Shook, L.L., Batorsky, R.E., De Guzman, R.M. et al. "Maternal SARS-CoV-2 impacts fetal placental macrophage programs and placenta-derived microglial models of neurodevelopment". J Neuroinflammation 21, 163 (2024). [View PDF]

Batorsky R, Ceasrine AM, Shook LL, Kislal S, Bordt EA, Devlin BA, Perlis RH, Slonim DK, Bilbo SD, Edlow AG. "Hofbauer cells and fetal brain microglia share transcriptional profiles and responses to maternal diet-induced obesity". Cell Reports, Volume 43, Issue 6, 25 June 2024, 114326[View PDF]

Haensch, A., Gordon, D., Knudson, K., & Cheng, J. (2024). "A Multi-Method Data Science Pipeline for Analyzing Police Service." The American Statistician, 1–18. [View PDF]

Jiri Bonaventura, Ethan J. Rowin, Raymond H. Chan, Michael T. Chin, Veronika Puchnerova, Eva Polakova, Milan MacekJr, Pavel Votypka, Rebecca Batorsky, Gayani Perera, Benjamin Koethe, Josef Veselka, Barry J. Maron and Martin S. Maron. "Relationship Between Genotype Status and Clinical Outcome in Hypertrophic Cardiomyopathy". Journal of the American Heart Association. 2024 [View PDF]

Hend Alqedari, Khaled Altabtbaei, Josh L Espinoza, Saadoun Bin-Hasan, Mohammad Alghounaim, Abdullah Alawady, Abdullah Altabtabae, Sarah AlJamaan, Sriraman Devarajan, Tahreer AlShammari, Mohammed Ben Eid, Michele Matsuoka, Hyesun Jang, Christopher L Dupont, Marcelo Freire, "Host–microbiome associations in saliva predict COVID-19 severity", PNAS Nexus, Volume 3, Issue 4, April 2024, pgae126, [View PDF]

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]