Lucy Li

For Prospective PhD Students

I am an incoming assistant professor at Wisconsin-Madison’s Computer Sciences department (starting Fall 2026), and my research focuses on natural language processing, computational social science, and responsible AI. I wrote the following so you can learn more about me and where I’m at, research-wise and department-wise.

Before applying, you do not have to reach out to me in advance, but you may do so if you’d like. I plan to assess applicants based on everyone’s full applications after the deadline. Given my commitments to my collaborators and research, I may not have time to respond to every email I receive. I really do appreciate your interest, though!! It’s okay to address me as “Lucy” when you contact me (titles aren’t needed, unless you prefer to use one).

Please apply by our December deadline, and put my name down in your application. Wisconsin-Madison CS also has a research-based master’s program.

🌱 This page is a digital garden -- it will be revised over time as my thoughts evolve.

Research fit

What is Lucy looking for?

Successful PhD students come in many forms. Most are driven by passion, from which they find resilience — a much-needed trait for the challenges of grad school. To be best prepared for the research we’d do, you would likely have a degree in computer science or a related field, or similar industry experience. You should also have some past experience demonstrating a sincere interest in some social science or humanities discipline, and/or an appreciation for language and linguistics. Producing good research and forming good research taste are two distinct but equally important skills, and your application should hopefully suggest the potential for both.

PhD admissions is a process of trust. Your application should make a good argument for why I should trust you to join my group, and if you are offered a position, I’d like you to trust me to become your advisor.

What might we work on?

My research group will bring together two research areas: human-centered AI development and computational social science. I don’t expect every student to span both, as each is a large, complex research area, and there is plenty of depth to achieve within a single side. Both sides benefit from knowing how models work and how they could be used, and an interchange of concepts and ideas between them makes everyone stronger. Both of these research areas benefit from interdisciplinarity, but I will likely train you to speak towards these communities from an NLP perspective. It’s good to define your home community early on, even if you do wish to be interdisciplinary, as it offers context for how you approach problems and guides your focus during your PhD.

With human-centered AI, my PhD students may generally wish to examine how social characteristics, behaviors, and interactions relate to models’ data ecosystems and development pipelines. Though much of my past work focuses on fairness, I’m also interested in studying the societal impact and safety of language models (LMs) more broadly. My research toolkit mostly consists of computational methods, but I am open to blending qualitative methods into this work. Some big questions we may try to tackle include:

• How do LMs’ performance vary across language from different communities (e.g. sociolects, dialects), and how may we measure and/or mitigate these issues?
• How do social aspects of model (pre-, mid-, post-) training affect their downstream behavior?
• How can computational social science methods contribute more nuanced evaluation of generated text and human-AI conversations?
• How can we model human behavior to support LM development (e.g. this blog post)?

Speaking of computational social science, my PhD students may also wish to develop NLP approaches for answering research questions relevant to sociology, psychology, education, and/or media studies. My flavor of computational social science overlaps with cultural analytics and computational humanities. When I work with data drawn from books and other media, I lean towards questions that center social aspects of that data. I also tend prefer projects that nudge us to be methodologically creative and/or connect language to insights around people (my undergraduate degree was in cognitive science, and hints of that still remain!). Currently, I am intent on interrogating how LMs may be useful for researchers in the aforementioned disciplines, while considering practical constraints that may arise (e.g. resource-efficiency, subjective interpretation).

AI is a fast-moving field with lots of potential directions to pursue. It’s totally okay if not all of the above is perfectly reflected in your statement, and when in doubt, you should apply anyways. If you do end up working with me, your interests will help me evolve mine.

Generally, I hope our work together will be thoughtful, timely, and impactful. Our research should go beyond providing surface-level answers to questions like "Can LMs be prompted to do [some task]?" or "Are LMs biased against [some social group]?" Good AI research should also avoid being overly model-specific, and instead concentrate on something conceptual, to lengthen the potential lifespan of a paper. Aside from conceptual takeaways, our work may also produce artifacts such as code, data, and tools that could be readily used by others.

Why Wisconsin?

Wisconsin-Madison has one of the oldest doctoral-granting computer science departments in the United States. It is heavily invested in growing its AI presence in the next few years, such as hiring new faculty across campus and constructing a new philanthropically-funded, $260 million building for the School of Computer, Data & Information Sciences (CDIS). Since my work is interdisciplinary, I'll definitely be on the lookout for collaborations with researchers in other departments.

I have not yet moved to Madison, WI, and so I can’t describe it from a personal perspective. AnHai Doan, who has lived here for awhile, wrote a nice page about it.

Based on some aggressive Google Maps’ing and Internet browsing, I can share a few intriguing things about Madison and the surrounding area. UW-Madison includes cows that help make ice cream on campus. Madison the city is located on an ithsmus, or between two big lakes, and its downtown area hosts the state capitol building and the biggest farmer’s market in the U.S. It’s one hour from Milwaukee (big museums!), two hours from Chicago (even more big museums!), and four hours from Minneapolis-St. Paul (the Mall of America!). Madison is also one hour from the waterpark capital of the world: Wisconsin Dells. Importantly, the Dells include the tallest waterslide in the U.S., a waterslide that spins like a ferris wheel, and an interactive deer park. For nature lovers, Madison is also near several state parks and cross-country skiing trails. Finally, users on the Reddit forum r/SameGrassbutGreener tend to view it as underrated and a great place to live.

Overall, I hope this page was helpful! And again, please apply by our deadline.