Semantic topic modeling, topic evolution, scientific knowladge graph generation with human in the loop procedure, and scientific leadership identification and characterization.

Exascale data analytics, dimension reduction, hidden feature extraction, and efficent and scalable algorithms in emerging computing architectures.

Anomaly detection, user-behavior analysis, malware analysis, and novel threat discovery.

Ultra-fast solving extra-large partial differential equations, high-dimensional integrals, and integro-differential equations.

Detection of latent communities in directed and undirected graphs and networks, ranking of latent research communities hidden in temporal multilayer networks.

Latent patterns in genomics, transcriptomics, metabolomics, proteomics, and cell membranes.

Analysis of combinatorial material libraries based on their: X-ray, Hyperspectral X-ray Imaging, Raman fluresence and other spectra.

Latent patterns in medical research.

Discoring new chemical pathways and reactions, radioisotope characterization, phase seperation analysis in complex liquids, and co-polymers.

Compression of large images and videos (e.g. asteroid water impacts), scientific computer-generated data, and more.

Ice and water masses trainsient patterns, micro-climate patterns.

Boolean factorization analysis of categorical patterns.

Data privacy with federated learning, and recommender systems.

Macro-economy analyses, and marketing.

Estimating the role of water, salt, and fertilizer content on the yield.

Tensor Extraction of Latent Features (T-ELF) is one of the machine learning software packages developed as part of the R&D 100 winning SmartTensors AI project at Los Alamos National Laboratory (LANL). T-ELF presents an array of customizable software solutions crafted for analysis of datasets.

pyCP-APR is a Python library for tensor decomposition and anomaly detection that is developed as part of the R&D 100 award wining SmartTensors project. It is designed for the fast analysis of large datasets by accelerating computation speed using GPUs.

pyDNMFk is a software package for applying non-negative matrix factorization in a distributed fashion to large datasets. It has the ability to minimize the difference between reconstructed data and the original data through various norms (Frobenious, KL-divergence).

Tensors-based framework for adversarial robustness. Library implements a variety of tensor factorization methods for defending Artificeal intelligence (AI) models against adversarial attacks.

pyDNTNK is a software package for applying non-negative Hierarchical Tensor decompositions such as Tensor train and Hierarchical Tucker decompositons in a distributed fashion to large datasets. It is built on top of pyDNMFk.

Cuda Python Distributed Non Negative Matrix Factorization with determination of hidden features. cuda-pyDNMFk is a dynamic software platform tailored for the decomposition of large datasets that surpass the limitations of in-memory processing.

pyDRESCALk is a software package for applying non-negative RESCAL decomposition in a distributed fashion to large datasets. It can be utilized for decomposing relational datasets.

The identification of sources of advection-diffusion transport is based usually on solving complex ill-posed inverse models against the available state-variable data records. pyHNMFk synergistically performs decomposition of the recorded mixtures, finds the number of the unknown sources and uses the Green's function of advection-diffusion equation to identify their characteristics.

pyQBTNs is a Python library for boolean matrix and tensor factorization using D-Wave quantum annealers.