PhD: Information Science

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At CU, students are as important as the problems they solve.

Diverse Students

Information science is a broad discipline and our students come from a range of backgrounds. Some have computer science degrees and come prepared to take on information science with a toolkit of computational and data science methods. Some have little to no experience in computing but have backgrounds in social science or qualitative research. Some come to us with a rich knowledge of domains (e.g., public health, education or journalism) to which information science research and techniques can be applied. Some come straight out of undergraduate programs, others have already completed graduate degrees, and others have years of professional experience.

Our program is designed to help PhD students tailor their education towards their own research interests and skill sets while developing a shared body of knowledge around methods, computational techniques, theoretical frameworks and design practices.

Student Voices: Why CU?

Dylan Thomas Doyle

“As a second career PhD student, the Information Science Program at CU has empowered me to grow in both personal and professional ways. The faculty I have been able to work with are empathetic and brilliant. They push me to be a more effective scholar and give me the freedom to shape my impact on the field.”

Erin Robinson

"I looked for a long time to find a graduate program that was the right fit to combine my interest in how science communities collaborate around information. The students and faculty here bring together diverse ways of knowing and expertise. This has been a productive and supportive place for me to learn and grow as researcher. It is also exciting to be in a place that I can apply what I learn so rapidly to my research."

Dipto Das

"I love the interdisciplinary nature of the information science department at CU Boulder! The community is inclusive and open to new ideas. Together we all work on important and interesting topics around people and technology."

Samantha Dalal

"The CU Boulder information science department truly embodies the principles of an intersectional education. I have found mentorship in both quantitative data analytics skills and qualitative research methods all within a deeply supportive academic community. This department is an ideal environment to grow as a scholar engaged in applied topical research."

Research a mile above ordinary.

Our core areas of research span topics of:

Collaboration
Algorithmic Fairness
Data Science and Analytics
Ethics and Policy
Design and Human-Computer Interaction
Learning
Social Computing

Learn more about the research groups led by our faculty.

Learn more about the research interests of both our faculty and current PhD students on their profile pages.

In recent years, our PhD students have published and presented work in venues such as CHI (human-computer interaction), AAAI (artificial intelligence), CSCW (collaboration & social computing), FaaCT (fairness and accountability), Ubicomp (ubiquitous computing), AOIR (internet research), DIS (interaction design) and SIGCSE (computer science education). Below is a small selection of student work:

Amanda Aird, Cassidy All, Paresha Farastu, Elan Stefancova, Joshua Sun, Nicholas Mattei, Robin Burke. "Exploring Social Choice Mechanisms for Recommendation Fairnes in SCRUF." In ACM RecSys 2023.
Katy Weathington, Jed R. Brubaker. "Queer Identitities, Normative Databases: Challenges to Capturning Queerness on Wikidata." Proceedings of the ACM on Human-Computer Interaction. Volume 7, Issue CSCW1, Article No.: 84, pp 1–26. 2023.
Jessie J. Smith, Anas Buhayh, Anushka Kathait, Pradeep Ragothaman, Nicholas Mattei, Robin Burke, Amy Voida. "The Many Faces of Fairness: Exploring the Institutional Logics of Multistakeholder Microlending Recommendation." FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 1652-1663. 2023.
Ellen Simpson, Bryan Semaan. "Rethinking Creative Labor: A Sociotechnical Examination of Creativity & Creative Work on TikTok." CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. April 2023. Article No.: 244. pp. 1–16.
Janghee Cho, Tian Xu, Abigail Zimmermann-Niefield, and Stephen Voida. "Reflection in Theory and Reflection in Practice: An Exploration of the Gaps in Reflection Support among Personal Informatics Apps." In CHI Conference on Human Factors in Computing Systems, pp. 1-23. 2022.
Shamika Klassen, Sara Kingsley, Kalyn McCall, Joy Weinberg, and Casey Fiesler. "More than a Modern Day Green Book: Exploring the Online Community of Black Twitter." Proceedings of the ACM on Human-Computer Interaction 5, no. CSCW2 (2021): 1-29.
Simpson, Ellen, Andrew Hamann, and Bryan Semaan. "How to Tame" Your" Algorithm: LGBTQ+ Users' Domestication of TikTok." Proceedings of the ACM on Human-Computer Interaction 6, no. GROUP (2022): 1-27.
Smith, Jessie J., Saleema Amershi, Solon Barocas, Hanna Wallach, and Jennifer Wortman Vaughan. "REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research." In 2022 ACM Conference on Fairness, Accountability, and Transparency, pp. 587-597. 2022.
Sonboli, Nasim, Robin Burke, Michael Ekstrand, and Rishabh Mehrotra. "The multisided complexity of fairness in recommender systems." AI Magazine 43, no. 2 (2022): 164-176.

Across our department, there are many research projects happening at any given point, covering a wide range of domains and methods. Examples of funded projects in our department include research involving AI education, building computational tools to help scale qualitative data analysis, computer-mediated communication during crisis, post-mortem technology design, organizational behavior in e-sports teams, research ethics for social computing, ethics in technology design, gender diversity in computing education, real-world machine learning, fairness-aware recommender systems, humanizing algorithms, fair recommendation through social change, designing to overcome the asymmetry problem in computer and information science service and vaccine misinformation on social media.

