Applied Machine Learning Group

Welcome to the Applied Machine Learning Group at Northeastern.

Professor Brodley’s research group, Applied Machine Learning at the Khoury College of Computer Sciences, focuses on core issues of machine learning, as well as real-world applications of machine learning.

Recent core issues investigated include:

  • Active learning
  • Conditional random fields
  • Constraint-based clustering
  • Crowd sourcing in clustering

Recent applications (and collaborators) include:

  • Predicting disease course for Multiple Sclerosis patients (Neurology, Harvard Medical School)
  • Detecting Focal Cortical Dysplasia in the MRI’s of Epilepsy Patients (Neuroscience and Radiology, NYU Medical School)
  • Understanding the predictors of unilateral vestibular disorders (Mass Eye and Ear)

Publications

Removing confounding factors via constraint-based clustering: An application to finding homogeneous groups of multiple sclerosis patients

Jingjing Liu, Carla E. Brodley, Brian C. Healy, Tanuja, Chitnis, Removing confounding factors via constraint-based clustering: An application to finding homogeneous groups of multiple sclerosis patients, Artificial Intelligence In Medicine, 2015

Clinical Vestibular Testing Assessed With Machine-Learning Algorithms

Adrian J. Priesol, MD; Mengfei Cao, BA; Carla E. Brodley, PhD; Richard F. Lewis, MD,,Clinical Vestibular Testing Assessed With Machine-Learning Algorithms, JAMA Otolaryngol Head Neck Surg. doi:10.1001/jamaoto.2014.3519, 2015.

Cortical feature analysis and machine learning improves detection of “MRI-negative” focal cortical dysplasia

Bilal Ahmed, Carla E. Brodley, Karen E. Blackmon, Ruben Kuzniecky, Gilad Barash, Chad Carlson, Brian T. Quinn, Werner Doyle, Jacqueline French, Orrin Devinsky, Thomas Thesen , Cortical feature analysis and machine learning improves detection of “MRI-negative” focal cortical dysplasia, Science Direct, Epilepsy & Behavior, 2015