Dealing with the limitations of our noisy world
Tamara Broderick uses statistical approaches to understand and quantify the uncertainty that can affect study results.
MIT researchers develop a customized onboarding process that helps a human learn when a model’s advice is trustworthy.
AI accelerates problem-solving in complex scenarios
A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.
Rewarding excellence in open data
MIT researchers who share their data recognized at second annual awards celebration.
Celebrating the impact of IDSS
Taking the place of IDSS’s annual statistics and data science conference SDSCon, the celebration also doubled as a way to recognize Dahleh for his work creating and executing the vision of IDSS as he prepares to step down from his director position this summer.
The system they developed eliminates a source of bias in simulations, leading to improved algorithms that can boost the performance of applications.
Caroline Uhler named SIAM Fellow for 2023
In the award announcement, SIAM noted that Uhler is being honored for her “fundamental contributions at the interface of statistics, machine learning, and biology”.
With the right building blocks, machine-learning models can more accurately perform tasks like fraud detection or spam filtering.
Subtle biases in AI can influence emergency decisions
But the harm from a discriminatory AI system can be minimized if the advice it delivers is properly framed, an MIT team has shown.
Unpacking the “black box” to build better AI models
Stefanie Jegelka seeks to understand how machine-learning models behave, to help researchers build more robust models for applications in biology, computer vision, optimization, and more.