Prospective PhD students who are interested in working with me can apply to UMD iSchool's PhD in Information Studies program and mention my name in your application. If you have a background or an interdisciplinary background in information science (esp. those from informatics, HCI, AI, data science, computational social science tracks) / (social) psychology / (quantitative) communication / computer science (with interests in social science) / statistics or mathematics (with interests in social science) / public health / other related fields, I welcome your application. 

Admissions decisions are made collectively by the iSchool community. You can (but you don't have to) email me prior to your application, and I typically do not speak to candidates in advance of the admissions process - this allows me to treat candidates equitably during the official review process. I aim to review all applications that mention my name and pass the initial screening by the admissions committee, and will invite those shortlisted candidates for an interview (around late December/early January). Please do not email to request an interview—interviews are by invitation only, and I am not able to accommodate individual requests outside of this process. Similarly, after submitting your application, there is no need to email me to highlight or “boost” your application. Such messages do not influence the review process, and I am not able to respond to or consider additional materials outside of the official application. I’m also unable to provide feedback or evaluation on individual application materials in advance. These policies help ensure fairness and equal consideration for all applicants. I also cannot answer whether an applicant will be accepted or not before you apply, as individual faculty members do not make admissions decisions on our own.

Please check these links for more information on PhD application via UMD iSchool and graduate school: 

https://ischool.umd.edu/academics/phd-information-studies/admissions; https://ischool.umd.edu/academics/phd-information-studies/; https://gradschool.umd.edu/funding/assistantship-information; https://gradschool.umd.edu/funding/student-fellowships-awards  

If you are not sure about whether doing a PhD is right for you, I encourage you to read these recourses closely before you decide:

"PhD in pictures" by Matthew Might

"Do you need a PhD" and "So, you want to go to grad school" by Matt Welsh

"What I wish I knew when I switched fields for my PhD" by Sterling Williams-Ceci 

"Notes on the PhD degree" by Douglas Comer

During your PhD journey, I'm here to support you as you explore ideas, critically engage with theories and literature, rigorously design and conduct data analysis for your research projects, and define and pursue your own research trajectory and career path. PhD study is a time to develop intellectual independence, and my role is to support, guide, and challenge you as you shape your scholarly identity. I work collaboratively with my advisees to create new knowledge together and to address important problems that matter to both of us.

I aim to provide consistent and flexible support for my PhD advisees. In general, I am available to meet weekly or biweekly, depending on your needs and the stage of your work. I value both my time and yours, and believe our meetings should be as frequent and as long as is productive, but no more than that. Regular check-ins should serve to maintain momentum, address challenges, and support your intellectual and professional development. To help ensure our advising and research meetings are focused and efficient, I strongly encourage students to prepare and share a brief agenda with me before each meeting. These meetings can cover progress updates, brainstorming, feedback, or anything else that feels relevant - including roadblocks - whether academic, logistical, or beyond. I’d much rather hear about a potential issue early, even if it turns out to be minor, than too late when it’s harder to address. At the end of each meeting, you should take a moment to confirm key action items, next steps, and expectations so that we are on the same page moving forward. Open, ongoing communication helps us stay aligned and allows me to better support you. I also encourage my advisees to set their semester goals (short-term) and 3–5 year goals (medium-term) at the beginning of each semester so that I can better support you based on your goals. Setting clear goals helps provide direction, maintain focus, and track your progress throughout your academic journey. We review the goals together and refine them as needed to ensure they remain realistic and aligned with your evolving aspirations. 

In terms of my expectations for advisees: I expect you to work hard and engage seriously with your research, while also taking care of yourself and allowing time to rest and recharge. Sustainable productivity matters more than constant overwork. I expect you to take feedback seriously and approach it with an open mind—feedback is a core part of the research process, and engaging with it thoughtfully will help you grow. I also expect a strong commitment to ethical research practices and professional integrity in all aspects of your work. Importantly, you need to cultivate your own internal motivation and curiosity. I will support you, check in as needed, and do my best to create an environment where you can thrive—but I cannot force learning or motivation. Your progress ultimately depends on your own initiative and engagement. This means setting and meeting deadlines (or communicating very early when you can't), following through on commitments, and taking genuine ownership of your work: bringing ideas, asking questions, and actively shaping the direction of your research. At the same time, while brainstorming and ideation are important parts of research, they are not sufficient on their own. You should engage deeply with your research projects and consistently move them forward—whether that means extensive reading and writing, hypothesizing, building experimental interfaces, coding, analyzing data, or otherwise doing the substantive work required to make tangible progress. Generating exciting ideas and important questions is valuable, but research ultimately advances through sustained effort and execution. It is also important to recognize that even when you are working on questions you genuinely care about, not every part of the research process will be exciting. Much of research involves tedious and detail-oriented work—such as data cleaning, debugging, or revising drafts—that can feel slow or repetitive. Developing the discipline to work through these less glamorous but essential stages is critical. 

