Join the lab#
To join the lab, you must be interested and motivated to taking a socio-technical approach to doing ML research and building ML-powered systems.
Current URI Undergraduates#
I can supervise research for you to get experience to decide if you want to go to graduate school, for project credit (CSC499,491), and occaionally with funding.
If you are interested, please read the website to learn about the work and submit this form just for current URI students. The form notifies me by e-mail and allows you to book a meeting as an interview.
Typically, I prefer to work with students who have taken CSC310 (with any professor) or CSC392/CSC311 (with me).
Current Open Projects#
Undergraduates will almost always spend at least one semester on one of these before starting their own project. The most common exception is students with a double major who have a second research advisor and want to do data science too.
building and maintaining Python packages that facilitate other research in the lab including as AI agent tools
Example tasks:
add unit tests
improve documentation
add new features to support libraries to make other research easier
Build skills in software engineering, project management, and working with other developers. A great opportunity to learn ML from a software perspective
Prereq:
CSC392/CSC311 or demonstrated proficiency in git, bash, and IDEs
and proficiency in Python
Empirical evaluation of the contexts where fair ML algorithms succeed and fail through intentionally designed biased data
Example tasks:
write code that impelements types of biased synthetic data based on mathematical models in the literature
evalute parameter ranges that create/do not create bias interactions
analyze data on existing biases to determine advice for data scientists
add more ML models by implementing recent advances in fair ML or developing novel interventions
generate new types of biased data based on your own understanding of how social systems create inequity
Build skills in data science and machine learning
Prereq: CSC/DSP310
Robust evaluation of LLMs as decision-makers, assistants, and agents in data-rich contexts with respect to the fairness of the decision making.
Example tasks:
collect and manipulate datasets with fairness concerns
create personas for LLMs to try to make them do better/worse at fair data science
evaluate an LLM through an API
Build skills in data science and machine learning
Prereq: CSC/DSP310
collaborate on psychology experiments to figure out what people think is fair in ML in different contexts, how people differ and how they change their minds
Example tasks:
design new plot styles using python plotly and implement them for use in experiments
analyze experimental data
design new questions to ask people
Build skills in data science, front end, social impacts of computing and interdisciplinary work.
Prereq (one of the following):
CSC/DSP310 (preferred)
other significant Python work and Plotly familiarity
A low code opportunity is to work on documentation for any of the code tools we develop in the lab. Reading code and understanding it is a good way to learn more, while contributing written English instead of in a programming language.
Current URI Graduate Students#
Ideally, you should take a graduate level course with Dr. Brown in your first year at URI. If you are admitted without credit for a Machine Learning course, take CSC461 in your first fall.
My current graduate courses are Model Evaluation and Explanation (offered in odd springs) and Sociotechnical AI Safety (offered in even springs).
Please read the website to learn about the work and submit this form just for current URI students. The form notifies Dr. Brown by e-mail and allows you to book a meeting as an interview.
New graduate students may start out working on one of the projects listed above before taking leadership on a project of their own.
Prospective Graduate Students#
I deeply value research integrity and thoughtfulness in research. Using automation inappropriately in the process of searching for graduate school is inconsistent with demonstrating that you will conduct research with integrity as a member of the lab. Any students who send emails that seem inappropriately automated will not be considered for lab membership.
Tip
I will look for the lab name in your statement when I read the applications as a member of the graduate committee
Graduate students in our lab are in the URI Computer Science MS or PhD program. If you apply to this program, I will see your application. To be considered to join the lab, mention the lab name and why you want to do research aligned with the lab’s research goals in your personal statement.
Funding#
Important
I am actively recruiting a PhD student to start in Fall 2026 with RA funding.
Messaging#
I receive many inquiries from prospective students and am unable to reply to all of them. Some notes:
I do not require prospective students to contact me prior to applying.
I will try to reply to emails that contain specific questions about the fit of a students research interests with the lab.
I will also reply to specific emails that are asking about competitive external or URI fellowship opportunities where I can serve as a reference.
I primarily use these emails for extra information when reading graduate applications, to make your e-mail easy to find, use the subject,
Prospective ML4STS Lab member - <MS/PhD>with the appropriate degree based on what you are applying to selected and send your e-mail tobrownsarahm+ml4sts@uri.edu. These emails can be favorable if you send something personal about why you want to join the lab, but I cannot assess your application via email.URI CS Graduate Programs do not use GRE scores, please do not send them to me. If you email these to me, I will not read the rest of your message.
Meetings#
I will only meet with prospective students in the following conditions:
I have a funded position I am recruiting for, as indicated above
The Student is eligible for and applying to a competitive external fellowship (eg NSF GRFP) that requires support from a prospective advisor
The student has been admitted and is trying to decide to attend URI or not
Other inquiries#
Currently, Dr. Brown mostly does not have capacity to supervise people outside of the roles above. If you are interested in double checking, however, send an e-mail to brownsarahm+ml4sts@uri.edu with a descriptive subject and an email body that describes why you want to work in my lab, specifically.