Martinho, A., Kroesen, M., & Chorus, C. (2024). "Moral foundations in gender violence cases decided in Portuguese courts." European Journal of Criminology, 0(0). [View PDF]

Gritzer, L., Zavras, A., Macek, M. and Alqaderi, H., 2024. "Bridging gaps: Transforming dental public health training for modern job market demands." Journal of Dental Education. [View PDF]

Dinh, Y., Alawady, A., Alhazmi, H., Altabtbaei, K., Freire, M., Alghounaim, M., Devarajan, S., Bin-Hassan, S. and Alqaderi, H., 2024. "Association between risk of obstructive sleep apnea severity and risk of severe COVID-19 symptoms: insights from salivary and serum cytokines". Frontiers in Public Health, No. 12, p.1348441. [View PDF]

Martinho, A. "Surveying Judges about artificial intelligence: profession, judicial adjudication, and legal principles". AI & Soc (2024).  [View PDF]

Haensch, A., Tronci, E. M., Moynihan, B., and Moaveni, B., 2024. "Regularized hidden markov modeling with applications to wind speed predictions in Offshore Wind," Mechanical Systems and Signal Processing, 211, 111229. [View PDF]

Borgers, C., Dragovic, N., Haensch, A., Kirshtein, A., and Orr, L., 2024. "ODEs and Mandatory Voting," CODEE Journal: Vol. 17, Article 11 [View PDF]

Georgalis, G., Nathawani, D., Knepley, M., and Patra, A., 2024. “Uncertainty Quantification of Shear-induced Paraffin Droplet Pinch-off in Hybrid Rocket Motors,” AIAA Scitech ’24. [View PDF]


Prashant, S., Babu, M., and Patra, A., 2023. “Hierarchical Regularization Networks for Sparsification Based Learning on Noisy Datasets.” Foundations of Data Science 5, No. 4, pp. 520–57. [View PDF]

Reed E., Jankowski S.A., Spinella A.J., Noonan V., Haddad R., Nomoto K., Matsui J., Bais M.V., Varelas X., Kukuruzinska M.A., and Monti S., 2023. "β-catenin/CBP activation of mTORC1 signaling promotes partial epithelialmesenchymal states in head and neck cancer", Translational Research. [View PDF]

Vora N., Polleys C. M., Sakellariou F., Georgalis G.,  Thieu HT., Genega E., Jahanseir N., Patra A., Miller E., and Georgakoudi I., 2023. "Restoration of metabolic functional metrics from label-free, two-photon cervical tissue images using multiscale deep-learning-based denoising algorithms". bioRxiv 2023.06.07.544033; doi: [View PDF]

Börgers, C., Boghosian, B., Dragovic, N., and 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,” International Journal of Uncertainty Quantification, Vol. 13, No. 5, pp. 23-40. [View PDF]

Fala, N., Georgalis, G., and Arzamani, N., 2023. “Study on Machine Learning Methods for General Aviation Flight Phase Identification,” Journal of Aerospace Information Systems, Vol. 20, No. 10, pp. 636-647. [View PDF]

Salunkhe, A., Georgalis, G., Patra, A., and 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. [View PDF]

H. I. Muendlein, W. M. Connolly, J. Cameron, D. Jetton, Z. Magri, I. Smirnova, E. Vannier, X. Li, R.E. Batorsky, and 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]

  • Karagiannis, T., Dowrey, T., Villacorta-Martin, C., Montano, M., Reed, E., Andersen, S., Perls, T., Monti, S., Murphy, G. and Sebastiani, P., 2022. "Multi-modal profiling of peripheral blood cells across the human lifespan reveals distinct immune cell signatures of aging and longevity." Submitted to Immunology. [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]