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The goal of the MPLab is to develop systems that perceive and interact with humans in real time using natural communication channels. To this effect we are developing perceptual primitives to detect and track human faces and to recognize facial expressions. We are also developing algorithms for robots that develop and learn to interact with people on their own. Applications include personal robots, perceptive tutoring systems, and system for clinical assessment, monitoring, and intervention.

  • Introduction to the MPLab (PDF)
  • MPLAB 5 Year Progress Report (PDF)

  • NEWS


    [Blaschko et al] joint learning and projecting labeled data onto the manifold created from unlabeled data improves regression performance.

    [Fujiwara et al.] Bayesian CCA to reconstruct visual stimuli from fMRI. The learned basis varies by eccentricity from 1px to 4px patterns.

    ROC or Accuracy? Corinna Cortes and Mehryar Mohri[nips04], Davis & Goadrich [ICML2006], Ulf Brefeld Tobias Scheffer[icml05]

    Sahand Negahban analyzes error bounds of sparse or low rank parameters in Lasso regression/covariance problem.

    Sergio Verdu gives a nice (invited) talk on “relative entropy”. More ‘EE’ perspective such as compression/coding than CS though.

    Tony Torralba has a extensive and “exhausted” tutorial of object/scene recognition. 1 paper per slide! good summary but not very systematic

    Antoni Torralba has a extensive and “exhausted” survey of object/scene recognition. One paper per slide!

    Uncanny Valley in Monkeys http://bit.ly/2vrLvu


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