Information Course Schedule Spring 2025

Three hours of lecture per week. Methods and concepts of creating design requirements and evaluating prototypes and existing systems. Emphasis on computer-based systems, including mobile system and ubiquitous computing, but may be suitable for students interested in other domains of design for end-users. Includes quantitative and qualitative methods as applied to design, usually for short-term term studies intended to provide guidance for designers. Students will receive no credit for 114 after taking 214.

This course is a survey of web technologies that are used to build back-end systems that enable rich web applications. Utilizing technologies such as Python, FastAPI, Docker, RDBMS/NoSQL databases, and Celery/Redis, this class aims to cover the foundational concepts that drive the web today. This class focuses on building APIs using microservices that power everything from content management systems to data engineering pipelines that provide insights by processing large amounts of data. The goal of this course is to provide an overview of the technical issues surrounding back-end systems today and to provide a solid and comprehensive perspective of the web’s constantly evolving landscape.

This course introduces students to natural language processing and exposes them to the variety of methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and helps drive much contemporary work in text analysis (as used in computational social science, the digital humanities, and computational journalism). We will focus on major algorithms used in NLP for various applications (part-of-speech tagging, parsing, coreference resolution, machine translation) and on the linguistic phenomena those algorithms attempt to model. Students will implement algorithms and create linguistically annotated data on which those algorithms depend.

Introduces the data sciences landscape, with a particular focus on learning data science techniques to uncover and answer the questions students will encounter in industry. Lectures, readings, discussions, and assignments will teach how to apply disciplined, creative methods to ask better questions, gather data, interpret results, and convey findings to various audiences. The emphasis throughout is on making practical contributions to real decisions that organizations will and should make.

This course is designed to be an introduction to the topics and issues associated with information and information technology and its role in society. Throughout the semester we will consider both the consequence and impact of technologies on social groups and on social interaction and how society defines and shapes the technologies that are produced. Students will be exposed to a broad range of applied and practical problems, theoretical issues, as well as methods used in social scientific analysis. The four sections of the course are: 1) theories of technology in society, 2) information technology in workplaces 3) automation vs. humans, and 4) networked sociability.

This course uses examples from various commercial domains — retail, health, credit, entertainment, social media, and biosensing/quantified self — to explore legal and ethical issues including freedom of expression, privacy, research ethics, consumer protection, information and cybersecurity, and copyright. The class emphasizes how existing legal and policy frameworks constrain, inform, and enable the architecture, interfaces, data practices, and consumer facing policies and documentation of such offerings; and, fosters reflection on the ethical impact of information and communication technologies and the role of information professionals in legal and ethical work.

This course addresses concepts and methods of user experience research, from understanding and identifying needs, to evaluating concepts and designs, to assessing the usability of products and solutions. We emphasize methods of collecting and interpreting qualitative data about user activities, working both individually and in teams, and translating them into design decisions. Students gain hands-on practice with observation, interview, survey, focus groups, and expert review. Team activities and group work are required during class and for most assignments. Additional topics include research in enterprise, consulting, and startup organizations, lean/agile techniques, mobile research approaches, and strategies for communicating findings.

Discusses application of social psychological theory and research to information technologies and systems; we focus on sociological social psychology, which largely focuses on group processes, networks, and interpersonal relationships. Information technologies considered include software systems used on the internet such as social networks, email, and social games, as well as specific hardware technologies such as mobile devices, computers, wearables, and virtual/augmented reality devices. We examine human communication practices, through the lens of different social psychology theories, including: symbolic interaction, identity theories, social exchange theory, status construction theory, and social networks and social structure theory.

The design and presentation of digital information. Use of graphics, animation, sound, visualization software, and hypermedia in presenting information to the user. Methods of presenting complex information to enhance comprehension and analysis. Incorporation of visualization techniques into human-computer interfaces. Three hours of lecture and one hour of laboratory per week.

Provides a theoretical and practical introduction to modern techniques in applied machine learning. Covers key concepts in supervised and unsupervised machine learning, including the design of machine learning experiments, algorithms for prediction and inference, optimization, and evaluation. Students will learn functional, procedural, and statistical programming techniques for working with real-world data.

This course is a survey of web technologies that are used to build back-end systems that enable rich web applications. Utilizing technologies such as Python, Flask, Docker, RDBMS/NoSQL databases, and Spark, this class aims to cover the foundational concepts that drive the web today. This class focuses on building APIs using micro-services that power everything from content management systems to data engineering pipelines that provide insights by processing large amounts of data. The goal of this course is to provide an overview of the technical issues surrounding back-end systems today, and to provide a solid and comprehensive perspective of the web’s constantly evolving landscape.