I also want to be transparent about how I provide feedback and support across students. In general, the level and frequency of my engagement will be commensurate with your own input and needs, which naturally vary across PhD stages and goals. Students in coursework-heavy early years, those navigating milestones, and those targeting computing conference proceedings versus social science journals all have different rhythms — and I try to adapt accordingly. I also put in more when you put in more: when you come prepared — whether that means diving deep into the literature and arriving with thoughtful questions, writing and iterating on drafts, building out study infrastructure, running analyses, or wrestling carefully with data — there is simply more for us to work with together. Some students move quickly, bring ideas frequently, and iterate at a fast pace; in those cases, I'll do my best to match that energy. Others may work at a different pace, and that is completely okay as well. What I'd encourage you to avoid is comparing my attention across students without accounting for those differences in your own stage, pace, effort, working style, etc. My goal is to support each of you in a way that is appropriate and sustainable for you, not to distribute my time in a superficially equal way that ignores individual needs. Along the same lines, I strongly discourage unhelpful comparison or competition among advisees. That said, it can be very productive to have role models whose work or progress inspires and motivates you—this kind of positive modeling is healthy and welcome, and is fundamentally different from measuring your own worth or my level of attention against someone else’s; unhelpful comparisons can be counterproductive to both your well-being and your research. Research is not a zero-sum process, peer learning and being collaborative can be incredibly valuable, and a supportive environment benefits everyone.

Even though our program guarantees funding for all admitted full-time students, typically through teaching assistantships, research assistantships, or university fellowships, I strongly encourage my PhD students to look for opportunities and apply for additional internal and external fellowships and research grants throughout their PhD studies. These opportunities include major external awards like the National Science Foundation Graduate Research Fellowship (NSF GRFP; for those eligible), corporate fellowships (e.g., from Microsoft, Google), and dissertation funding such as the National Science Foundation Doctoral Dissertation Research Improvement Grant (NSF DDRIG; I was awarded one during my own PhD study) or the Social Science Research Council (SSRC) fellowships. UMD also offers competitive internal funding through programs like TRAILS and AIM. When I was a graduate student at Cornell and a postdoc at UW, I applied and received multiple competitive internal grants, and those were very rewarding experiences. Preparing these applications helps you clarify your research vision and sharpen your thinking. When you receive an award, it gives you more flexibility in your research, adds a strong credential to your CV, and creates more space for us to collaborate on deepening and refining your ideas. I'm happy to support you through this process, from identifying appropriate opportunities to brainstorming and reviewing drafts. Writing grant proposals is a skill that will serve you well in any research-driven career, and it’s worth developing early.

I support a range of career paths you are interested in pursuing, including those within or outside of academia, and I’m happy to help you think through and prepare for different options. My personal trajectory and experience are best aligned with students aiming for academic research careers.

The best way to contact me is via my work email (and through our advising meetings, of course). I try my best to be responsive and attentive to your needs. My working hours may differ from yours, and you should never feel obligated to reply to messages I send outside of normal work hours. I fully support your efforts to maintain a healthy and sustainable work rhythm.

I personally believe that successful scholarship is sustainable only when it is part of a balanced life. I encourage my students to set boundaries, maintain interests outside of work, and take care of their physical and mental well-being. As part of maintaining healthy boundaries, I prefer not to connect with students on personal social media platforms before you graduate, as I see those spaces as part of your personal life, and prefer to respect that separation. But I encourage you to connect with me on professional platforms such as LinkedIn, where I often share internship / job / fellowship / etc. opportunities with my students.

