Lara Kassab

Lara Kassab
Pacific Northwest National Laboratory | PNNL

Ph.D. Mathematics

About

19
Publications
759
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16
Citations
Introduction
I am interested in many aspects of machine learning and most of my research work focuses on mathematical methods for machine learning and data analysis.
Skills and Expertise

Publications

Publications (19)
Preprint
Full-text available
Automatic infectious disease classification from images can facilitate needed medical diagnoses. Such an approach can identify diseases, like tuberculosis, which remain under-diagnosed due to resource constraints and also novel and emerging diseases, like monkeypox, which clinicians have little experience or acumen in diagnosing. Avoiding missed or...
Preprint
Full-text available
Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements. Development and evaluation of new manufacturing methodologies is labor-, time-, and resource-intensive expensive due to complex, poorly defined relationships between advanced manufacturing...
Preprint
Full-text available
We propose new semi-supervised nonnegative matrix factorization (SSNMF) models for document classification and provide motivation for these models as maximum likelihood estimators. The proposed SSNMF models simultaneously provide both a topic model and a model for classification, thereby offering highly interpretable classification results. We deri...
Chapter
There is currently an unprecedented demand for large-scale temporal data analysis due to the explosive growth of data. Dynamic topic modeling has been widely used in social and data sciences with the goal of learning latent topics that emerge, evolve, and fade over time. Previous work on dynamic topic modeling primarily employ the method of nonnega...
Article
The task of predicting missing entries of a matrix, from a subset of known entries, is known as matrix completion. In today's data-driven world, data completion is essential whether it is the main goal or a pre-processing step. Structured matrix completion includes any setting in which data is not missing uniformly at random. In recent work, a modi...
Preprint
Full-text available
We propose several new models for semi-supervised nonnegative matrix factorization (SSNMF) and provide motivation for SSNMF models as maximum likelihood estimators given specific distributions of uncertainty. We present multiplicative updates training methods for each new model, and demonstrate the application of these models to classification, alt...
Preprint
We analyze Twitter data relating to the COVID-19 pandemic using dynamic topic modeling techniques to learn topics and their prevalence over time. Topics are learned using four methods: nonnegative matrix factorization (NMF), nonnegative CP tensor decomposition (NCPD), online NMF, and online NCPD. All of the methods considered discover major topics...
Preprint
In today's data-driven world, data completion is essential whether it is the main goal or a pre-processing step. Structured matrix completion includes any setting in which data is not missing uniformly at random. In recent work, a modification to the standard nuclear norm minimization (NNM) for matrix completion has been developed to take into acco...
Preprint
Full-text available
There is currently an unprecedented demand for large-scale temporal data analysis due to the explosive growth of data. Dynamic topic modeling has been widely used in social and data sciences with the goal of learning latent topics that emerge, evolve, and fade over time. Previous work on dynamic topic modeling primarily employ the method of nonnega...
Article
We propose a torus model for high-contrast patches of optical flow. Our model is derived from a database of ground-truth optical flow from the computer-generated video Sintel, collected by Butler et al. in A naturalistic open source movie for optical flow evaluation. Using persistent homology and zigzag persistence, popular tools from the field of...
Preprint
Multidimensional scaling (MDS) is a popular technique for mapping a finite metric space into a low-dimensional Euclidean space in a way that best preserves pairwise distances. We overview the theory of classical MDS, along with its optimality properties and goodness of fit. Further, we present a notion of MDS on infinite metric measure spaces that...
Preprint
Multidimensional scaling (MDS) is a popular technique for mapping a finite metric space into a low-dimensional Euclidean space in a way that best preserves pairwise distances. We study a notion of MDS on infinite metric measure spaces, along with its optimality properties and goodness of fit. This allows us to study the MDS embeddings of the geodes...
Preprint
Full-text available
In "A naturalistic open source movie for optical flow evaluation," Butler et al. create a database of ground-truth optical flow from the computer-generated video Sintel. We study the high-contrast $3\times 3$ patches from this video, and provide evidence that this dataset is well-modeled by a torus (a nonlinear 2-dimensional manifold). Our main too...

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