Dr. Brown 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 Dr. Brown 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 (with me).
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:::{card} Robust ML evaluation Developer tools
building and maintaining Python packages that facilitate other research in the lab
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 or demonstrated proficiency in git, bash, and IDEs
::: :::{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} 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
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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 gradutate students may start out working on one of the projects listed above
Graduate students in our lab are in the URI Computer Science MS or PhD program, if you apply to this program, Dr. Brown will see your application. To be considered, 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, if you send an e-mail that includes them it will not be considered.
Dr. Brown gets many inquiries from prospective students. To make your e-mail stick out, be sure to mention how your interested align with the labs goals and make it easy to find. 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 and send your e-mail to brownsarahm+ml4sts@uri.edu. Other inquiries may not be addressed.
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