I also deeply care about my students' well-being and want to be someone you can come to when challenges arise, especially as they relate to your research, academic progress, or professional development. I am happy to listen and help you think through difficult situations. At the same time, there are limits to the kind of support I can provide, and some concerns are best addressed with trained professionals (e.g., licensed therapists). I also encourage students to be mindful that expectations around emotional support can be unevenly distributed, and in academia this work often (though certainly not always) disproportionately falls on female professors. Being aware of this helps us maintain a respectful and sustainable advising relationship.

At the same time, I recognize that advising relationships are not one-size-fits-all. A strong advising relationship often emerges when there is mutual alignment—when you feel excited about the work I do, motivated by the projects we pursue together, and comfortable with my advising and working style. These are all good signs of a productive fit. At the same time, research interests and preferences can evolve over the course of your PhD, and what once felt like a good match may shift. If you ever feel that my advising style, research focus, or working approach isn’t the best fit for you, I encourage you to discuss this with me openly. I understand that these conversations can be difficult, especially given the inherent power dynamics, but I want to be transparent that I am genuinely open to them and will not take them personally. My priority is your growth and success, and I’m happy to help you explore other advising options if that would better meet your needs. Similarly, if I believe another advisor might be a better fit for your work, I will share that honestly and support you through any transition.

As I also acknowledged in my bio page, I have been incredibly fortunate to receive exceptional mentorship from my own academic advisors: Dr. Emma Spiro at UW, Dr. Jeff Niederdeppe at Cornell, Dr. Erin Ash at Clemson. I still maintain regular contact with them, as good mentorship can turn into a lifelong relationship. They were committed to my success; so will I be to my advisees. 

This is intended as a living document, and I update it when needed.

(acknowledgment: some content in this part was inspired by Drs. Jessica Gall Myrick, Mor Naaman, Sarita Yardi Schoenebeck, Eric Gilbert. I encourage you to take a look at these very comprehensive Ph.D. Syllabus from Dr. Eric Gilbert and Ph.D Syllabus from Dr. Mor Naaman. I also encourage you to take a look at this very thoughtful paragraph on checking/recognizing potential gender biases written by Dr. Jessica Myrick.)

If you would like to invite me to serve on your PhD dissertation committee, please include the following information in your initial email. Providing these details upfront will help me assess the alignment between your research and my expertise, and determine whether I can be of meaningful support to your work.   

• Your full name and program: Include your college/school/department, degree program (e.g., PhD in [Program Name]), and advisor(s)' name(s).  

• Dissertation topic / working title: A brief overview or working title of your dissertation. This helps me understand your general area of research.  

• Abstract or research summary (1–2 paragraphs): Please include a short summary of your research focus, including your central research question(s)  

• Methodological approach  

• Key theories or frameworks you’re engaging with (if applicable)  

• Your rationale for inviting me: A brief explanation of why you’re inviting me to join your committee, e.g., how my expertise is relevant to your topic, or what kind of input you hope I can provide.  

• Your anticipated timeline:  Proposal defense, dissertation research/writing, final defense  

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

Please note that I prioritize requests from PhD students within the UMD iSchool (my primary appointment). Depending on my current bandwidth and existing commitments to iSchool students, I occasionally join dissertation committees outside of my college if the research topic is highly relevant to my own work (and I prefer to join on/after your proposal stage, i.e., after your candidacy, if you are outside of my college).

Before you email: Please confirm with your advisor(s) that they approve of the proposed committee composition. Check any institutional guidelines or eligibility criteria for committee membership, especially if I am external to your department or institution.

https://academiccatalog.umd.edu/graduate/policies/doctoral-degrees-policies/#doctoral-dissertation-and-examination 

Additional notes for iSchool PhD students (first-year review committee): If you are considering inviting me to serve on your first-year review committee, we likely already have a good sense of each other’s work through my 801 class. Feel free to reach out after discussing with your advisor. I encourage you to follow your advisor’s recommendations and think carefully about who can best contribute to your research interests. If I believe another faculty member may be a better fit, I will also be happy to suggest alternatives.

iSchool PhD students: I prioritize requests from PhD students within the UMD iSchool (my primary appointment) - just email me and we will have a conversation to explore different options :)

