Transforming Machine Learning
QGNai has developed a novel machine learning platform (“RG Flow™”) to significantly improve the learning and decision-making of machines.
RG Flow learns hidden relationships in the data (e.g., image, text, bio sequence, etc.) through a hierarchical structure resulting in deeper learning than state of the art.
The platform uses a proprietary recursive dimensional reduction technology to coarse-grain initial datasets to discover their underlying dominant features at all levels. The invertibility of the architecture allows fine-tuning coarse-grain data to generate new datasets based on latent variables. RG Flow's bidirectional generative design enables efficient classification, clustering, and explainability.
RG Flow has broad applications in natural language processing, biomedical research, cybersecurity, and robotics.
Cofounder & CEO
Cofounder & CEO; Serial Entrepreneur; MBA, Harvard University
Cofounder & Chief Scientific Officer
Associate Professor of Mathematics, Center for Quantum Geometry of Moduli Spaces, Harvard University CMSA and Inst for Mathematic AU; Ph.D. in Mathematics, University of Illinois at Urbana-Champaign
Cofounder & Chief Technology Officer
Assistant Professor of Physics, UCSD; Ph.D., Condensed Matter Physics, Tsinghua University
Machine Learning Scientist
Ph.D., Physics, Harvard University
Ph.D., Physics & M.S., Computer Science, Virginia Tech
Associate Director of Machine Learning, Broad Institute; Ph.D., Physics, Harvard University
Director of Computational Biology, Monte Rosa Therapeutics; Ph.D., Biomedical Engineering, Johns Hopkins University School of Medicine
Medical Science Liaison, Oncology, Natera, Ph.D., Biochemistry, Virginia Commonwealth University
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