Empowering systemic racism research at MIT and beyond
Researchers in the MIT Initiative on Combatting Systemic Racism are building an open data repository to advance research on racial inequity in domains like policing, housing, and health care.
Researchers in the MIT Initiative on Combatting Systemic Racism are building an open data repository to advance research on racial inequity in domains like policing, housing, and health care.
Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.
The approach can detect anomalies in data recorded over time, without the need for any training.
The model could help clinicians assess breast cancer stage and ultimately help in reducing overtreatment.
A new technique enables users to compare several large models and choose the one that works best for their task.
Fifteen new faculty members join six of the school’s academic departments.
Graduate student Nolen Scruggs works with a local tenant association to address housing inequality as part of the MIT Initiative on Combatting Systemic Racism.
Tamara Broderick uses statistical approaches to understand and quantify the uncertainty that can affect study results.
By breaking an intractable problem into smaller chunks, a deep-learning technique identifies the optimal areas for thinning out traffic in a warehouse.
Exploiting the symmetry within datasets, MIT researchers show, can decrease the amount of data needed for training neural networks.