Non-iSchool PhD students: I occasionally accept PhD students (enrolled at my institution) outside of my college to conduct research. I encourage you to review my research page and recent publications on Google Scholar to get a sense of the topics and methods I engage in—methodological fit is especially important for a successful collaboration with me. I highly welcome interdisciplinary collaboration with students with a background in (social) psychology, computer science (with interests in social science), statistics or mathematics (with interests in social science), public health, and other related fields. If I think that we are good fit, I will be happy to supervise your research and will ask you to sign up for a 3-credit independent study with me. That usually amounts to ~10 hours / week. If things go well, we will have concrete outcomes by the end of the semester (e.g., study design finalized, design artifacts, observational data collected and analyzed, written proposal, occasionally even a completed study and a conference paper, etc.). If you reach out, please include your CV and the following information in your email so that I can assess if I can be of meaningful support to your research:

• Your full name and program: Include your college/school/department, degree program (e.g., PhD in [Program Name]), and advisor(s)' name(s).  

• Which research areas (or specific research questions and problems) interest you? What methodologies do you primarily use?

• A working title or brief description of your proposed research

• The research questions or problems you plan to explore

• The methodologies or approaches you intend to use

• Your timeline (e.g., which semester(s) you plan to work on this project)

• How my expertise is relevant to your learning goals, or what kind of guidance you hope I can provide (based on your reading of my research page and my Google Scholar)?

• What do you hope to get out of this collaboration?

• Are you proficient in R and/or Python? If so, what frameworks (ML, NLP, statistical analytics, data visualization, etc.) are you familiar with? If you have sample code from an open source project (not a class project), please send a link. 

• Have you worked with other faculty before on other research projects? If so, summarize what you worked on.

• Have you taken any courses with content in data science, (quantitative) research methods, statistics, NLP, AI, HCI, data/text mining (including any you are currently enrolled in)? Are you familiar with or interested in social science theories (broadly defined)? 

• If you have any research publications (for conferences, journals), please include the doi links and describe your contributions.

Masters/Undergraduate students: I occasionally accept advanced undergraduate and Masters students (preferably enrolled at my institution) to conduct research. If you reach out, please include your CV, your transcripts, and the following information in your email:

• Which research areas (or specific research questions and problems) interest you? What methodology you plan to use or are most interested in learning?

• How do you want to get involved? Depending on your goals, skills, and availability - example roles include:

  1. Independent study or thesis research under my supervision. If this is the case, please provide the following information: 

     • A working title or brief description of your proposed research

     • The research questions or problems you plan to explore

     • The methodologies or approaches you intend to use

     • Your timeline (e.g., which semester(s) you plan to work on this project)

     • Whether this will fulfill any degree or departmental requirements

  2. Research assistant (volunteer or for-credit) for ongoing projects led by me or led by one of my advanced graduate students (depending on availability):

     • This typically involves supporting tasks such as for literature review; study design; data collection, cleaning, analysis; tool development; other technical or analytical support; etc. 

• Why is my research group the right place to conduct this research? How my expertise is relevant to your learning goals, or what kind of guidance you hope I can provide?

• What do you hope to get out of this collaboration?

• Are you proficient in R and/or Python? If so, what frameworks (ML, NLP, statistical analytics, data visualization, etc.) are you familiar with? If you have sample code from an open source project (not a class project), please send a link.

• Have you worked with other faculty before on research? If so, summarize what you worked on.

• Have you taken any courses with content in data science, (quantitative) research methods, statistics, NLP, AI, HCI, data/text mining (including any you are currently enrolled in)?

• If you have any research publications (for conferences, journals), please include these as an attachment and describe your contributions.

I will agree to write letters for students only if I am confident I can write a strong letter that will help your application. This is because a short, thin, or lukewarm letter is more likely to hurt your chances of admission into a program than help them. My letters aim to accurately reflect my interactions with you and provide an honest assessment of your work, abilities, and potential. If I do not feel I can write a strong and supportive letter, I will decline your request. This is not a judgment of your overall potential, but rather a reflection of the extent and nature of our interaction. In such cases, it is in your best interest to ask someone who knows your work more closely and can advocate for you more effectively.

You should give me at least three weeks advance notice, and preferably more.

If you work(ed) with me closely (and ideally for at least 6 months) on research projects that I'm leading or supervising / I led or supervised in the past, you can email me to discuss about your potential LoR requests. 