Members of our faculty sometimes seek PhD prospective students with an interest in specific research areas or in specific funded projects. Advisor fit is also a critical part of PhD admissions, so we highly recommend that you look into the research areas of specific faculty and their research labs and consider if there is a good potential match for you. Additionally, depending on capacity and timing, some faculty members might be more actively recruiting than others. You should feel free to reach out to faculty members individually to inquire about research areas and advising capacity.

Student Voices: Fellowships & Industry Research Internships

Laurie Jones (Los Alamos National Lab)

"This summer I worked at the Los Alamos National Laboratory as a graduate student researcher. It was really exciting because I was able to work hands on within the government lab and get a glimpse of what that entails. It was great to see all the diverse work that was being done and how my interests were reflected in that. I came out having pushed myself to achieve our project goals and motivated to bring that into my personal research. There was also a fun connection because the people I worked with had collaborated with a past CU Information Science PhD!"

Shamika Klassen (UX Design Research Intern, Substantial)

"At Substantial, I conducted research for an Ed-Tech company including interviews and a co-design session to distill insights on how early faculty can be supported in teaching underserved STEM undergraduates. I also worked with the other intern on a capstone project to create an Equity-Centered Design Maturity Assessment Tool. In addition to research, I received professional development as a burgeoning UX Researcher and facilitated a workshop on the Black Mirror Writer’s Room Exercise with the design team. Overall, I thoroughly enjoyed my first industry internship and look forward to collaborating with them again in the future."

Jessie Smith (Google Fellowship)

"I am so grateful to have been awarded a PhD fellowship from Google for my research on fairness and transparency in recommender systems. Getting funded from big tech gives me a great chance to show that AI ethics research is important outside of academia, and gives me an opportunity to tap into the industry while completing my PhD."

Janghee Cho (Meta Fellowship)

"I am honored to have been awarded the Meta PhD Research Fellowship. Meta offered me a Fellowship with the AR/VR Future Technologies team to conduct studies to envision future technologies for remote workers’ wellbeing. I’m excited to generate new ideas on how AR/VR technologies can be situated in a home with a focus on social interaction for two years."

PhD Requirements

Application Deadlines

US applicants:
Dec. 1

International applicants:
Dec. 1

How to Apply

The PhD in Information Science is for students who want to engage in empirical investigations of interdisciplinary problems. Students in the PhD program will learn a diversity of methods, theoretical frameworks, design practices and computational techniques. A PhD student's scholarly practice will include collaborative research on grant-driven projects.

The scholarly skills required of PhDs in information science are fundamentally analytical, creative, interdisciplinary and in constant interaction with information that is generated, manipulated and transformed within and across domains. The PhD aligns culturally with the grant-driven, collaborative “lab model” of research that characterizes the natural and engineering sciences, but is nevertheless deeply integrative of the social sciences and humanities in its scholarly pursuit and intellectual contributions.

The PhD program in information science requires a minimum of 30 course credit hours and 30 thesis credit hours. Students are encouraged to take courses outside of the department beyond any departmental foundation courses pending approval by their advisor and the graduate committee. After a written and oral preliminary exam, students go on to specialize in a dissertation area in consultation with their committees. Completion of the PhD will take approximately five years.

See INFO PhD program handbook for more information.

Bachelor’s degree or its equivalent
We encourage applications from individuals representing the broad range of disciplines that bring fundamental skills and insights to bear on the range of issues related to understanding and shaping a future of information science as envisioned above. However, all students admitted to the program will be expected to develop a breadth of competencies (including empirical, computational and designerly competencies) that are essential to being a researcher in this diverse, interdisciplinary field. One’s ability and willingness to expand skillsets should be demonstrated in the statement of purpose.
CV or resume
Statement of purpose (maximum of two pages):
- Your statement of purpose should describe a question, problem or topic in information science that you have a passion to address.
- Please describe how your previous academic training, professional experience and/or personal passions have led you to this question, problem or topic and have drawn you to this degree program.
- Identify the faculty members with whom you would be interested in working and why.
Undergraduate GPA of at least 3.20, and graduate GPA of 3.50 if any prior graduate coursework was taken.
One unofficial transcript from each college or university attended.
Three letters of recommendation from people qualified to judge your potential for success in graduate school.
- The most compelling letters will provide specific observations about the candidate’s promise in analytical thinking, scientific communication (both oral and written), research and teaching, as well as a demonstration of teamwork and collegiality.
GRE scores are not required for students applying to our graduate program. Applicants are welcome to submit a general GRE score but will not be at a disadvantage if they choose not to.
Optional: A writing sample in addition to the statement of purpose.
International applicants should also have a TOEFL score of 100, or an IELTS score of 7.0, or a DuoLingo of 120.

Faculty member, Casey Fiesler, maintains a playlist of YouTube videos with general advice for PhD applications. Though this advice is not specific to our department, you may find it helpful.

It is also not uncommon for applicants to reach out to specific faculty advisors prior to submitting their application in order to inquire about current research interests and advising capacity. You can find contact information for faculty on their profile pages.

Phone: 303-735-0730

Email: cmcigrad@colorado.edu

Contact the Department of Information Science

Department of Information Science:

General Inquiries: Email

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CMCI Department of Information Science (INFO)
1045 18th Street, UCB 315
Boulder, CO 80309

303-735-7581

PhD: Information Science

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Diverse Students