The course overviews a broad number of paradigms of privacy from a technical point of view. The course is designed to assist system engineers and information systems professionals in getting familiar with the subject of privacy engineering and train them in implementing those mechanisms. In addition, the course is designed to coach those professionals to critically think about the strengths and weaknesses of the different privacy paradigms. These skills are important for cybersecurity professionals and enable them to effectively incorporate privacy-awareness in the design phase of their products.

This course will cover the principles and practices of managing data at scale, with a focus on use cases in data analysis and machine learning. We will cover the entire life cycle of data management and science, ranging from data preparation to exploration, visualization and analysis, to machine learning and collaboration, with a focus on ensuring reliable, scalable operationalization.

This course introduces students to natural language processing and exposes them to the variety of methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and helps drive much contemporary work in text analysis (as used in computational social science, the digital humanities, and computational journalism). We will focus on major algorithms used in NLP for various applications (part-of-speech tagging, parsing, coreference resolution, machine translation) and on the linguistic phenomena those algorithms attempt to model. Students will implement algorithms and create linguistically annotated data on which those algorithms depend.

This course will cover new interface metaphors beyond desktops (e.g., for mobile devices, computationally enhanced environments, tangible user interfaces) but will also cover visual design basics (e.g., color, layout, typography, iconography) so that we have systematic and critical understanding of aesthetically engaging interfaces. Students will get a hands-on learning experience on these topics through course projects, design critiques, and discussion, in addition to lectures and readings. Two hours of lecture per week.

This course provides students with real-world experience assisting politically vulnerable organizations and persons around the world to develop and implement sound cybersecurity practices. In the classroom, students study basic theories and practices of digital security, intricacies of protecting largely under-resourced organizations, and tools needed to manage risk in complex political, sociological, legal, and ethical contexts. In the clinic, students work in teams supervised by Clinic staff to provide direct cybersecurity assistance to civil society organizations. We emphasize pragmatic, workable solutions that take into account the unique needs of each partner organization.

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

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

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

Biosensory computing is the multidisciplinary study and development of systems and practices that sense, represent, communicate, and interpret biological signals from the body.

Biosignals are expansive in scope, and can enable a diverse range of biosensory computing applications. They can include physiological (e.g., ECG/PPG, EDA, EEG) and kinesthetic signals (e.g., accelerometry, eye gaze, facial expressions). Many inferences can be drawn about the person from these signals, including their activities, emotional and mental states, health, and even their identities, intentions, memories, and thoughts.

While generated by the person, biosensory data have important characteristics that distinguish them from other types of user-generated data. They are intimate yet leakable, precise yet ambiguous, familiar yet unverifiable, and have limited controllability. Therefore, responsible stewardship of biosensory data must be in place before the full potential of biosensory computing can be realized.

This multidisciplinary course will explore the intellectual foundations and research advances in biosensory computing. We will survey the range of biosensing modalities and technologies, study temporal and spectral data analysis and visualization techniques, interrogate the designs of novel biosensing applications, and tackle issues of user privacy and research ethics. Students signing up for the 3-unit option will continue in the second half of the semester with a student-led research project.

This course introduces the theoretical and practical aspects of computer vision, covering both classical and state of the art deep-learning based approaches. This course covers everything from the basics of the image formation process in digital cameras and biological systems, through a mathematical and practical treatment of basic image processing, space/frequency representations, classical computer vision techniques for making 3D measurements from images, and modern deep-learning based techniques for image classification and recognition.

An intensive weekly discussion of current and ongoing research by Ph.D. students with a research interest in issues of information (social, legal, technical, theoretical, etc.). Our goal is to focus on critiquing research problems, theories, and methodologies from multiple perspectives so that we can produce high-quality, publishable work in the interdisciplinary area of information research. Circulated material may include dissertation chapters, qualifying papers, article drafts, and/or new project ideas. We want to have critical and productive discussion, but above all else we want to make our work better: more interesting, more accessible, more rigorous, more theoretically grounded, and more like the stuff we enjoy reading.

Topics in information management and systems and related fields. Specific topics vary from year to year. May be repeated for credit, with change of content. May be offered as a two semester sequence.

Discussion, reading, preparation, and practical experience under faculty supervision in the teaching of specific topics within information management and systems. Does not count toward a degree.