Q: If I was a student in your class, will you write me a letter of recommendation?

A: Like I said above, I will agree to write letters for students in my classes only if I am confident I can write a strong letter that will help your application. In general, if you did not actively participate in class or work with me outside of class in some manner (e.g., as a TA or participating in research), I would recommend that you ask someone else who knows you better and can speak to your qualities. In other words, I should have some sense of your skills, work ethic, and personality that goes beyond just the grade you received in my class.

I also ask that you meet the following criteria:

• You have fully completed a class with me

• You did well in my class

• You give me at least three weeks advance notice, and preferably more

• You will waive your right to read your letter

In addition to all this, it also matters what the letter of recommendation will be used for. If you are applying to Masters programs or fellowships, meeting the above criteria is generally sufficient. If you are applying to PhD programs, I will write you a letter only if we have worked together in a research capacity.

Q: Do the recommender's connections or institutional ties affect the letter?

A: Students sometimes assume that if I have connections to a program (e.g., institutions I have attended or worked at), this will increase their chances of admission. This is not how letters of recommendation work. A letter is only as strong as the substance behind it. Admissions committees place far more weight on how well I know you and how strongly I can speak to your abilities, work ethic, and potential than on any institutional affiliation I may have. I do not write letters based on perceived “connections." If I cannot write a strong, detailed, and genuinely supportive letter based on my direct experience with you, then any connection I may have to a program will not be meaningful.

(acknowledgment: this part was partially adapted from Drs. Benjamin Mako Hill, Lucy Lu Wang)

Q: How would you describe your work style?

A: I am organized, plan-oriented, and prefer steady progress over last-minute sprints. I work best when expectations, timelines, and responsibilities are discussed early and revisited as needed, which allows me to provide more thoughtful, in-depth feedback and helps reduce unnecessary stress for everyone involved. I do not micromanage my advisees’ day-to-day work and do not expect frequent check-ins throughout the week. Instead, I am hands-on through regular (typically weekly) meetings, where I prefer to consolidate updates, discuss progress, and address questions or challenges. Outside of these check-ins, I am generally hands-off regarding how students organize their work and manage their own timelines, as long as we are aligned on goals and making steady progress toward them.

Q: What are your expectations around conference or journal submissions when working with graduate students?  

A: I strongly prefer advance planning for all submissions. I do not work well with last-minute drafting or editing (e.g., the day before the deadline). Ideally, I would like to receive a complete draft at least 2–3 weeks before the submission deadline. Earlier is always better, especially for journal submissions or first-time conference submissions. This timeline allows me to: Block sufficient time for reading and editing; Provide substantive, not just surface-level, feedback; Allow time for revisions based on my comments.

In the best-case scenario, you will: Propose a submission timeline (e.g., outline → first draft → revised draft → final polish); Indicate when you would like feedback at each stage; Let me know when I should plan to block time for reviewing and editing. A clear plan helps us coordinate our schedules and ensures that the feedback cycle is productive and realistic.

Q: How do you define authorship and how is authorship order determined?    

A: Authorship is based on substantial intellectual contribution to a research project. This typically includes intellectually meaningful involvement in one or more of the following: Developing the research question or theoretical framing; Designing the study or methodology; Collecting, analyzing, or interpreting data; Writing substantial portions of the manuscript; Revising the paper in response to feedback and reviews. Authorship is not granted solely for tasks such as pretesting a survey for your lab-mates, giving brief feedback during one's idea presentation, proofreading, data entry, administrative support, etc.

Authorship order reflects the relative level and nature of contribution, following norms in information science and related social science fields. First author: Usually the person who led the project and writing (often a graduate student when the project is their primary work); Middle authors: Contributed substantively but to a lesser degree; Last author: Often reflects a senior or supervisory role (e.g., PI or advisor), depending on field norms and project structure. Please feel free to bring up the discussion of authorship order early in the project and we can always revisit it as contributions evolve.

To make contributions transparent and explicit, we may refer to the CRediT (Contributor Roles Taxonomy: https://credit.niso.org/) when discussing roles and authorship. CRediT identifies specific contribution categories such as Conceptualization, Methodology, Formal Analysis, Data Curation, Writing, and Supervision. While not every contribution listed in CRediT automatically warrants authorship, this framework helps us clarify who did what and supports fair, consistent authorship decisions.

