Students will submit a capstone proposal to their intended faculty capstone advisor before the dates indicated on the form. Once the capstone advisor approves the proposal, the form will route to the Recorder who will add permission to enroll within 48 hours. Students should also complete the intent to graduate form at this time.
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Faculty Advisors
Ideally, you want to work with a faculty whose expertise aligns with your academic interests, experience, and career path. Faculty availability is not guaranteed, depending on the faculty’s other assignments and duties. Plan your capstone early and thoroughly. Here is the list of faculty advisors in the Informatics program.
Fawzi BenMessaoud
Students are given the opportunity to select from real-world or from various community organizations projects. The students are mentored on how to best apply all their acquired skills, academic knowledge, and use of the Systems Development Life Cycles (SDLCs) and sound Informatics project management to scope, design, develop, and present their completed project. Students are mentored mainly in two types of Capstone projects; (1) Digital Transformation (DX) and the design, development, use, and leverage of Technology and Computing to harness the power of the data to solve problems or explore opportunities in all aspects of our lives, including software development, technology integration, the use of APIs with apps and web applications, and data analytics and visualization; (2) The applications of modern Ai models and bot platforms such as the implementation of Robotic Process Automation (RPA) to deploy a digital workforce or the implementation of cognitive bots to improve the user interface/experience (UI/UX) and increase the throughputs in business, organization, and technology by improving user experience and business insights.
Erin Brady
Students will identify real-world accessibility problems or issues through direct interaction with people with disabilities, and then develop an information system as the solution to solve the problems or address the issues. Students will be responsible for identifying participants with disabilities, conducting formative research using ability-appropriate data collection methods, developing personas or portraits to represent participant needs, and then generating accessible design artifacts and soliciting user feedback. Prerequisites: INFO-I481 Experience Design and Evaluation of Access Technologies.
Francesco Cafaro
Students will collaborate in the activities of the Interactive Embodied Spaces (IES) Laboratory. They will contribute to the design, implementation, and evaluation of interactive data visualizations for informal learning. Activities will be coordinated with the instructor, and may include working with Unity and C#, leading user studies, conducting statistical analysis of quantitative data and thematic analysis of qualitative data.
Sunandan Chakraborty
Students will identify a problem and devise a solution to the problem using available datasets or curating a dataset. The data-driven solution should involve techniques from (but not limited to) machine learning, deep learning, and natural language processing. The students are expected to visualize their results and validate their findings through a sound evaluation plan. The problem should ideally address a real-world issue, preferably within the domains of agriculture, health, education, and sustainability.
Ran Chang
Option 1: This course assumes prior knowledge and experience with procedural and object-oriented programming in PHP, designing information systems with the MVC pattern, and developing dynamic, data-driven web applications with server-side technologies and relational databases. Prior to taking this course, students are expected to complete INFO I211 Information Infrastructure II with a C or better grade or have acquired equivalent experiences, and INFO-I425 Applications of Web Services.
Option 2: If the students are interested in the Data Mining related project topic, INFO-I223 Data Fluency and INFO-I421 Applications of Data Mining is required. And you need to be familiar to Python or R programming language.
Andrea Copeland
Students will partner with public libraries, archives, and community organizations to design data-for-good solutions that improve local services and civic engagement. Mentoring emphasizes community engagement values and strategies, stakeholder co-design, responsible data practices, and delivering results that non-technical audiences can use.
Leon Johnson
Students will identify a human-centered problem that can be addressed using data analytics, and build a solution that incorporates algorithmic fairness and data ethics. The project may apply techniques from machine learning or natural language processing, but it must pay explicit attention to mitigating bias. A key aspect of this project will be the creation of a publicly accessible interactive visualization or web app. Students will validate their solutions using both traditional performance metrics and fairness-specific measures, ensuring practical accuracy, equity, and ethical soundness.
Angela Murillo
Students will have the opportunity to pursue projects in data analysis, statistical analysis, natural language processing, and other data science methods, such as machine learning and neural networks. They will be required to identify existing data or generate their own data for their projects and utilize this information to analyze and visualize it, addressing a specific question or engaging with a particular community. Projects should focus on clearly communicating findings and linking results to human impacts, as well as considering social and ethical contexts.
Additionally, students may pursue projects related to research infrastructure, particularly those involving data generated at large-scale scientific facilities. These projects will involve gaining an understanding of the data and cyberinfrastructure utilized at scientific research facilities and exploring various solutions for the capturing, organizing, analyzing, archiving, and disseminating of these complex data.
Bryan Stephens
Students may pursue projects that combine technical depth in statistical learning or deep learning with community-engaged applications. Possible directions include developing predictive models (e.g., neural networks, supervised/unsupervised learning), conducting program evaluation or digital inclusion studies, and creating interactive data visualizations or dashboards that lower barriers to accessing knowledge. Many projects emphasize data storytelling and applied partnerships with mission-driven organizations, allowing students to connect advanced analytics with real-world impact.
Austin Stroud
Students will have the opportunity to design projects at the intersection of programming, libraries, and information systems. Projects may involve applying Python and other high-level programming languages to data curation, metadata processing, or digital services challenges. Students may also focus on public library technology, information access, or the responsible use of AI in information systems. Work may include developing scripts or applications to automate tasks, cleaning and organizing real-world data, or exploring the ethical implications of AI in professional practice.
Louie Zhu
Prerequisite: INFO-I 211
Students will identify real-world problems or issues and then develop an information system as the solution to solve the problems or address the issues. The information system must be designed with the MVC pattern. The underlying business logic must be modeled with OOP objects. The data layer can be implemented with relational databases or RESTful APIs. The application must be capable of performing CRUD operations, authenticating, and authoring users.