PhD: Information Science

You belong here.

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?

Shamika Klassen

“I have really appreciated being a part of the information science department. Between faculty and my peers, there is such a collaborative and encouraging spirit. Everyone is supportive and I truly feel like a valuable part of our community.”

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."

Mikhaila Friske

"I love the community. The grad students have a range of backgrounds from computer science to anthropology to English. Everyone can offer a unique perspective while being really supportive of your interests."

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).

Cho, Janghee, 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.
Klassen, Shamika, 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.
Robledo Yamamoto, Fujiko, Amy Voida, and Stephen Voida. "From therapy to teletherapy: Relocating mental health services online." Proceedings of the ACM on Human-Computer Interaction 5, no. CSCW2 (2021): 1-30.
Scheuerman, Morgan Klaus, Jialun Aaron Jiang, Casey Fiesler, and Jed R. Brubaker. "A framework of severity for harmful content online." Proceedings of the ACM on Human-Computer Interaction 5, no. CSCW2 (2021): 1-33.
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.
Wirfs-Brock, Jordan, Alli Fam, Laura Devendorf, and Brian Keegan. "Examining Narrative Sonification: Using First-Person Retrospection Methods to Translate Radio Production to Interaction Design." ACM Transactions on Computer-Human Interaction (TOCHI) 28, no. 6 (2021): 1-34.
Yu, Junnan, Julisa Granados, Ronni Hayden, and Ricarose Roque. "Parental Facilitation of Young Children's Technology-based Learning Experiences from Nondominant Groups During the COVID-19 Pandemic." Proceedings of the ACM on Human-Computer Interaction 5, no. CSCW2 (2021): 1-27.

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 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

Kandrea Wade (Twitter)

"My experience as a Health Research Intern at Twitter was absolutely amazing. The teams that I worked on and with were all very welcoming and made me feel like a valuable part of the team. My time at Twitter was both a personal and professional growth and learning experience, and I will take what I learned with me to all future academic and industry positions."

Nasim Sonboli (Max Planck Institute)

"Joining the Responsible Computing Group at Max Planck Institute for Security and Privacy, and collaborating with the research scientists there, helped me in looking at the topic of fairness in recommender systems from another perspective. I specifically loved that this internship was in academia since I had the freedom to choose the topic of my interest which is also aligned with my thesis topic. This Internship has also helped me in expanding my technical skills and has led to on-going collaborations."

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 must also have a TOEFL score of 75 or an IELTS score of 6.0.

The director of graduate studies, 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: 303-492-7977

Email: cmcigrad@colorado.edu

Contact the Department of Information Science

Department of Information Science:

General Inquiries
Email

PhD Program Inquiries
Email

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

303-735-7581

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