| Technical language processing: Unlocking maintenance knowledge MP Brundage, T Sexton, M Hodkiewicz, A Dima, S Lukens Manufacturing Letters 27, 42-46, 2021 | 114 | 2021 |
| Adapting natural language processing for technical text A Dima, S Lukens, M Hodkiewicz, T Sexton, MP Brundage Applied AI Letters 2 (3), e33, 2021 | 66 | 2021 |
| The inflammatory response to influenza A virus (H1N1): an experimental and mathematical study I Price, ED Mochan-Keef, D Swigon, GB Ermentrout, S Lukens, ... Journal of theoretical biology 374, 83-93, 2015 | 59 | 2015 |
| Using Lagrangian coherent structures to analyze fluid mixing by cilia S Lukens, X Yang, L Fauci Chaos: An Interdisciplinary Journal of Nonlinear Science 20 (1), 2010 | 57 | 2010 |
| A large-scale immuno-epidemiological simulation of influenza A epidemics S Lukens, J DePasse, R Rosenfeld, E Ghedin, E Mochan, ST Brown, ... BMC public health 14, 1-15, 2014 | 54 | 2014 |
| A mathematical model of intrahost pneumococcal pneumonia infection dynamics in murine strains E Mochan, D Swigon, GB Ermentrout, S Lukens, G Clermont Journal of theoretical biology 353, 44-54, 2014 | 45 | 2014 |
| Best practices framework for improving maintenance data quality to enable asset performance analytics S Lukens, M Naik, K Saetia, X Hu Annual conference of the phm society 11 (1), 2019 | 37 | 2019 |
| Rethinking maintenance terminology for an industry 4.0 future M Hodkiewicz, S Lukens, MP Brundage, T Sexton International Journal of Prognostics and Health Management 12 (1), 2021 | 20 | 2021 |
| Lagrangian transport properties of pulmonary interfacial flows BJ Smith, S Lukens, E Yamaguchi, DP Gaver III Journal of fluid mechanics 705, 234-257, 2012 | 11 | 2012 |
| Data-driven approach to equipment taxonomy classification K Saetia, S Lukens, E Pijcke, X Hu Annual Conference of the PHM Society 11 (1), 2019 | 9 | 2019 |
| Data-driven application of PHM to asset strategies S Lukens, M Markham Proceedings of the annual conference of the prognostics and health …, 2018 | 9 | 2018 |
| Evaluating the Performance of ChatGPT in the Automation of Maintenance Recommendations for Prognostics and Health Management S Lukens, A Ali Annual Conference of the PHM Society 15 (1), 2023 | 7 | 2023 |
| A data quality scorecard for assessing the suitability of asset condition data for prognostics modeling S Lukens, D Rousis, T Baer, M Lujan, M Smith Annual Conference of the PHM Society 14 (1), 2022 | 7 | 2022 |
| Evaluating word representations in a technical language processing pipeline AV Nandyala, S Lukens, S Rathod, P Agarwal Phm society european conference 6 (1), 17-17, 2021 | 7 | 2021 |
| The role of transactional data in prognostics and health management work processes S Lukens, M Naik, DS Hu, Xiaohui, Doan, S Abado Proceedings of the Annual Conference of the Prognostics and Health …, 2017 | 6 | 2017 |
| Adapting natural language processing for technical text. Applied AI Letters 2 (2): e33 A Dima, S Lukens, M Hodkiewicz, T Sexton, MP Brundage | 5 | 2021 |
| Sensitivity of human immune response to influenza a virus infection and its dependence on virus and host phenotypes S Lukens, G Clermont, D Swigon Journal of Critical Care 1 (28), e7-e8, 2013 | 4 | 2013 |
| Data-driven approach to estimate maintenance life cycle cost of assets S Lukens, M Markham, M Naik, M Laplante Model-Based Enterprise Summit(MBE2019), 2019 | 3 | 2019 |
| Apparatus and method for failure identification J Cline, C Defalco, S Lukens US Patent App. 15/496,541, 2018 | 3 | 2018 |
| Overcoming data quality constraints to improve the accuracy in evaluation of SMRP metrics M Naik, S Lukens Annual conference of the Society of Maintenance and Reliability Professionals, 2019 | 1 | 2019 |
David SwigonProfessor of Mathematics, University of PittsburghDirección de correo verificada de pitt.edu