this course focuses on the production of human participant research studies using mobile HRI.

Information networks such as the World Wide Web are characterized by the interplay between heterogeneous content and a complex underlying link structure. This course covers recent research on algorithms for analyzing such networks and models that abstract their basic properties. Topics include combinatorial and probabilistic techniques for link analysis, centralized and decentralized search algorithms, generative models for networks, and connections with work in the areas of social networks and citation analysis.

Study of topics not currently covered in INFO offerings, as determined by faculty and student interest.

This seminar will showcase a variety of methods common to global development research across disciplines - from machine learning to statistics to economics to public health - and will instruct on both (a) reading out-of-discipline papers and (b) writing for a multidisciplinary audience. Weekly lectures will each focus on a different sustainable development goal through an academic lens, and will include guided paper discussions, student presentations, and featured guest lecturers spanning academia, non-profits, and NGOs.

This seminar focuses on the societal perspectives on digital technology. Speakers will come from a range of disciplines including computer science, data science, engineering, OR, humanities, social sciences, law, and policy and will present work in progress that considers ethical and political questions related to the design, development, and deployment of digital, computational, and information systems and devices. Topics include accountable algorithms, bias in machine learning, AI and the workplace, privacy, and cybersecurity.

For staff, visitors, and graduate students interested in information science.

PhD-level seminar on special topics in computer systems.

Independent research for MPS students and pre-A exam Ph.D. Students.

This seminar is designed to help orient incoming Information Science PhD students to intellectual, professional, and practical aspects of getting a PhD in information science at Cornell. This includes helping students become more familiar with people, projects, and useful resources in information science and related disciplines at Cornell; developing a sense of the broader information science field and related academic communities; and to support cohort-building among incoming students.

Thesis research for post-A exam Ph.D. Students.