Roshni  Sahoo

Roshni Sahoo

Scholar Title

MIT EECS | Angle Undergraduate Research and Innovation Scholar

Research Title

Developing Models Resilient to Feature-Dependent Label Noise





Research Areas
  • Artificial Intelligence and Machine Learning

John V. Guttag


In real-world datasets, training labels may be inaccurate because datasets suffer from annotation error, human bias, or a noisy process for generating the labels. Label noise in training datasets leads to a decrease in classification accuracy and poses a challenge to identifying relevant features. Feature-dependent label noise is a class of label noise that has yet to be studied in great depth. Through this project, we aim to measure feature-dependent label noise in existing datasets, develop a technique for creating models that are resilient to this type of noise, and benchmark our results against existing methods for label noise reduction.


“I’m excited about SuperUROP because I want to improve the accountability of machine learning methods. I became interested in this area during my previous UROP in the Distributed Robotics Lab, where I worked on quantifying uncertainty of pose estimates. Additionally, I’ve enjoyed classes that have prepared me for this work, such as Courses 6.437 (Inference and Information) and 9.60 (Machine-Motivated Human Vision).”

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