Q: When multiple students are on the same project, what happens if student collaborators are contributing unevenly? How are responsibilities and authorship handled?

A: Collaborative research often involves students working together with different roles, timelines, and strengths. I do my best to set clear expectations around responsibilities and to ensure that work is distributed fairly. At the same time, authorship and continued involvement in a project are not automatic—they are earned through meaningful, consistent contributions and the quality of work delivered over time. If concerns arise about uneven contributions, I encourage open and respectful communication early on. In many cases, challenges can be addressed by clarifying expectations, adjusting roles, or identifying obstacles that may be affecting someone’s ability to contribute. I also encourage students to support and keep each other accountable in a constructive way—collaboration works best when team members are aligned and communicating, not when concerns are left unspoken. That said, if a student is consistently unable to contribute at a level that moves the project forward—despite feedback and opportunities to improve—it may not be appropriate for them to remain on the project or receive authorship. These decisions are never made lightly, but they are important for maintaining fairness to others on the team and ensuring the integrity and progress of the research. Students' authorship order is typically determined by the level, consistency, and impact of contributions to the project. In general, the first author is the person who takes the lead on the project (e.g., driving the idea, conducting the majority of the work, and leading the writing), while subsequent authors are ordered based on the significance of their contributions. In some cases, co-first authorship or other shared credit may be appropriate. As a supervisor, I will do my best to monitor collaboration dynamics, provide guidance, and step in when needed. If you ever feel that a situation is unclear or unfair, I encourage you to bring it to me early so we can work through it together. My goal is to foster a collaborative environment where everyone contributes meaningfully, is treated fairly, and has the opportunity to do their best work.

Q: What are your expectations for student first authorship?  

A: I strongly support student first authorship when: The project is based on the student’s idea AND the student leads the entire research process (literature review, study design, data collection, analysis, writing and revision). The student also takes primary responsibility for responding to feedback and reviewer comments. My role in these cases is typically supportive and developmental, even if my feedback is extensive. Student first authors are expected to: Drive the project forward and keep it on schedule; Coordinate drafts and revisions among co-authors; Incorporate feedback thoughtfully; Draft and manage responses to reviewers (with guidance); Communicate clearly about timelines and deadlines. First authorship comes with both credit and responsibility.

Q: Do you support your advisees collaborating with other faculty, external researchers, or engaging in academic activities outside of UMD?  

A: In general, I am supportive of advisees collaborating with other professors, external researchers, and engaging in academic activities beyond UMD, as these experiences can be valuable for intellectual growth and career development. At the same time, it is important to prioritize core responsibilities and primary research commitments. For example, when a student is supported by a research assistantship, the RA project should be treated as a top priority, and when a student is working on their dissertation or thesis, steady progress on that work should take precedence. I also generally encourage students to prioritize projects in which they have a leading role, rather than taking on additional commitments that may dilute their focus. I do not directly tell students what they should or should not pursue; instead, I am always happy to help them think through how to prioritize their time across multiple projects, evaluate which opportunities best support their research goals and long-term career trajectory, identify when certain activities may be helpful versus when they may become distractions, and discuss whether there may be potential conflicts of interest, competing obligations, or misaligned expectations across different roles or collaborations. I also appreciate being kept informed about students’ different academic involvements so that I have a holistic understanding of what they are working on, which allows me to provide more thoughtful guidance, anticipate potential challenges, and offer insights as needed.

Q: How are TA and RA positions determined? Can you fund me as an RA?

