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.
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).
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.
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:::{card} Robust ML evaluation Developer tools
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.
Prereq:
- CSC392/CSC311 or demonstrated proficiency in git, bash, and IDEs
- and proficiency in Python
::: :::{card} When do fair ML algorithms work?
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 :::
:::{card} Can LLMs do fair data science?
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:
- implement functions to compute custom scores for LLMs on data sicence tasks
- collect and manipulate datasets with fairness concerns
- evaluate an LLM through an API
Build skills in data science and machine learning
Prereq: CSC/DSP310 :::
:::{card} Perceptions of AI fairness
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 :::
:::{card} Data Empowerment for Election workers
collaborate with Industrial Enginers to help election workers learn data science skills by designing a short workshop
Example Tasks:
- clean example datasets for the lesson
- examine candidate data sets to help decide what examples to use in the lesson
- design practice problems with example datasets
- write explanations for necessary code structures
- write and test setup instructions
Prereq:
- CSC/DSP310 or STA/DSP305
- experience as a TA (preferred) :::
:::{card} Documentation
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.
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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 course is ML for science and society see this page for information and to request a permission number.
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.
I will look for the lab name in your statment 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.
URI CS Graduate Programs do not use GRE scores, please do not send them to me, I will not look at them if you send them via email.
I do not anticipate having RA funding for incoming students starting at any time in 2025.
I receive many inquiries from prospective students and am unable to reply to all of them. I generally do not set up meetings with prospective students until they are admitted and deciding to atten URI or not. If you request that, I am unlikely to reply. 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 to [email protected]. 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.
I will 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 fellowship opportunities where I can serve as a reference.
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.
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 [email protected] with a descriptive subject and an email body that describes why you want to work in my lab, specifically.