Imaging, Data, and Learning:
Jeremias Sulam
Modern challenges in biomedical datadata science
ISPEED 2025
Inverse Problems
Micro
Meso
Macro
Imaging, Data, and Learning
Fairness in Data Science
Is the model fair?
\text{TPR}_\text{sex} = \mathbb P [\hat Y = 1 | Y = 1, \text{sex}]
Pneumonia: \(Y=1\)
Clear: \(Y=0\)
95% accurate
\Delta_{\text{TPR}} = {\Large|} \text{True Positives}_\text{males} - \text{True Positives}_\text{females} {\Large|}
Fairness in Data Science
predictions
ground-truth
Does your model achieve a fairness violation of at most (say) 6% ?
Fairness in Data Science
Pneumonia: \(Y=1\)
Clear: \(Y=0\)
95% accurate
Tight upper bounds to fairness violations
(optimally) Actionable
Maximum TPR
discrepancy
True TPR
discrepancy
Bharti, B., Yi, P., & Sulam, J. (2023). Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors NeurIPS 2023Fairness in Data Science
Pneumonia
Clear
95% accurate
Macro
Meso
Micro
Imaging, Data, and Learning
Micro
Imaging, Data, and Learning
Micro
Macro
Meso
Imaging, Data, and Learning
“The future rewards those who press on.”
B. Obama
Inverse Problems in Neuroscience: Susceptibility Tensor Imaging
6 times faster
Inverse Problems in Digital Pathology
Inverse Problems in Radiology
Opportunity is missed by most people because it is dressed in overalls and looks like work.
T. Edison
ISPEED Sulam Lab and BME Path
By Jeremias Sulam
