Keivan Faghih Niresi
Fields of expertise
Inverse problems, Graph representation learning, Deep learning, Signal processing, Sensor networks, and Hyperspectral imaging
Biography
Keivan is a PhD student at École Polytechnique Fédérale de Lausanne (EPFL). He joined the Intelligent Maintenance and Operations Systems (IMOS) Lab under the supervision of Prof. Olga Fink in February 2023.Prior to joining EPFL, he obtained his master's degree from the Institute of Communications Engineering, College of Electrical Engineering and Computer Science, National Tsing Hua University (NTHU) where he conducted research in convex and non-convex optimization, statistical signal processing, deep learning, and hyperspectral imaging under the supervision of Prof. Chong-Yung Chi. He also had the opportunity to work as a machine learning engineer intern at PranaQ, where he focused on developing signal processing and feature extraction algorithms for biomedical signals such as photoplethysmogram (PPG) and electrocardiogram (ECG).
Publications
Selected publications
| Keivan Faghih Niresi, Chong-Yung Chi IEEE Geoscience and Remote Sensing Letters |
Robust Hyperspectral Inpainting via Low-Rank Regularized Untrained Convolutional Neural Network |
| Keivan Faghih Niresi, Chong-Yung Chi IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Unsupervised Hyperspectral Denoising Based on Deep Image Prior and Least Favorable Distribution |