Associate Professor, Computer Information Systems
College
College of Business Administration
Department
Management Information Systems
Phone
Location
Demergasso-Bava Hall DBH236
Ph D in Industrial Engineering, University of Central Florida, 2016.
MS in Industrial and Systems Engineering, University of Florida, 2014.
Diploma in Production Engineering and Management, Technical University of Crete, 2010
Intro to Python Programming, Advanced Python Programming, Information Technology for Mgt; Computer Information Systems, Business Analytics, Application Development Project
Data Mining (Massive Data Sets, Feature Selection and Dimensionality Reduction Techniques), Machine Learning (Supervised Learning, Unsupervised Learning, Approximation Methods), Optimization Methods Applied to Business Decisions
- Pantelidakis, M., Panagopoulos, A. A., Mykoniatis, K., Ashkan, S., Eravi, R. C., Pamula, V., Verduzco III, E. C., Babich, O., Panagopoulos, O., Chalkiadakis, G. (2022). Identifying sunlit leaves using Convolutional Neural Networks: An expert system for measuring the crop water stress index of pistachio trees. Expert Systems with Applications, 209.
- Panagopoulos, A. A., Christianos, F., Katsigiannis, M., Mykoniatis, K., Pritoni, M., Panagopoulos, O., Peffer, T., Chalkiadakis, G., Culler, D. E., Jennings, N. R., Lipman, T. (2022). A low-complexity non-intrusive approach to predict the energy demand of buildings over short- term horizons. Advances in Building Energy Research, 16(2), 202-213.
- Panagopoulos, A. A., Christianos, F., Katsigiannis, M., Mykoniatis, K., Chalkiadakis, G., Pritoni, M., Peffer, T., Panagopoulos, O., Rigas, E., Culler, D., Jennings, N., Lipman, T. (2022). iPlugie: Intelligent electric vehicle charging in buildings with grid-connected intermittent energy resources. Simulation Modelling Practice and Theory, 115.
- Panagopoulos, A. A., Christianos, F., Katsigiannis, M., Mykoniatis, K., Pritoni, M., Panagopoulos, O., Peffer, T., Chalkiadakis, G., Culler, D. E., Jennings, N. R., Lipman, T. (2020). A low-complexity non-intrusive approach to predict the energy demand of buildings over short- term horizons. Advances in Building Energy Research.
- Mytidis, A., Panagopoulos, A. A., Panagopoulos, O. P., Miller, A., Whiting, B. (2019). Sensitivity study using machine learning algorithms on simulated r-mode gravitational wave signals from newborn neutron stars. PHYSICAL REVIEW D, 99(2).
- Kirts, S., Nam, B. H., Panagopoulos, O. P., Xanthopoulos, P. (2019) Settlement Prediction Using Support Vector Machine (SVM)-Based Compressibility Models: A Case Study. International Journal of Civil Engineering, 17, 1547-1557.
- Panagopoulos, O., Xanthopoulos, P., Razzaghi, T., Seref, O. (2018). Relaxed Support Vector Regression. Annals of Operations Research, 276, 191-210.
- Kirts, S., Panagopoulos, O., Xanthopoulos, P., Nam, B. H. (2018). Soil-Compressibility Prediction Models Using Machine Learning. Journal of Computing in Civil Engineering, 32(1).
- Panagopoulos, O., Pappu, V., Xanthopoulos, P., Pardalos, P. M. (2016). Constrained subspace classifier for high dimensional datasets. Omega, 59(A), 40-46.
- Panagopoulos, O., Xanthopoulos, P., Bakamitsos, Y., Freudmann, E. (2015). Hashtag hijacking: what it is, why it happens and how to avoid it. Journal of Digital & Social Media Marketing, 3(4).
- Pappu, V., Panagopoulos, O., Xanthopoulos, P., Pardalos, P. M. (2015). Sparse Proximal Support Vector Machines for Feature Selection in High Dimensional Datasets. Expert Systems with Applications, 42(23), 9183-9191.