Börgers, C., Boghosian, B., Dragovic, N., Haensch, A., 2023. "A blue sky bifurcation in the dynamics of political candidates" . To appear in The American Mathematical Monthly. [View PDF]

Georgalis, G., Retfalvi, K., DeJardin, P.E., and Patra, A., 2023. “Combined Input Data and Deep Learning Model Uncertainty: An Application to the Measurement of Solid Fuel Regression Rate”. Submitted to International Journal of Uncertainty Quantification [Accepted; In press]. [View PDF]

Fala, N., Georgalis, G., Arzamani, N., 2023. “A Study on Machine Learning Methods for General Aviation Flight Phase Identification”. Submitted to Journal of Aerospace Information Systems [Under review].

Salunkhe, A., Georgalis, G., Patra, A., Chandola, V., 2023. “An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds,” AIAA Scitech ’23. [View PDF]

Georgalis, G., and Fala, N., 2023. “Automated Identification of Phase of Flight via Probabilistic Clustering for General Aviation Operations,” AIAA Aviation ’23. [Accepted; Will appear]

H. I. Muendlein, W. M. Connolly, J. Cameron, D. Jetton, Z. Magri, I. Smirnova, E. Vannier, X. Li, R.E. Batorsky, A. Poltorak., 2023. “Neutrophils and macrophages drive TNF-induced lethality via TRIF/CD14-mediated responses,” Science Immunology, Vol. 7, No. 78. [View PDF]

Haensch, A., Dragovic, N., Börgers, C. and Boghosian, B., 2022. “A geospatial bounded confidence model including mega-influencers with an application to Covid-19 vaccine hesitancy.” Journal of Artificial Societies and Social Simulation,  Vol. 26, No. 1. [View PDF]

  • Haensch, A. and Knudson, K., 2022. “Python for Global Applications: teaching scientific Python in context to law and diplomacy students,” Proc. Of the 21st Python in Science Conference (Scipy 2022). [View PDF]

    Hanscom, T., Woodward, N., Batorsky, R., Brown, A.J., Roberts, S.A. and McVey, M., 2022. “Characterization of sequence contexts that favor alternative end joining at Cas9-induced double-strand breaks,” Nucleic acids research, 50(13), pp.7465-7478. [View PDF]

    Surina III, G., Georgalis, G., Aphale, S.S., Patra, A. and DesJardin, P.E., 2022. “Measurement of hybrid rocket solid fuel regression rate for a slab burner using deep learning.” Acta Astronautica, 190, pp.160-175. [View PDF]

    Haensch, A., Gordon, D., Knudson, K., & Cheng, J., 2022. “A Multi-method Data Science Pipeline for Analyzing Police Service in the Presence of Misconduct.” SocArXiv. November 5. [View PDF]

    Fiore, N.J., Ganat, Y.M., Devkota, K., Batorsky, R., Lei, M., Lee, K., Cowen, L.J., Croft, G., Noggle, S.A., Nieland, T.J. and Kaplan, D.L., 2022. “Bioengineered models of Parkinson’s disease using patient-derived dopaminergic neurons exhibit distinct biological profiles in a 3D microenvironment,” Cellular and Molecular Life Sciences, 79(2), pp.1-20. [View PDF]

    Wojnowicz, M. T., Aeron, S., Miller, E. L., & Hughes, M., 2022.  “Easy Variational Inference for Categorical Models via an Independent Binary Approximation,” In International Conference on Machine Learning (pp. 23857-23896). [View PDF]

  • Ceasrine, A.M., Batorsky, R., Shook, L.L., Kislal, S., Bordt, E.A., Devlin, B.A., Perlis, R.H., Slonim, D.K., Bilbo, S.D. and Edlow, A.G., 2021. Single cell profiling of Hofbauer cells and fetal brain microglia reveals shared programs and functions. bioRxiv 2021.12.03.471177 [View PDF]

    Reed, E.R. and Monti, S., 2021. Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data. Nucleic acids research, 49(17), pp.e98-e98. [View PDF]

    Kim, S., Reed, E., Monti, S. and Schlezinger, J.J., 2021. “A data-driven transcriptional taxonomy of adipogenic chemicals to identify white and brite adipogens,” Environmental health perspectives, 129(7), p.077006. [View PDF]

    Georgalis, G. and Marais, K., 2021. “Predicting failure events from crowd-derived inputs: schedule slips and missed requirements”, In INCOSE International Symposium, Vol. 31, No. 1. [View PDF]

    Reed, E., Moses, E., Xiao, X., Liu, G., Campbell, J., Perdomo, C. and Monti, S., 2019. “Assessment of a highly multiplexed RNA sequencing platform and comparison to existing high-throughput gene expression profiling techniques,” Frontiers in genetics, 10, p.150. [View PDF]