Massive data and predictive analytics requires award-winning capability.
Massive data and predictive analytics requires award-winning capability.
Tensor Medium Corporation is a Texas-based AI technology company founded by leading data scientists from national laboratories. Our expert team developed the R&D100-winning SmartTensors AI Platform, with capabilities validated in over 100 peer-reviewed publications. We specialize in revolutionary tensor mathematics and distributed computing solutions that enable analysis of massive datasets at scales previously considered impossible, bringing unmatched expertise in exabyte-scale AI to defense and commercial applications.
Extreme compression and top-tier performance for multi-terabyte data, validated by HPC achievements on one of the world’s fastest supercomputers.
We’ve set new records in large-scale factorization by harnessing the raw power of the Summit supercomputer. Leveraging our distributed Tensor-Train and Quantized Tensor-Train methods, we successfully factorized multi-terabyte datasets to a fraction of their size—without sacrificing critical information. This world-first achievement in extreme-scale HPC factorization for machine learning accelerates simulations, reduces storage footprints, and supports real-time analytics for some of the biggest data challenges in science and industry.
Rapidly organize and interpret large, messy datasets through robust ML pipelines that reveal hierarchies and dependencies you didn’t know existed.
Our team’s expertise in hierarchical alignment and automated data labeling unlocks hidden relationships in complex datasets, drastically improving predictive analytics. From segmenting sprawling sensor logs to revealing subtle process dependencies, we’ve developed advanced NMF-based and transformer-driven workflows that classify and cluster data with minimal human intervention. By iteratively refining these labels and models, we uncover multi-scale patterns—enabling improved forecasting, anomaly detection, and strategic insights.
Make sense of your sprawling data environment with an end-to-end pipeline that elegantly organizes, clusters, and contextualizes information.
Harnessing semantic Nonnegative Matrix Factorization (SeNMFk) and knowledge graph construction, we unify structured and unstructured data to yield advanced sense-making. Whether you’re tackling open-source intelligence or internal R&D archives, we provide intuitive visualizations, refined relationships, and intelligent classification. Our integrated approach clarifies hidden trends and powers evidence-based decision-making across industries.
Achieve synchronized analysis across different data modalities, ensuring every relevant signal is captured and effectively harnessed.
Today’s data streams are rarely one-dimensional. Our frameworks fuse multi-sensor signals, textual intelligence, imagery, and more into coherent ML pipelines. By leveraging advanced neural architectures, domain-driven features, and robust dimensionality reduction, we detect anomalies, classify content, and extract patterns from even the most heterogeneous sources—without sacrificing performance or interpretability.
Reduce computational overhead and gain deeper physical accuracy for PDE-based tasks in engineering, climate modeling, and beyond.
From fluid dynamics to heat transfer, our advanced tensor-based PDE solvers drastically speed up time-dependent and steady-state simulations. Building on our “Tensor Network Space-Time Spectral Collocation Method,” we combine spectral accuracy with HPC-friendly tensor decompositions, effectively overcoming the “curse of dimensionality.” Whether you’re simulating multi-phase flows or complex structural mechanics, our PDE pipelines cut runtime from days to hours and offer near-exponential convergence rates.
Harness data-rich AI workflows that optimize toxin discovery and bioactive compound analysis, bridging the gap between bench research and clinical innovation..
Our breakthroughs in biomedical data analysis—exemplified by conotoxin research from “Conotoxin Prediction New Features..."-demonstrate how specialized ML pipelines can accelerate drug discovery. By integrating collisional cross-section features, advanced NMF methods, and post-translational modifications into our models, we’ve enhanced the accuracy of conotoxin identification and toxicity prediction. This approach opens the door for faster screening of novel peptides, streamlining the journey from basic research to potential therapeutics.