In the Computer Science and Artificial Intelligence Lab you will have the opportunity to engage in cutting-edge applied computer science projects including applied artificial intelligence and machine learning projects utilizing real-world datasets and innovative predictive problems. Students working in the lab will gain an end-to-end understanding of problem formulation and understanding, data manipulation and integration, model implementation, accelerated computing, and model evaluation.
Students have the opportunity to gain expertise and project experience with cutting-edge machine learning technologies such as deep learning and tree ensemble-based learning. Where innovative contributions to current knowledge are made, students will also have the possibility of contributing to peer-reviewed papers reporting on those contributions.
Steele, R., Hillsgrove, T., Khoshavi, N., Jaimes, L. (2021). A Survey of Cyber-Physical System Implementations of Real-time Personalized Interventions. Journal of Ambient Intelligence and Humanized Computing, Springer. https://doi.org/10.1007/s12652-021-03263-0
Galen, C., Steele, R. (2021). Empirical Measurement of Performance Maintenance of Gradient Boosted Decision Tree Models for Malware Detection. The 3rd IEEE International Conference on Artificial Intelligence in Information and Communications. Jeju Island, Korea, April 14-15.
Brettle, C., Steele, R. (2021). Advance Prediction of Maryland Elective Admission Fatalities Using Machine Learning. The 7th IEEE International Conference on Information Management, Imperial College London, UK. March 28-29.
Young, Z., Steele, R. (2021). Performance Maintenance of Machine Learning-based Emergency Patient Mortality Predictive Models. The 4th International Conference of Computer and Informatics Engineering (IC2IE), IEEE, Sept 14-15.
Galen, C., & Steele, R. (2020). Evaluating Performance Maintenance and Deterioration Over Time of Machine Learning-based Malware Detection Models on the EMBER PE Dataset. Proceedings of the 4th International Workshop on Data Science Engineering, IEEE.
Galen, C., & Steele, R. (2020, October). Performance Maintenance Over Time of Random Forest-based Malware Detection Models. In Proceedings of 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 0536-0541). IEEE.
Steele, R., Jaimes, L. (2019). Crowdsensing Sub-populations in a Region. Journal of Ambient Intelligence and Humanized Computing, Springer. doi: 10.1007/s12652-018-0799-y.
Jaimes, L, Chakeri, A., Steele, R., (2019). Localized Cooperation for Crowdsensing in a Fog Computing-enabled Internet of Things. Journal of Ambient Intelligence and Humanized Computing, Springer. doi: 10.1007/s12652-018-0818-z
Gentimis, T., Alnaser, AJ., Durante, A., Cook, K., Steele, R. (2019). Predicting Hospital Length of Say Using Neural Networks. International Journal of Big Data Intelligence doi: 10.1504/IJBDI.2019.10019022
Hillsgrove, T, Steele, R. (2019). Machine Learning-based Wait Time Prediction for Autonomous Mobility-on-Demand Systems. IEEE SoutheastCon, Huntsville, AL, April, 2019.
Steele, R. , Hillsgrove, T. (2019). Predicting All-condition, In-hospital Mortality of Elective Patients at Time of Scheduling. IEEE SoutheastCon, Huntsville, AL, April, 2019.
Steele, R. , Hillsgrove, T. (2019). Data Mined Models for Predicting In-hospital Mortality of Emergency Admissions at Time of Admission. IEEE SoutheastCon, Huntsville, AL, April, 2019.
Gentimis, T., Alnaser, AJ., Durante, A., Cook, K., Steele, R. (2017). Predicting Hospital Length of Stay Using Neural Networks on MIMIC III Data. IEEE International Conference on Big Data Intelligence and Computing.
Steele, R., Lo, A., Secombe, C., Wong, YK. (2009). Elderly Persons' Perception and Acceptance of Using Wireless Sensor Networks to Assist Healthcare. International Journal of Medical Informatics. 78(12): 788-801.
Kohlhoff, C., Steele, R. (2003). Evaluating SOAP for High Performance Business Applications: Real-Time Trading Systems, Proceedings of WWW2003, Budapest, Hungary, May 20-24.