A: TA and RA positions come from different funding sources and are determined through different processes. TA positions are funded and assigned by the department or college based on instructional needs, so individual faculty typically do not control them. Some students may strongly prefer RA roles, but these are not assigned based on your preference. RA positions depend on external (and sometimes internal) research grants that faculty must apply for, which are highly competitive, time-consuming to prepare, and not guaranteed to be funded. As a result, RA support is not automatically available. Even when RA funding is available, decisions about allocation are made carefully and are based on fit with specific projects. This includes prior experience and relevant skills (e.g., coding and programming, statistics, experimental design and participants recruitment, large-scale observational data scraping and analysis, etc.), alignment with the project’s topic, and—importantly—demonstrated initiative, sustained engagement, and meaningful contribution to the research. Priority is typically given to students who show strong motivation, take ownership of their work, and are actively contributing to projects that align closely with the advisor’s research program. While students are welcome to express interest in funded RA opportunities, it is important to understand that repeated requests alone do not create such opportunities. A strong indicator for receiving RA support is when a student’s work and interests clearly align with ongoing or emerging projects, and when they have demonstrated readiness to contribute at a high level. Therefore, RA positions depend both on the availability of funding and on how well a student’s background, interests, and contributions match the needs of a particular research project - If you’re genuinely a strong fit, I’ll reach out to you first to see if you’re interested in working as my RA—before you even need to ask. Finally, whether you are supported as a TA or RA, it is important to recognize that these roles come with professional responsibilities and expectations. Because these positions provide financial support (e.g., stipend and tuition), the associated duties should be treated as a core priority during the appointment period. TA and RA roles serve different purposes and they both provide valuable forms of professional development. (Note: This part reflects my experience, and specific practices or criteria may vary across faculty members)

Q: If I develop a class project from your course into a publication or proceeding, do you expect to be included as a co-author?  

A: No. If you continue the project after the course on your own or primarily with your advisor, you are under no obligation to invite me to remain involved or to include me as a co-author—even if the project originated in my class and I provided extensive feedback as part of the course. Providing detailed feedback, helping you refine ideas, and supporting your early-stage thinking are core parts of my role as an instructor and do not by themselves constitute authorship.  That said, you are very welcome to invite me to collaborate on the project after the course proposal stage if—and only if—you believe my expertise is a strong fit for the project, either topically, methodologically, or both. If you would like me to play an active, ongoing collaborative role beyond the course (e.g., shaping theory, methods, analysis, or writing), I am happy to do so, assuming we mutually agree on expectations, scope of involvement, and timelines. In cases where I am involved as a post-course collaborator, it is important to make sure that your primary advisor is aware of and supportive of the collaboration. Clear communication and alignment among all mentors is important. 

To summarize: Course feedback alone does not imply authorship. Post-course collaboration is optional and student-initiated. Authorship is appropriate only if I make a substantive intellectual contribution beyond the course, consistent with standard authorship criteria. You should never feel pressured or obligated to include me as a co-author simply because the project began in my class. If you are unsure whether a collaboration or authorship discussion is appropriate, I encourage you to ask—we can always talk it through openly and early.

Q: Do you collaborate with graduate students who are not your PhD advisees?  

A: Yes. I am always happy to collaborate with graduate students who are not my formal advisees, provided a few conditions are met: You can clearly articulate what expertise, guidance, or perspective you are seeking from me; Why you think our research interests are a good fit; Your primary advisor is aware of and supportive of the collaboration. Transparency and alignment among all mentors involved are very important.

When reaching out, please include: A brief description of the project or idea  Your current stage (e.g., early idea, data collected, draft in progress); What kind of input or role you are hoping I can play; Any relevant deadlines or time constraints. This helps me assess whether and how I can best support you.

Q: I don’t currently work with you, and I emailed you but didn’t hear back—what does that mean?

A: I try my best to respond to messages, and I genuinely appreciate you reaching out. That said, if you don’t hear back from me, it usually reflects my current capacity rather than anything about you or your work. Like many university professors, I receive a high volume of emails, and sometimes messages can get buried among hundreds of incoming emails. If your email was time-sensitive or particularly important, a polite follow-up after some time is completely appropriate. It also helps me respond more efficiently when your email clearly articulates the questions you’d like me to address. At the same time, it is not always possible for me to respond to every email I receive given the volume and the time required to provide thoughtful replies. I need to prioritize my time across existing commitments—my current students, teaching, ongoing research projects, grant writing, service—so that I can engage in those responsibilities thoughtfully and do that work well. Because I’m already committed to supporting the students I work with, I need to allocate my time in a way that is sustainable and allows me to be a reliable mentor to them. I also prioritize responsibilities within my college and my existing collaborations. As a result, I may not be able to prioritize requests from students outside of my primary appointment unit in the same way. No one can be effective if they are stretched too thin, and maintaining these boundaries helps ensure that I can provide meaningful support where I’ve already made commitments.