Skip to main content

Faculty

Department of Electrical and Computer Engineering

 

Biography

Kalantari received her doctoral degree in computer engineering from UCF in 2021, focusing on enhancing learning in deep neural networks through sparse representation in transform domains. She received her master’s degree from Simon Fraser University in Canada and was a visiting graduate student at the University of California, Berkeley, where she developed an MRI brain image segmentation algorithm. Following her doctoral studies at UCF, she received a post-doctoral fellowship from the American Society for Engineering Education, sponsored by the National Science Foundation.

She has been a lecturer at UCF since 2024. Her teaching interests include signal processing and analysis, machine learning and digital systems. Her research interests include the application of artificial intelligence, machine learning and signal processing in medical imaging, in particular for magnetic particle imaging.

Publications

  • M. Kalantari Khandani, Yaser Fallah, Azadeh Vosoughi, “Magnetic Particle Image Super Resolution Using Transfer Learning with Diverse Datasets and Sparse Transforms”, to appear in, IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS), 2024
  • M. K. Khandani, A. Vosoughi, “A Review of Enabling Technologies for Magnetic Particle Imaging,” 2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS), Fukuoka, Japan, 2022, pp. 1-5, 10.1109/MWSCAS54063.2022.9859431
  • M. K. Khandani, Mikhael W.B. (2022). Enhancing Convolutional Neural Network Performance Using Domain Transforms in Constrained Networks. Circuits, Systems, and Signal Processing. 41:9. (5160-5182). Online publication date: 1-Sep-2022. https://doi.org/10.1007/s00034-022-02026-2
  • M. K., Khandani, Wasfy B. Mikhael. “Efficient Size Reduction of Convolutional Neural Network using Domain Transforms”, Circuits, Systems, and Signal Processing, Springer (2022), https://doi.org/10.1007/s00034-020-01610-8
  • M., K. Khandani, Mikhael, W.B. Effect of Sparse Representation of Time Series Data on Learning Rate of Time-Delay Neural Networks. Circuits Syst Signal Process 40, 3007–3032 (2021) https://doi.org/10.1007/s00034-020-01610-8
  • M. K. Khandani, W. B. Mikhael. “Training Strategies for Convolutional Neural Networks with Transformed Input”, IEEE MWSCAS 2021, 10.1109/MWSCAS47672.2021.9531913
  • M. Kalantari Khandani, W. B. Mikhael. “A Study on Network Size Reduction Using Sparse Input Representation in Time Delay Neural Networks”. IEEE MWSCAS 2020 https://doi.org/10.1109/MWSCAS48704.2020.9184438
  • M. Kalantari Khandani, W. B. Mikhael. “Efficient Time Series Forecasting Using Time Delay Neural Networks with Domain Pre-Transforms”. IEEE MWSCAS 2019 https://doi.org/10.1109/MWSCAS.2019.8884826