The Polus Center for Data Research  

At The Polus Center for Data Research (PCDR) , we are advancing a future where biomedical discovery is accelerated through open, interoperable, and high-performing data systems. As stewards of a collaborative research ecosystem, we support the development of AI-driven tools, standardized workflows, and open-source platforms that enable scientists to work smarter, faster, and more collaboratively. Our mission is to empower the biomedical community with the infrastructure, training, and technologies needed to unlock new knowledge and drive collective progress.

Leading with Vision and Expertise

Center Director

Nathan Hotaling, PhD

Dr. Nathan Hotaling brings over 17 years of experience in biomedical research and data science to Novagen and the Polus Center for Data Research (PCDR). He has led teams of 80+ data professionals, driving the creation and expansion of Data Science, reproducible research, and secure data practices into government contracting and consulting where he and his team supported innovative research in large scale (100s of TBs and beyond) biomedical data analysis. His role in private industry, in partnership with the National Institutes of Health (NIH), was to oversee the development of the Polus Platform, an advanced open-source cloud and high-performance compute-based data analysis platform that integrates novel artificial intelligence, statistical methods, reproducible and traceable analyses, and dynamic data exploration tools into a cohesive platform. He has also built teams of clinical and microscopy image analysis engineers, real world evidence pharmacoepidemiologists, cheminformaticists, molecular modeling experts, and omics researchers who are focused on data quality and harmonization, hypothesis-driven drug discovery and clinical best practices for diseases spanning the translational spectrum from COVID-19 to cancer. This work caused him to realize that the tools, platforms, and processes set up by his teams needed to reach a wider audience and that a community needed to be created to continue to grow and support these critical components of scientific research. With that drive, he has joined Novagen and begun to build the PCDR.  

Professional and Research Background Before PCDR

Before starting PCDR, his professional journey included postdoctoral research at the National Eye Institute (NEI) and the National Institute of Standards and Technology (NIST). There, he focused on developing deep learning algorithms and pipelines to analyze cell therapy tissues and optimize nanofiber scaffolds for regenerative medicine. This work is part of the quality control pipeline for a Phase I clinical trial using autologous induced pluripotent stem cells to treat patients with macular degeneration, which is the first of its kind in the US. His earlier academic background includes a PhD in Biomedical Engineering from Georgia Tech and a Master of Science in Clinical Research from Emory University, where he studied immune responses to carbohydrates presented from biomaterial surfaces and developed statistical models to understand various systems from clinical to educational. This diverse research experience equipped him with a solid foundation in translational science, computational modeling, and data analytics, which he brings to his work at Axle.  

PCDR Mission: To accelerate biomedical research using Artificial Intelligence & other novel technologies. PCDR fosters an open-source community & supports interoperable platforms, applications, tools, training, & data while increasing the aggregate rate of scientific advancement.  

PCDR Vision: To move Biomedical research forward more quickly. We envision a future in which data, workflows, analysis, standards, & tools can be seamlessly utilized, exchanged, & scaled to empower scientists at every level to speed research, discovery, & knowledge generation. 

About Us

At PCDR, we provide a powerful ecosystem of tools and platforms that accelerate biomedical discovery through data-driven research and open collaboration. Our core capabilities include:

  • Notebooks Hub
    A coding interface designed for cloud-based creation, execution, and visualization of scripts, analyses, dashboards, and prototype GUIs.

  • Pipeline

    An interactive environment for building, executing, and monitoring computational workflows—enabling reproducible, scalable research pipelines.

  • Explore
    Dynamic applications for visual data exploration that help researchers uncover patterns, drive insights, and spark discovery across large datasets.

  • Data Catalog
    An interoperable resource that organizes and connects datasets to streamline access, promote reusability, and ensure data integrity.

Dr. Hotaling’s Selected Publications

  • Schaub, N. J., and Hotaling, N., “Assessing Efficiency in Artificial Neural Networks,” Applied Sciences, Vol. 13, No. 18, 2023, p. 10286. https://doi.org/10.3390/app131810286.

  • Moore, J., et al. “OME-Zarr: A Cloud-Optimized Bioimaging File Format with International Community Support,” Histochemistry and Cell Biology, 2023. https://doi.org/10.1007/s00418-023-02209-1.

  • Ishaq, N., Hotaling, N., and Schaub, N., “Theia: Bleed-Through Estimation With Convolutional Neural Networks,” presented at the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. https://doi.org/10.1109/CVPRW59228.2023.00447.

  • Goyal, V., Schaub, N. J., Voss, T. C., and Hotaling, N., “Unbiased Image Segmentation Assessment Toolkit for Quantitative Differentiation of State-of-the-Art Algorithms and Pipelines,” BMC Bioinformatics, Vol. 24, No. 1, 2023, p. 388. https://doi.org/10.1186/s12859-023-05486-8.

  • Florczyk, S., Hotaling, N., Simon, M., Chalfoun, J., Horenberg, A., Schaub, N., Wang, D., Szczypiński, P., DeFelice, V., Bajcsy, P., and Simon, C. “Measuring Dimensionality of Cell-Scaffold Contacts of Primary Human Bone Marrow Stromal Cells Cultured on Electrospun Fiber Scaffolds.” Journal of Biomedical Materials Research. Part A, 2022. https://doi.org/10.1002/jbm.a.37449.

  • Silva, T. D., Hotaling, N., Chew, E. Y., and Cukras, C. Feature-Based Retinal Image Registration for Longitudinal Analysis of Patients with Age-Related Macular Degeneration. In Medical Imaging 2020: Image Processing, No. 11313, 2020, pp. 113132Z.https://doi.org/10.1117/12.2549969.

  • Schaub, N. J.*, Hotaling, N.*, Manescu, P., Padi, S., Wan, Q., Sharma, R., George, A., Chalfoun, J., Simon, M., Ouladi, M., Carl G. Simon, J., Bajcsy, P., and Bharti, K. “Deep Learning Predicts Function of Live Retinal Pigment Epithelium from Quantitative Microscopy.” The Journal of Clinical Investigation, Vol. 2, No. 130, 2019, pp. 1010–1023. https://doi.org/10.1172/JCI131187. 

  • Hotaling, N. A., Khristov, V., Wan, Q., Sharma, R., Jha, B. S., Lotfi, M., Maminishkis, A., Simon, C. G., and Bharti, K. “Nanofiber Scaffold-Based Tissue-Engineered Retinal Pigment Epithelium to Treat Degenerative Eye Diseases.” Journal of Ocular Pharmacology and Therapeutics, Vol. 32, No. 5, 2016, pp. 272–285.https://doi.org/10.1089/jop.2015.0157.

  • Hotaling, N. A., Bharti, K., Kriel, H., and Simon Jr., C. G. “DiameterJ: A Validated Open Source Nanofiber Diameter Measurement Tool.” Biomaterials, Vol. 61, 2015, pp. 327–338. https://doi.org/10.1016/j.biomaterials.2015.05.015.