M. Alex O. Vasilescu received her education at the Massachusetts Institute of Technology and the University of Toronto.


Vasilescu introduced the tensor paradigm for computer vision, computer graphics, machine learning, and extended the tensor algebraic framework by generalizing concepts from linear algebra. Starting in the early 2000s, she re-framed the analysis, recognition, synthesis, and interpretability of sensory data as multilinear tensor factorization problems suitable for mathematically representing cause-and-effect and demonstratively disentangling the causal factors of observable data.  The tensor framework is a powerful paradigm whose utility and value has been further underscored by theoretical evidence that has showing that deep learning is a neural network approximation of multilinear tensor factorization and shallow networks are linear tensor factorizations (CP decomposition).


Vasilescu’s face recognition research, known as TensorFaces, has been funded by the TSWG, the Department of Defenses Combating Terrorism Support Program, and by IARPA, Intelligence Advanced Research Projects Activity. Her work was featured on the cover of Computer World, and in articles in the New York Times, Washington Times, etc. MIT Technology Review TR100 honoree, and the National Academy of Science co-awarded the KeckFutures Initiative Grant.​​


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  • How are tensor methods used in computer vision & machine learning? Answer

  • What is all the current fuss with tensor data analysis? Historical Perspective