Elizabeth Newman

Assistant Professor


Curriculum vitae



Department of Mathematics

Tufts University

Joyce Cummings Center
177 College Ave
Room 561
Medford, MA 02155



Funding


Brief Description

My research falls into two broad categories:

  • Multilinear Algebra: Many data are naturally represented as multiway arrays or tensors, and as a result, tensor-based approaches have revolutionized feature extraction and compression. My research focuses on developing matrix-mimetic tensor frameworks that preserve desirable linear algebraic properties (think rank, orthogonality, and multiplication). The resulting framework looks and feels like matrix algebra and, as a result, we are able to naturally extend traditional algorithms to high-dimensions and obtain optimal representations of multiway data.

  • Deep Learning: Deep neural networks (DNNs) have achieved undeniable success as high-dimensional function approximators in countless applications. However, this success comes at a significant hidden cost: the cost of training. Typically, the training problem is posed as a stochastic optimization problem with respect to the learnable DNN weights. With millions of weights, a non-convex objective function, and many hyperparameters to tune, solving the training problem well is no easy task. My research focuses on making training easier by exploiting commonly-used DNN architectures resulting in faster convergence, more accurate models, and automatic hyperparameter tuning.

Building Open Machine Learning Benchmarks


Sandia National Laboratories LDRD




Learnable Tensor Algebras


NSF Computational Math: DMS 2309751


AI-Assisted Social Justice in Tissue and Organ Biomanufacturing


Emory/Georgia Tech AI.Humanity with a Social Justice Lens


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