Maybe, depends on my capacity and our research alignment.

To clarify, at the School of Journalism and Media, the admission of PhD/MA students is determined collectively by the search committee members. I am not serving on the admission committee for Fall 2026. If you want to work with me, please list my name as your top choice of advisor on the application SOP. If you have further questions, please reach out at least three weeks prior to the application due date. 

Note: While my work spans both quantitative and qualitative research, I am currently working with students who are interested in computational and quantitative paradigms. 

If you would like to invite me to serve on your MA thesis/PhD dissertation committee, please include the following information in your initial email to help me assess whether I can provide meaningful support to your work.   

• Your full name and program: Include your name, affiliation, current year, and the name of your advisor. Please indicate your advisor's approval of the proposed committee composition. If you are an externally affiliated student, please check any institutional guidelines or eligibility criteria for committee membership before contacting me.

• Project description: A paragraph (3-4 sentences) of your thesis/dissertation, with clear research questions and methods to use.

• How I may help: A two-sentence explanation of how I will be helpful to you.  

• Anticipated timeline for proposal/final defense

• Any relevant documents (optional): You may attach your proposal (draft or finalized), CV, or other relevant materials if available.  

Absolutely. There are several opportunities for you to work with me, potentially:

• Graduate research assistant (GRA): I am looking for a full-time (20hrs/week) GRA for the summer of 2026. Only JaM doctoral candidates are qualified. I expect you to be interested in news behaviors, user perceptions, or platform algorithms. If you are interested in this role, please reach out to me by February 2026. I am not currently hiring GRAs during semesters.

• Ad hoc research assistant: Some of my ongoing projects may involve students to assist with specific tasks. This role is not limited to UT students or those at the graduate level. If you want to join, please send me an email with your CV and a one-paragraph summary outlining which aspects of my work specifically interest you, as well as any relevant research experience or skills you have had. A few notes before you reach out:

  • Time commitment is a prerequisite. Before you reach out, I expect you to be willing to devote a substantial amount of time to the project (minimum 5-10 hours per week, and continue for at least 12 weeks).

  • Based on the task, your contribution will be either acknowledged and compensated through payment, or you will be listed as a co-author on the paper/final product without monetary compensation.

• Support for student-led projects: I am happy to provide feedback to student-led research projects. A few rules for research inquiries:

  • If you like to further develop a class project that originated from my class, feel free to reach out and follow up. To get feedback, you email me with the proposal/manuscript and specific questions (i.e., where you want my eyes on) or come to my office hour to discuss (by appointment).  Due to limited availability, please allow 3-4 weeks for a detailed email response, or 3 weeks for me to review materials before a scheduled meeting. Please note that feedback does not automatically imply co-authorship.

  • I encourage students to publish independently. I do not take co-authorship on student-led projects unless these conditions are met: (1) you formally ask me to be a co-author with legit reasons; (2) the project is closely aligned with my interest and expertise; (3) I am making significant input to the project (at least one of the four sections: theorization, design, analysis, and writing); (4) your advisor is aware of this collaboration.

  • In cases where I am expected to be a co-author, an initial meeting is necessary to specify expectations, timelines, and credit prior to the collaboration, with the agreement of all stakeholders on this project. 

Thanks for your enthusiasm in this class! Please contact Luisa Cantu, the Senior Academic Program Coordinator of JaM, at luisa.cantu@austin.utexas.edu to enroll in this class. Since we are constrained by the size of the classroom, once the class is full, you can continue to check the enrollment and see if an open spot becomes available if someone drops out. 

It would be great if you are familiar with statistics or coding skills, but it is not a prerequisite. The class is designed to introduce students from journalism and communication backgrounds to computational methods and data science. It is more than just a technical tutorial of cool, trending computational methods. One primary goal of this class is to understand the questions that computational methods can answer and how they address them. The primary coding language used in class is R, and AI-assisted approaches will be introduced.

• A note to qualitative students: Don't let the codes deter you. If you have been curious about computational methods, and you believe they can benefit your research, this is the class for you.