Keivan Faghih Niresi
keivan.faghihniresi@epfl.ch https://keiv4n.github.io/
Birth date: 30.07.1997
Fields of expertise
Computational sensing/imaging, Inverse problems, Signal processing, Graph neural networks, Domain adaptation.
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).
Education
Master of Science
Communications Engineering
National Tsing Hua University
2020-2022
Bachelor of Science
Electrical Engineering
University of Guilan
2015-2019
Publications
Selected publications
| Keivan Faghih Niresi, Hugo Bissig, Henri Baumann, Olga Fink IEEE Internet of Things Journal |
Physics-Enhanced Graph Neural Networks for Soft Sensing in Industrial Internet of Things |
| 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 |
Teaching & PhD
CIVIL-332
Data Science for infrastructure condition monitoringCourse Book
CIVIL-426
Machine learning for predictive maintenance applicationsCourse Book